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409 Program Highlights »
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Invited Talk (Breiman Lecture)
Thu Dec 6th 08:30 -- 09:20 AM @ Rooms 220 CDE
Making Algorithms Trustworthy: What Can Statistical Science Contribute to Transparency, Explanation and Validation?
David Spiegelhalter
Break
Thu Dec 6th 09:20 -- 09:45 AM @
Coffee Break
Spotlight
Thu Dec 6th 09:45 -- 09:50 AM @ Room 220 CD
Learning with SGD and Random Features
Luigi Carratino · Alessandro Rudi · Lorenzo Rosasco
Spotlight
Thu Dec 6th 09:45 -- 09:50 AM @ Room 220 E
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten · Jouni Helske · Matti Vihola
Spotlight
Thu Dec 6th 09:45 -- 09:50 AM @ Room 517 CD
Boolean Decision Rules via Column Generation
Sanjeeb Dash · Oktay Gunluk · Dennis Wei
Spotlight
Thu Dec 6th 09:50 -- 09:55 AM @ Room 220 CD
KONG: Kernels for ordered-neighborhood graphs
Moez Draief · Konstantin Kutzkov · Kevin Scaman · Milan Vojnovic
Spotlight
Thu Dec 6th 09:50 -- 09:55 AM @ Room 220 E
Boosting Black Box Variational Inference
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Raetsch
Spotlight
Thu Dec 6th 09:50 -- 09:55 AM @ Room 517 CD
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
Kaito Fujii · Tasuku Soma
Spotlight
Thu Dec 6th 09:55 -- 10:00 AM @ Room 220 CD
Quadrature-based features for kernel approximation
Marina Munkhoeva · Yermek Kapushev · Evgeny Burnaev · Ivan Oseledets
Spotlight
Thu Dec 6th 09:55 -- 10:00 AM @ Room 220 E
Discretely Relaxing Continuous Variables for tractable Variational Inference
Trefor Evans · Prasanth Nair
Spotlight
Thu Dec 6th 09:55 -- 10:00 AM @ Room 517 CD
Distributed $k$-Clustering for Data with Heavy Noise
Shi Li · Xiangyu Guo
Spotlight
Thu Dec 6th 10:00 -- 10:05 AM @ Room 220 CD
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello · Lorenzo Rosasco
Spotlight
Thu Dec 6th 10:00 -- 10:05 AM @ Room 220 E
Implicit Reparameterization Gradients
Mikhail Figurnov · Shakir Mohamed · Andriy Mnih
Spotlight
Thu Dec 6th 10:00 -- 10:05 AM @ Room 517 CD
Do Less, Get More: Streaming Submodular Maximization with Subsampling
Moran Feldman · Amin Karbasi · Ehsan Kazemi
Oral
Thu Dec 6th 10:05 -- 10:20 AM @ Room 220 CD
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
Amir Dezfouli · Richard Morris · Fabio Ramos · Peter Dayan · Bernard Balleine
Oral
Thu Dec 6th 10:05 -- 10:20 AM @ Room 220 E
Variational Inference with Tail-adaptive f-Divergence
Dilin Wang · Hao Liu · Qiang Liu
Oral
Thu Dec 6th 10:05 -- 10:20 AM @ Room 517 CD
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh · Tim Roughgarden · Joshua Wang
Spotlight
Thu Dec 6th 10:20 -- 10:25 AM @ Room 220 CD
Why Is My Classifier Discriminatory?
Irene Chen · Fredrik Johansson · David Sontag
Spotlight
Thu Dec 6th 10:20 -- 10:25 AM @ Room 220 E
Mirrored Langevin Dynamics
Ya-Ping Hsieh · Ali Kavis · Paul Rolland · Volkan Cevher
Spotlight
Thu Dec 6th 10:20 -- 10:25 AM @ Room 517 CD
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao · Purnamrita Sarkar · Deepayan Chakrabarti
Spotlight
Thu Dec 6th 10:25 -- 10:30 AM @ Room 220 CD
Human-in-the-Loop Interpretability Prior
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez
Spotlight
Thu Dec 6th 10:25 -- 10:30 AM @ Room 220 E
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu
Spotlight
Thu Dec 6th 10:25 -- 10:30 AM @ Room 517 CD
Removing the Feature Correlation Effect of Multiplicative Noise
Zijun Zhang · Yining Zhang · Zongpeng Li
Spotlight
Thu Dec 6th 10:30 -- 10:35 AM @ Room 220 CD
Link Prediction Based on Graph Neural Networks
Muhan Zhang · Yixin Chen
Spotlight
Thu Dec 6th 10:30 -- 10:35 AM @ Room 220 E
Identification and Estimation of Causal Effects from Dependent Data
Eli Sherman · Ilya Shpitser
Spotlight
Thu Dec 6th 10:30 -- 10:35 AM @ Room 517 CD
Connectionist Temporal Classification with Maximum Entropy Regularization
Hu Liu · Sheng Jin · Changshui Zhang
Spotlight
Thu Dec 6th 10:35 -- 10:40 AM @ Room 220 CD
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver · Augustus Odena · Colin A Raffel · Ekin Dogus Cubuk · Ian Goodfellow
Spotlight
Thu Dec 6th 10:35 -- 10:40 AM @ Room 220 E
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh
Spotlight
Thu Dec 6th 10:35 -- 10:40 AM @ Room 517 CD
Entropy and mutual information in models of deep neural networks
Marylou Gabrié · Andre Manoel · Clément Luneau · jean barbier · Nicolas Macris · Florent Krzakala · Lenka Zdeborová
Spotlight
Thu Dec 6th 10:40 -- 10:45 AM @ Room 220 CD
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merrienboer · Olivier Breuleux · Arnaud Bergeron · Pascal Lamblin
Spotlight
Thu Dec 6th 10:40 -- 10:45 AM @ Room 220 E
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus · Aahlad Manas Puli · Uri Shalit
Spotlight
Thu Dec 6th 10:40 -- 10:45 AM @ Room 517 CD
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Benjamin Aubin · Antoine Maillard · jean barbier · Florent Krzakala · Nicolas Macris · Lenka Zdeborová
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #1
Experimental Design for Cost-Aware Learning of Causal Graphs
Erik Lindgren · Murat Kocaoglu · Alexandros Dimakis · Sriram Vishwanath
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #2
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus · Aahlad Manas Puli · Uri Shalit
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #3
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane · Thijs van Ommen · Tom Claassen · Stephan Bongers · Philip Versteeg · Joris M Mooij
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #4
Structural Causal Bandits: Where to Intervene?
Sanghack Lee · Elias Bareinboim
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #5
Uplift Modeling from Separate Labels
Ikko Yamane · Florian Yger · Jamal Atif · Masashi Sugiyama
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #6
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Nathan Kallus · Xiaojie Mao · Madeleine Udell
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #7
Fast Estimation of Causal Interactions using Wold Processes
Flavio Figueiredo · Guilherme Resende Borges · Pedro O.S. Vaz de Melo · Renato Assunção
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #8
Learning and Testing Causal Models with Interventions
Jayadev Acharya · Arnab Bhattacharyya · Constantinos Daskalakis · Saravanan Kandasamy
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #9
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #10
Multi-domain Causal Structure Learning in Linear Systems
AmirEmad Ghassami · Negar Kiyavash · Biwei Huang · Kun Zhang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #11
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
Shoubo Hu · Zhitang Chen · Vahid Partovi Nia · Laiwan CHAN · Yanhui Geng
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #12
Direct Estimation of Differences in Causal Graphs
Yuhao Wang · Chandler Squires · Anastasiya Belyaeva · Caroline Uhler
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #13
Identification and Estimation of Causal Effects from Dependent Data
Eli Sherman · Ilya Shpitser
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #14
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
Michelle Yuan · Benjamin Van Durme · Jordan Ying
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #15
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing
Abram L Friesen · Pedro Domingos
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #16
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman · Matthew Johnson · Dustin Tran
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #17
Distributionally Robust Graphical Models
Rizal Fathony · Ashkan Rezaei · Mohammad Ali Bashiri · Xinhua Zhang · Brian Ziebart
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #18
Flexible and accurate inference and learning for deep generative models
Eszter Vértes · Maneesh Sahani
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #19
Provable Gaussian Embedding with One Observation
Ming Yu · Zhuoran Yang · Tuo Zhao · Mladen Kolar · Princeton Zhaoran Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #20
Learning and Inference in Hilbert Space with Quantum Graphical Models
Siddarth Srinivasan · Carlton Downey · Byron Boots
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #21
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
Tong Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #22
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
Quan Zhang · Mingyuan Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #23
Theoretical guarantees for EM under misspecified Gaussian mixture models
Raaz Dwivedi · nhật Hồ · Koulik Khamaru · Martin Wainwright · Michael Jordan
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #24
Nonparametric learning from Bayesian models with randomized objective functions
Simon Lyddon · Stephen Walker · Chris C Holmes
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #25
Rectangular Bounding Process
Xuhui Fan · Bin Li · Scott SIsson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #26
A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai · Michael Mitzenmacher · Ryan Adams
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #27
Communication Efficient Parallel Algorithms for Optimization on Manifolds
Bayan Saparbayeva · Michael Zhang · Lizhen Lin
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #28
Lifted Weighted Mini-Bucket
Nicholas Gallo · Alexander Ihler
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #29
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner · Heinz Koeppl
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #30
Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb · Adam Golinski · Rob Zinkov · Siddharth Narayanaswamy · Tom Rainforth · Yee Whye Teh · Frank Wood
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #31
A Stein variational Newton method
Gianluca Detommaso · Tiangang Cui · Youssef Marzouk · Alessio Spantini · Robert Scheichl
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #32
Reparameterization Gradient for Non-differentiable Models
Wonyeol Lee · Hangyeol Yu · Hongseok Yang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #33
Implicit Reparameterization Gradients
Mikhail Figurnov · Shakir Mohamed · Andriy Mnih
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #34
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin · Frederik Kunstner · Didrik Nielsen · Mark Schmidt · Mohammad Emtiyaz Khan
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #35
Wasserstein Variational Inference
Luca Ambrogioni · Umut Güçlü · Yağmur Güçlütürk · Max Hinne · Marcel A. J. van Gerven · Eric Maris
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #36
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha · Young-Jin Park · Hyeok-Joo Chae · Soon-Seo Park · Han-Lim Choi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #37
Variational Inference with Tail-adaptive f-Divergence
Dilin Wang · Hao Liu · Qiang Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #38
Boosting Black Box Variational Inference
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Raetsch
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #39
Discretely Relaxing Continuous Variables for tractable Variational Inference
Trefor Evans · Prasanth Nair
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #40
Using Large Ensembles of Control Variates for Variational Inference
Tomas Geffner · Justin Domke
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #41
The promises and pitfalls of Stochastic Gradient Langevin Dynamics
Nicolas Brosse · Alain Durmus · Eric Moulines
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #42
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker · Paul Fearnhead · Emily Fox · Christopher Nemeth
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #43
Mirrored Langevin Dynamics
Ya-Ping Hsieh · Ali Kavis · Paul Rolland · Volkan Cevher
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #44
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo · Jianhong Wang · Yaodong Yang · Jun WANG · Zhanxing Zhu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #45
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi · Nisheeth Vishnoi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #46
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #47
Meta-Learning MCMC Proposals
Tongzhou Wang · YI WU · Dave Moore · Stuart Russell
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #48
Posterior Concentration for Sparse Deep Learning
Veronika Rockova · nicholas polson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #49
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Tom Michoel
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #50
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang · Guosheng Yin · Francesca Dominici
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #51
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten · Jouni Helske · Matti Vihola
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #52
Implicit Probabilistic Integrators for ODEs
Onur Teymur · Han Cheng Lie · Tim Sullivan · Ben Calderhead
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #53
A Bayes-Sard Cubature Method
Toni Karvonen · Chris J Oates · Simo Sarkka
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #54
Deep State Space Models for Time Series Forecasting
Syama Sundar Rangapuram · Matthias W Seeger · Jan Gasthaus · Lorenzo Stella · Yuyang Wang · Tim Januschowski
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #55
BRUNO: A Deep Recurrent Model for Exchangeable Data
Iryna Korshunova · Jonas Degrave · Ferenc Huszar · Yarin Gal · Arthur Gretton · Joni Dambre
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #56
Scaling Gaussian Process Regression with Derivatives
David Eriksson · Kun Dong · Eric Lee · David Bindel · Andrew Wilson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #57
Algebraic tests of general Gaussian latent tree models
Dennis Leung · Mathias Drton
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #58
Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein · Daniel Sheldon
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #59
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo · TIAN TIAN · Jiaxin Shi · Jun Zhu · Bo Zhang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #60
Deep Poisson gamma dynamical systems
Dandan Guo · Bo Chen · Hao Zhang · Mingyuan Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #61
Deep State Space Models for Unconditional Word Generation
Florian Schmidt · Thomas Hofmann
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #62
Modular Networks: Learning to Decompose Neural Computation
Louis Kirsch · Julius Kunze · David Barber
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #63
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale · Adrian Dalca · Luca Saglietti · Jennifer Listgarten · Nicolo Fusi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #64
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng · Nicolò Colombo · Ricardo Silva
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #65
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Marton Havasi · José Miguel Hernández-Lobato · Juan José Murillo-Fuentes
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #66
Variational Bayesian Monte Carlo
Luigi Acerbi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #67
Bayesian Alignments of Warped Multi-Output Gaussian Processes
Markus Kaiser · Clemens Otte · Thomas Runkler · Carl Henrik Ek
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #68
Automating Bayesian optimization with Bayesian optimization
Gustavo Malkomes · Roman Garnett
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #69
Infinite-Horizon Gaussian Processes
Arno Solin · James Hensman · Richard E Turner
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #70
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
David Reeb · Andreas Doerr · Sebastian Gerwinn · Barbara Rakitsch
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #71
Algorithmic Linearly Constrained Gaussian Processes
Markus Lange-Hegermann
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #72
Efficient Projection onto the Perfect Phylogeny Model
Bei Jia · Surjyendu Ray · Sam Safavi · José Bento
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #73
Distributed $k$-Clustering for Data with Heavy Noise
Shi Li · Xiangyu Guo
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #74
Communication Compression for Decentralized Training
Hanlin Tang · Shaoduo Gan · Ce Zhang · Tong Zhang · Ji Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #75
Do Less, Get More: Streaming Submodular Maximization with Subsampling
Moran Feldman · Amin Karbasi · Ehsan Kazemi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #76
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh · Tim Roughgarden · Joshua Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #77
Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga · Stefanie Jegelka · Andreas Krause
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #78
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
Kaito Fujii · Tasuku Soma
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #79
Boolean Decision Rules via Column Generation
Sanjeeb Dash · Oktay Gunluk · Dennis Wei
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #80
Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs
Gennaro Auricchio · Federico Bassetti · Stefano Gualandi · Marco Veneroni
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #81
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization
Mingrui Liu · Zhe Li · Xiaoyu Wang · Jinfeng Yi · Tianbao Yang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #82
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #83
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen · Eirikur Agustsson · Mario Lucic
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #84
Visual Object Networks: Image Generation with Disentangled 3D Representations
Jun-Yan Zhu · Zhoutong Zhang · Chengkai Zhang · Jiajun Wu · Antonio Torralba · Josh Tenenbaum · Bill Freeman
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #85
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
Yunzhe Tao · Qi Sun · Qiang Du · Wei Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #86
Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
Nitin Bansal · Xiaohan Chen · Zhangyang Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #87
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang · Mingkui Tan · Bohan Zhuang · Jing Liu · Yong Guo · Qingyao Wu · Junzhou Huang · Jinhui Zhu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #88
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn · Kelvin Xu · Sergey Levine
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #89
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
Aditya Kusupati · Manish Singh · Kush Bhatia · Ashish Kumar · Prateek Jain · Manik Varma
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #90
Understanding Batch Normalization
Nils Bjorck · Carla P Gomes · Bart Selman · Kilian Weinberger
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #91
How Many Samples are Needed to Estimate a Convolutional Neural Network?
Simon Du · Yining Wang · Xiyu Zhai · Sivaraman Balakrishnan · Ruslan Salakhutdinov · Aarti Singh
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #92
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
Zhihao Zheng · Pengyu Hong
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #93
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
Zhuwen Li · Qifeng Chen · Vladlen Koltun
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #94
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merrienboer · Olivier Breuleux · Arnaud Bergeron · Pascal Lamblin
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #95
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver · Augustus Odena · Colin A Raffel · Ekin Dogus Cubuk · Ian Goodfellow
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #96
Toddler-Inspired Visual Object Learning
Sven Bambach · David Crandall · Linda Smith · Chen Yu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #97
Generalisation in humans and deep neural networks
Robert Geirhos · Carlos R. M. Temme · Jonas Rauber · Heiko H. Schütt · Matthias Bethge · Felix A. Wichmann
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #98
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov · Adam Santoro · Blake Richards · Luke Marris · Geoffrey E Hinton · Timothy Lillicrap
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #99
Incorporating Context into Language Encoding Models for fMRI
Shailee Jain · Alexander Huth
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #100
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task
Dalin Guo · Angela J Yu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #101
Mental Sampling in Multimodal Representations
Jianqiao Zhu · Adam Sanborn · Nick Chater
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #102
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
Amir Dezfouli · Richard Morris · Fabio Ramos · Peter Dayan · Bernard Balleine
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #103
Efficient inference for time-varying behavior during learning
Nicholas Roy · Ji Hyun Bak · Athena Akrami · Carlos Brody · Jonathan W Pillow
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #104
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Tom Dupré la Tour · Thomas Moreau · Mainak Jas · Alexandre Gramfort
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #105
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Anirvan Sengupta · Cengiz Pehlevan · Mariano Tepper · Alexander Genkin · Dmitri Chklovskii
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #106
Connectionist Temporal Classification with Maximum Entropy Regularization
Hu Liu · Sheng Jin · Changshui Zhang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #107
Removing the Feature Correlation Effect of Multiplicative Noise
Zijun Zhang · Yining Zhang · Zongpeng Li
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #108
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Mikhail Belkin · Daniel Hsu · Partha Mitra
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #109
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
Nima Anari · Constantinos Daskalakis · Wolfgang Maass · Christos Papadimitriou · Amin Saberi · Santosh Vempala
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #110
Entropy and mutual information in models of deep neural networks
Marylou Gabrié · Andre Manoel · Clément Luneau · jean barbier · Nicolas Macris · Florent Krzakala · Lenka Zdeborová
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #111
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Benjamin Aubin · Antoine Maillard · jean barbier · Florent Krzakala · Nicolas Macris · Lenka Zdeborová
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #112
A Unified Framework for Extensive-Form Game Abstraction with Bounds
Christian Kroer · Tuomas Sandholm
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #113
Connecting Optimization and Regularization Paths
Arun Suggala · Adarsh Prasad · Pradeep Ravikumar
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #114
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao · Purnamrita Sarkar · Deepayan Chakrabarti
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #115
Learning latent variable structured prediction models with Gaussian perturbations
Kevin Bello · Jean Honorio
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #116
Self-Supervised Generation of Spatial Audio for 360° Video
Pedro Morgado · Nuno Nvasconcelos · Timothy Langlois · Oliver Wang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #117
Symbolic Graph Reasoning Meets Convolutions
Xiaodan Liang · Zhiting Hu · Hao Zhang · Liang Lin · Eric Xing
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #118
Towards Deep Conversational Recommendations
Raymond Li · Samira Ebrahimi Kahou · Hannes Schulz · Vincent Michalski · Laurent Charlin · Chris Pal
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #119
Human-in-the-Loop Interpretability Prior
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #120
Why Is My Classifier Discriminatory?
Irene Chen · Fredrik Johansson · David Sontag
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #121
Link Prediction Based on Graph Neural Networks
Muhan Zhang · Yixin Chen
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #122
KONG: Kernels for ordered-neighborhood graphs
Moez Draief · Konstantin Kutzkov · Kevin Scaman · Milan Vojnovic
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #123
Efficient Stochastic Gradient Hard Thresholding
Pan Zhou · Xiaotong Yuan · Jiashi Feng
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #124
Measures of distortion for machine learning
Leena Chennuru Vankadara · Ulrike von Luxburg
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #125
Relating Leverage Scores and Density using Regularized Christoffel Functions
Edouard Pauwels · Francis Bach · Jean-Philippe Vert
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #126
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Md Enayat Ullah · Poorya Mianjy · Teodor Vanislavov Marinov · Raman Arora
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #127
Learning with SGD and Random Features
Luigi Carratino · Alessandro Rudi · Lorenzo Rosasco
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #128
But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun · Anna Gilbert · Ambuj Tewari
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #129
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello · Lorenzo Rosasco
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #130
Quadrature-based features for kernel approximation
Marina Munkhoeva · Yermek Kapushev · Evgeny Burnaev · Ivan Oseledets
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #131
Processing of missing data by neural networks
Marek Śmieja · Łukasz Struski · Jacek Tabor · Bartosz Zieliński · Przemysław Spurek
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #132
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar · Shami Nisimov · Yaniv Gurwicz · Guy Koren · Gal Novik
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #133
Mallows Models for Top-k Lists
Flavio Chierichetti · Anirban Dasgupta · Shahrzad Haddadan · Ravi Kumar · Silvio Lattanzi
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #134
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava · Eugene Bart · Bob Price · Hanjun Dai · Bo Dai · Srinivas Aluru
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #135
Maximum-Entropy Fine Grained Classification
Abhimanyu Dubey · Otkrist Gupta · Ramesh Raskar · Nikhil Naik
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #136
Efficient Loss-Based Decoding on Graphs for Extreme Classification
Itay Evron · Edward Moroshko · Koby Crammer
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #137
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch · Kalina Jasinska · Mikhail Kuznetsov · Róbert Busa-Fekete · Krzysztof Dembczynski
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #138
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
Corinna Cortes · Vitaly Kuznetsov · Mehryar Mohri · Dmitry Storcheus · Scott Yang
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #139
Deep Structured Prediction with Nonlinear Output Transformations
Colin Graber · Ofer Meshi · Alexander Schwing
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #140
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Roei Herzig · Moshiko Raboh · Gal Chechik · Jonathan Berant · Amir Globerson
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #141
Large Margin Deep Networks for Classification
Gamaleldin Elsayed · Dilip Krishnan · Hossein Mobahi · Kevin Regan · Samy Bengio
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #142
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Neal Jean · Sang Michael Xie · Stefano Ermon
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #143
Multitask Boosting for Survival Analysis with Competing Risks
Alexis Bellot · Mihaela van der Schaar
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #144
Multi-Layered Gradient Boosting Decision Trees
Ji Feng · Yang Yu · Zhi-Hua Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #145
Unsupervised Adversarial Invariance
Ayush Jaiswal · Rex Yue Wu · Wael Abd-Almageed · Prem Natarajan
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #146
Learning Deep Disentangled Embeddings With the F-Statistic Loss
Karl Ridgeway · Michael Mozer
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #147
Learning Latent Subspaces in Variational Autoencoders
Jack Klys · Jake Snell · Richard Zemel
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #148
Dual Swap Disentangling
Zunlei Feng · Xinchao Wang · Chenglong Ke · An-Xiang Zeng · Dacheng Tao · Mingli Song
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #149
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen · Johannes Ballé · Johannes Ballé · George D Toderici
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #150
Group Equivariant Capsule Networks
Jan Eric Lenssen · Matthias Fey · Pascal Libuschewski
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #151
Learning Disentangled Joint Continuous and Discrete Representations
Emilien Dupont
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #152
Image-to-image translation for cross-domain disentanglement
Abel Gonzalez-Garcia · Joost van de Weijer · Yoshua Bengio
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #153
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Bruno Korbar · Du Tran · Lorenzo Torresani
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #154
Non-Adversarial Mapping with VAEs
Yedid Hoshen
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #155
Learning to Teach with Dynamic Loss Functions
Lijun Wu · Fei Tian · Yingce Xia · Yang Fan · Tao Qin · Lai Jian-Huang · Tie-Yan Liu
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #156
Maximizing acquisition functions for Bayesian optimization
James Wilson · Frank Hutter · Marc Deisenroth
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #157
MetaReg: Towards Domain Generalization using Meta-Regularization
Yogesh Balaji · Swami Sankaranarayanan · Rama Chellappa
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #158
Transfer Learning with Neural AutoML
Catherine Wong · Neil Houlsby · Yifeng Lu · Andrea Gesmundo
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #159
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn · Junhyuk Oh · Honglak Lee
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #160
Lifelong Inverse Reinforcement Learning
Jorge A Mendez · Shashank Shivkumar · Eric Eaton
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #161
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer · Mona Meister · Duy Nguyen-Tuong
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #162
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra · Abdullah Rashwan · Wei-Shou Hsu · Pascal Poupart · Prashant Doshi · Georgios Trimponias
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #163
Preference Based Adaptation for Learning Objectives
Yao-Xiang Ding · Zhi-Hua Zhou
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #164
Byzantine Stochastic Gradient Descent
Dan Alistarh · Zeyuan Allen-Zhu · Jerry Li
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #165
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan Foster · Akshay Krishnamurthy
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #166
Online Learning of Quantum States
Scott Aaronson · Xinyi Chen · Elad Hazan · Satyen Kale · Ashwin Nayak
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #167
Horizon-Independent Minimax Linear Regression
Alan Malek · Peter Bartlett
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #168
Factored Bandits
Julian Zimmert · Yevgeny Seldin
Poster
Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 & 230 AB #169
A Model for Learned Bloom Filters and Optimizing by Sandwiching
Michael Mitzenmacher
Invited Talk
Thu Dec 6th 02:15 -- 03:05 PM @ Rooms 220 CDE
Designing Computer Systems for Software 2.0
Kunle Olukotun
Break
Thu Dec 6th 03:05 -- 03:30 PM @
Coffee Break
Spotlight
Thu Dec 6th 03:30 -- 03:35 PM @ Room 220 CD
Robust Subspace Approximation in a Stream
Roie Levin · Anish Prasad Sevekari · David Woodruff
Spotlight
Thu Dec 6th 03:30 -- 03:35 PM @ Room 220 E
Hyperbolic Neural Networks
Octavian Ganea · Gary Becigneul · Thomas Hofmann
Spotlight
Thu Dec 6th 03:30 -- 03:35 PM @ Room 517 CD
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Zhize Li · Jian Li
Spotlight
Thu Dec 6th 03:35 -- 03:40 PM @ Room 220 CD
Efficient nonmyopic batch active search
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett
Spotlight
Thu Dec 6th 03:35 -- 03:40 PM @ Room 220 E
Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer · Ron Banner · Itay Golan · Daniel Soudry
Spotlight
Thu Dec 6th 03:35 -- 03:40 PM @ Room 517 CD
Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han · Haim Avron · Jinwoo Shin
Spotlight
Thu Dec 6th 03:40 -- 03:45 PM @ Room 220 CD
Interactive Structure Learning with Structural Query-by-Committee
Christopher Tosh · Sanjoy Dasgupta
Spotlight
Thu Dec 6th 03:40 -- 03:45 PM @ Room 220 E
Constructing Fast Network through Deconstruction of Convolution
Yunho Jeon · Junmo Kim
Spotlight
Thu Dec 6th 03:40 -- 03:45 PM @ Room 517 CD
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen · Georgios Giannakis · Tao Sun · Wotao Yin
Spotlight
Thu Dec 6th 03:45 -- 03:50 PM @ Room 220 CD
Contour location via entropy reduction leveraging multiple information sources
Alexandre Marques · Remi Lam · Karen Willcox
Spotlight
Thu Dec 6th 03:45 -- 03:50 PM @ Room 220 E
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee · Kibok Lee · Honglak Lee · Jinwoo Shin
Spotlight
Thu Dec 6th 03:45 -- 03:50 PM @ Room 517 CD
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin · Hoi-To Wai · Julie Josse · Olga Klopp · Eric Moulines
Oral
Thu Dec 6th 03:50 -- 04:05 PM @ Room 220 CD
Non-delusional Q-learning and value-iteration
Tyler Lu · Dale Schuurmans · Craig Boutilier
Oral
Thu Dec 6th 03:50 -- 04:05 PM @ Room 220 E
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn · Noah Snavely · Jonathan Tompson · Mohammad Norouzi
Oral
Thu Dec 6th 03:50 -- 04:05 PM @ Room 517 CD
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman · Francis Bach · Sebastien Bubeck · Laurent Massoulié · Yin Tat Lee
Spotlight
Thu Dec 6th 04:05 -- 04:10 PM @ Room 220 CD
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
Andrea Tirinzoni · Marek Petrik · Xiangli Chen · Brian Ziebart
Spotlight
Thu Dec 6th 04:05 -- 04:10 PM @ Room 220 E
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum
Spotlight
Thu Dec 6th 04:05 -- 04:10 PM @ Room 517 CD
Direct Runge-Kutta Discretization Achieves Acceleration
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie
Spotlight
Thu Dec 6th 04:10 -- 04:15 PM @ Room 220 CD
Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger · Thomas Schön
Spotlight
Thu Dec 6th 04:10 -- 04:15 PM @ Room 220 E
Learning Loop Invariants for Program Verification
Xujie Si · Hanjun Dai · Mukund Raghothaman · Mayur Naik · Le Song
Spotlight
Thu Dec 6th 04:10 -- 04:15 PM @ Room 517 CD
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
Song Zhou · Swati Gupta · Madeleine Udell
Spotlight
Thu Dec 6th 04:15 -- 04:20 PM @ Room 220 CD
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor
Spotlight
Thu Dec 6th 04:15 -- 04:20 PM @ Room 220 E
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve · Sebastijan Dumancic · Angelika Kimmig · Thomas Demeester · Luc De Raedt
Spotlight
Thu Dec 6th 04:15 -- 04:20 PM @ Room 517 CD
(Probably) Concave Graph Matching
Haggai Maron · Yaron Lipman
Spotlight
Thu Dec 6th 04:20 -- 04:25 PM @ Room 220 CD
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine
Spotlight
Thu Dec 6th 04:20 -- 04:25 PM @ Room 220 E
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum
Spotlight
Thu Dec 6th 04:20 -- 04:25 PM @ Room 517 CD
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth · Jialei Wang · Adam Smith · Brendan McMahan · Nati Srebro
Oral
Thu Dec 6th 04:25 -- 04:40 PM @ Room 220 CD
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman · Danijar Hafner · George Tucker · Eugene Brevdo · Honglak Lee
Oral
Thu Dec 6th 04:25 -- 04:40 PM @ Room 220 E
Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu
Oral
Thu Dec 6th 04:25 -- 04:40 PM @ Room 517 CD
Smoothed analysis of the low-rank approach for smooth semidefinite programs
Thomas Pumir · Samy Jelassi · Nicolas Boumal
Spotlight
Thu Dec 6th 04:40 -- 04:45 PM @ Room 220 CD
Bilevel learning of the Group Lasso structure
Jordan Frecon · Saverio Salzo · Massimiliano Pontil
Spotlight
Thu Dec 6th 04:40 -- 04:45 PM @ Room 220 E
Improving Neural Program Synthesis with Inferred Execution Traces
Richard Shin · Illia Polosukhin · Dawn Song
Spotlight
Thu Dec 6th 04:40 -- 04:45 PM @ Room 517 CD
Wasserstein Distributionally Robust Kalman Filtering
Soroosh Shafieezadeh Abadeh · Viet Anh Nguyen · Daniel Kuhn · Peyman Mohajerin Esfahani
Spotlight
Thu Dec 6th 04:45 -- 04:50 PM @ Room 220 CD
Binary Classification from Positive-Confidence Data
Takashi Ishida · Gang Niu · Masashi Sugiyama
Spotlight
Thu Dec 6th 04:45 -- 04:50 PM @ Room 220 E
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin · Stefanie Jegelka
Spotlight
Thu Dec 6th 04:45 -- 04:50 PM @ Room 517 CD
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechenskii · Darina Dvinskikh · Alexander Gasnikov · Cesar Uribe · Angelia Nedich
Spotlight
Thu Dec 6th 04:50 -- 04:55 PM @ Room 220 CD
Fully Understanding The Hashing Trick
Lior Kamma · Casper B. Freksen · Kasper Green Larsen
Spotlight
Thu Dec 6th 04:50 -- 04:55 PM @ Room 220 E
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Liwei Wang · Lunjia Hu · Jiayuan Gu · Zhiqiang Hu · Yue Wu · Kun He · John Hopcroft
Spotlight
Thu Dec 6th 04:50 -- 04:55 PM @ Room 517 CD
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
RUI GAO · Liyan Xie · Yao Xie · Huan Xu
Spotlight
Thu Dec 6th 04:55 -- 05:00 PM @ Room 220 CD
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
Raghav Somani · Chirag Gupta · Prateek Jain · Praneeth Netrapalli
Spotlight
Thu Dec 6th 04:55 -- 05:00 PM @ Room 220 E
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang · Mert Sabuncu
Spotlight
Thu Dec 6th 04:55 -- 05:00 PM @ Room 517 CD
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
Yi Zhou · Zhe Wang · Yingbin Liang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #1
Streamlining Variational Inference for Constraint Satisfaction Problems
Aditya Grover · Tudor Achim · Stefano Ermon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #2
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
RUI GAO · Liyan Xie · Yao Xie · Huan Xu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #3
Smoothed analysis of the low-rank approach for smooth semidefinite programs
Thomas Pumir · Samy Jelassi · Nicolas Boumal
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #4
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
Yi Zhou · Zhe Wang · Yingbin Liang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #5
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Zhize Li · Jian Li
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #6
Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han · Haim Avron · Jinwoo Shin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #7
Proximal SCOPE for Distributed Sparse Learning
Shenyi Zhao · Gong-Duo Zhang · Ming-Wei Li · Wu-Jun Li
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #8
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen · Georgios Giannakis · Tao Sun · Wotao Yin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #9
Direct Runge-Kutta Discretization Achieves Acceleration
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #10
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman · Francis Bach · Sebastien Bubeck · Laurent Massoulié · Yin Tat Lee
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #11
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth · Jialei Wang · Adam Smith · Brendan McMahan · Nati Srebro
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #12
(Probably) Concave Graph Matching
Haggai Maron · Yaron Lipman
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #13
Solving Non-smooth Constrained Programs with Lower Complexity than $\mathcal{O}(1/\varepsilon)$: A Primal-Dual Homotopy Smoothing Approach
Xiaohan Wei · Hao Yu · Qing Ling · Michael Neely
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #14
Wasserstein Distributionally Robust Kalman Filtering
Soroosh Shafieezadeh Abadeh · Viet Anh Nguyen · Daniel Kuhn · Peyman Mohajerin Esfahani
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #15
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechenskii · Darina Dvinskikh · Alexander Gasnikov · Cesar Uribe · Angelia Nedich
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #16
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
Song Zhou · Swati Gupta · Madeleine Udell
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #17
Stochastic Spectral and Conjugate Descent Methods
Dmitry Kovalev · Peter Richtarik · Eduard Gorbunov · Elnur Gasanov
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #18
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
Yaodong Yu · Pan Xu · Quanquan Gu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #19
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
Yi Xu · Jing Rong · Tianbao Yang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #20
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
Kush Bhatia · Aldo Pacchiano · Nicolas Flammarion · Peter Bartlett · Michael Jordan
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #21
Sparse DNNs with Improved Adversarial Robustness
Yiwen Guo · Chao Zhang · Changshui Zhang · Yurong Chen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #22
Constructing Fast Network through Deconstruction of Convolution
Yunho Jeon · Junmo Kim
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #23
Learning Loop Invariants for Program Verification
Xujie Si · Hanjun Dai · Mukund Raghothaman · Mayur Naik · Le Song
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #24
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #25
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #26
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Liwei Wang · Lunjia Hu · Jiayuan Gu · Zhiqiang Hu · Yue Wu · Kun He · John Hopcroft
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #27
Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer · Ron Banner · Itay Golan · Daniel Soudry
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #28
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin · Stefanie Jegelka
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #29
Hyperbolic Neural Networks
Octavian Ganea · Gary Becigneul · Thomas Hofmann
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #30
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee · Kibok Lee · Honglak Lee · Jinwoo Shin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #31
Improving Neural Program Synthesis with Inferred Execution Traces
Richard Shin · Illia Polosukhin · Dawn Song
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #32
Scaling the Poisson GLM to massive neural datasets through polynomial approximations
David Zoltowski · Jonathan W Pillow
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #33
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn · Noah Snavely · Jonathan Tompson · Mohammad Norouzi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #34
Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #35
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks · Mantas Mazeika · Duncan Wilson · Kevin Gimpel
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #37
A Reduction for Efficient LDA Topic Reconstruction
Matteo Almanza · Flavio Chierichetti · Alessandro Panconesi · Andrea Vattani
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #38
A Unified View of Piecewise Linear Neural Network Verification
Rudy Bunel · Ilker Turkaslan · Philip Torr · Pushmeet Kohli · Pawan K Mudigonda
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #39
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Dimitris Bertsimas · Christopher McCord
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #40
The Convergence of Sparsified Gradient Methods
Dan Alistarh · Torsten Hoefler · Mikael Johansson · Nikola Konstantinov · Sarit Khirirat · Cedric Renggli
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #41
Estimating Learnability in the Sublinear Data Regime
Weihao Kong · Gregory Valiant
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #42
Learning convex polytopes with margin
Lee-Ad Gottlieb · Eran Kaufman · Aryeh Kontorovich · Gabriel Nivasch
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #43
Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling
Shannon McCurdy
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #44
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang · Farbod Roosta-Khorasani · Peng Xu · Michael W Mahoney
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #45
Wavelet regression and additive models for irregularly spaced data
Asad Haris · Ali Shojaie · Noah Simon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #46
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
Pan Zhou · Xiaotong Yuan · Jiashi Feng
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #47
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
Tomoya Murata · Taiji Suzuki
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #48
Robust Subspace Approximation in a Stream
Roie Levin · Anish Prasad Sevekari · David Woodruff
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #50
A Practical Algorithm for Distributed Clustering and Outlier Detection
Jiecao Chen · Erfan Sadeqi Azer · Qin Zhang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #51
Compact Representation of Uncertainty in Clustering
Craig Greenberg · Nicholas Monath · Ari Kobren · Patrick Flaherty · Andrew McGregor · Andrew McCallum
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #52
Bipartite Stochastic Block Models with Tiny Clusters
Stefan Neumann
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #53
Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms
Vincent Cohen-Addad · Varun Kanade · Frederik Mallmann-Trenn
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #54
Understanding Regularized Spectral Clustering via Graph Conductance
Yilin Zhang · Karl Rohe
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #55
Query K-means Clustering and the Double Dixie Cup Problem
I Chien · Chao Pan · Olgica Milenkovic
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #56
How to tell when a clustering is (approximately) correct using convex relaxations
Marina Meila
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #57
Derivative Estimation in Random Design
Yu Liu · Kris De Brabanter
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #58
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression
Neha Gupta · Aaron Sidford
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #59
Boosted Sparse and Low-Rank Tensor Regression
Lifang He · Kun Chen · Wanwan Xu · Jiayu Zhou · Fei Wang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #60
An Efficient Pruning Algorithm for Robust Isotonic Regression
Cong Han Lim
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #61
A convex program for bilinear inversion of sparse vectors
Alireza Aghasi · Ali Ahmed · Paul Hand · Babhru Joshi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #62
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms
Kishan Wimalawarne · Hiroshi Mamitsuka
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #63
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
Raghav Somani · Chirag Gupta · Prateek Jain · Praneeth Netrapalli
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #64
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
Fariborz Salehi · Ehsan Abbasi · Babak Hassibi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #65
On Controllable Sparse Alternatives to Softmax
Anirban Laha · Saneem Ahmed Chemmengath · Priyanka Agrawal · Mitesh Khapra · Karthik Sankaranarayanan · Harish Ramaswamy
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #66
Sparse PCA from Sparse Linear Regression
Guy Bresler · Sung Min Park · Madalina Persu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #67
Efficient Anomaly Detection via Matrix Sketching
Vatsal Sharan · Parikshit Gopalan · Udi Wieder
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #69
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Minshuo Chen · Lin Yang · Mengdi Wang · Tuo Zhao
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #70
Contrastive Learning from Pairwise Measurements
Yi Chen · Zhuoran Yang · Yuchen Xie · Princeton Zhaoran Wang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #71
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Minhyuk Sung · Hao Su · Ronald Yu · Leonidas J Guibas
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #72
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
Theo Lacombe · Marco Cuturi · Steve OUDOT
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #73
Representation Learning of Compositional Data
Marta Avalos · Richard Nock · Cheng Soon Ong · Julien Rouar · Ke Sun
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #74
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD
Zeyuan Allen-Zhu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #75
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
Loucas Pillaud-Vivien · Alessandro Rudi · Francis Bach
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #76
Optimal Subsampling with Influence Functions
Daniel Ting · Eric Brochu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #77
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
Isao Ishikawa · Keisuke Fujii · Masahiro Ikeda · Yuka Hashimoto · Yoshinobu Kawahara
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #78
Random Feature Stein Discrepancies
Jonathan Huggins · Lester Mackey
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #79
Informative Features for Model Comparison
Wittawat Jitkrittum · Heishiro Kanagawa · Patsorn Sangkloy · James Hays · Bernhard Schölkopf · Arthur Gretton
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #80
On Fast Leverage Score Sampling and Optimal Learning
Alessandro Rudi · Daniele Calandriello · Luigi Carratino · Lorenzo Rosasco
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #81
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
Tam Le · Makoto Yamada
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #82
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Shahin Shahrampour · Vahid Tarokh
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #83
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang · Mianzhi Wang · Yijian Xiang · Yan Huang · Arye Nehorai
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #84
Nonparametric Density Estimation under Adversarial Losses
Shashank Singh · Ananya Uppal · Boyue Li · Chun-Liang Li · Manzil Zaheer · Barnabas Poczos
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #85
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
Priyank Jaini · Pascal Poupart · Yaoliang Yu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #86
Gaussian Process Conditional Density Estimation
Vincent Dutordoir · Hugh Salimbeni · James Hensman · Marc Deisenroth
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #87
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin · Hoi-To Wai · Julie Josse · Olga Klopp · Eric Moulines
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #88
Mixture Matrix Completion
Daniel Pimentel-Alarcon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #89
Multivariate Time Series Imputation with Generative Adversarial Networks
Yonghong Luo · Xiangrui Cai · Ying ZHANG · Jun Xu · Yuan xiaojie
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #90
Fully Understanding The Hashing Trick
Lior Kamma · Casper B. Freksen · Kasper Green Larsen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #91
Learning semantic similarity in a continuous space
Michel Deudon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #92
Bilevel learning of the Group Lasso structure
Jordan Frecon · Saverio Salzo · Massimiliano Pontil
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #93
Bayesian Structure Learning by Recursive Bootstrap
Raanan Y. Rohekar · Yaniv Gurwicz · Shami Nisimov · Guy Koren · Gal Novik
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #94
Learning from Group Comparisons: Exploiting Higher Order Interactions
Yao Li · Minhao Cheng · Kevin Fujii · Fushing Hsieh · Cho-Jui Hsieh
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #95
A Structured Prediction Approach for Label Ranking
Anna Korba · Alexandre Garcia · Florence d'Alché-Buc
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #96
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
Chen Dan · Liu Leqi · Bryon Aragam · Pradeep Ravikumar · Eric Xing
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #97
Binary Classification from Positive-Confidence Data
Takashi Ishida · Gang Niu · Masashi Sugiyama
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #98
Learning SMaLL Predictors
Vikas Garg · Ofer Dekel · Lin Xiao
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #99
Contour location via entropy reduction leveraging multiple information sources
Alexandre Marques · Remi Lam · Karen Willcox
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #100
Multi-Class Learning: From Theory to Algorithm
Jian Li · Yong Liu · Rong Yin · Hua Zhang · Lizhong Ding · Weiping Wang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #101
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang · Mert Sabuncu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #102
A Smoother Way to Train Structured Prediction Models
Venkata Krishna Pillutla · Vincent Roulet · Sham Kakade · Zaid Harchaoui
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #103
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu · Miltiadis Allamanis · Marc Brockschmidt · Alexander Gaunt
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #104
Learning Beam Search Policies via Imitation Learning
Renato Negrinho · Matthew Gormley · Geoffrey Gordon
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #105
Loss Functions for Multiset Prediction
Sean Welleck · Zixin Yao · Yu Gai · Jialin Mao · Zheng Zhang · Kyunghyun Cho
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #106
Learning Confidence Sets using Support Vector Machines
Wenbo Wang · Xingye Qiao
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #107
Fast Similarity Search via Optimal Sparse Lifting
Wenye Li · Jingwei Mao · Yin Zhang · Shuguang Cui
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #108
The Sparse Manifold Transform
Yubei Chen · Dylan Paiton · Bruno Olshausen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #109
When do random forests fail?
Cheng Tang · Damien Garreau · Ulrike von Luxburg
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #110
Diverse Ensemble Evolution: Curriculum Data-Model Marriage
Tianyi Zhou · Shengjie Wang · Jeff Bilmes
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #111
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Jesse Krijthe · Marco Loog
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #112
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
Haitian Sun · William Cohen · Lidong Bing
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #113
Learning to Multitask
Yu Zhang · Ying Wei · Qiang Yang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #114
CatBoost: unbiased boosting with categorical features
Liudmila Prokhorenkova · Gleb Gusev · Aleksandr Vorobev · Anna Veronika Dorogush · Andrey Gulin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #115
Supervised autoencoders: Improving generalization performance with unsupervised regularizers
Lei Le · Andrew Patterson · Martha White
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #116
Representation Learning for Treatment Effect Estimation from Observational Data
Liuyi Yao · Sheng Li · Yaliang Li · Mengdi Huai · Jing Gao · Aidong Zhang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #117
SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi · David Poole
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #118
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve · Sebastijan Dumancic · Angelika Kimmig · Thomas Demeester · Luc De Raedt
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #119
Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija · Bryan Perozzi · Rami Al-Rfou · Alexander Alemi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #120
Invariant Representations without Adversarial Training
Daniel Moyer · Shuyang Gao · Rob Brekelmans · Aram Galstyan · Greg Ver Steeg
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #121
Domain-Invariant Projection Learning for Zero-Shot Recognition
An Zhao · Mingyu Ding · Jiechao Guan · Zhiwu Lu · Tao Xiang · Ji-Rong Wen
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #122
Unsupervised Learning of View-invariant Action Representations
Junnan Li · Yongkang Wong · Qi Zhao · Mohan Kankanhalli
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #123
Neural Architecture Optimization
Renqian Luo · Fei Tian · Tao Qin · Enhong Chen · Tie-Yan Liu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #124
Scalable Hyperparameter Transfer Learning
Valerio Perrone · Rodolphe Jenatton · Matthias W Seeger · Cedric Archambeau
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #125
Learning To Learn Around A Common Mean
Giulia Denevi · Carlo Ciliberto · Dimitris Stamos · Massimiliano Pontil
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #126
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
JING LI · Rafal Mantiuk · Junle Wang · Suiyi Ling · Patrick Le Callet
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #127
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
Shivapratap Gopakumar · Sunil Gupta · Santu Rana · Vu Nguyen · Svetha Venkatesh
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #128
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
Yuxin Chen · Adish Singla · Oisin Mac Aodha · Pietro Perona · Yisong Yue
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #129
Active Learning for Non-Parametric Regression Using Purely Random Trees
Jack Goetz · Ambuj Tewari · Paul Zimmerman
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #130
Interactive Structure Learning with Structural Query-by-Committee
Christopher Tosh · Sanjoy Dasgupta
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #131
Efficient nonmyopic batch active search
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #132
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
Stephen Mussmann · Percy Liang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #133
Online Adaptive Methods, Universality and Acceleration
Yehuda Kfir Levy · Alp Yurtsever · Volkan Cevher
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #134
Online Improper Learning with an Approximation Oracle
Elad Hazan · Wei Hu · Yuanzhi Li · zhiyuan li
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #135
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
Hippolyt Ritter · Aleksandar Botev · David Barber
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #136
Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Asier Mujika · Florian Meier · Angelika Steger
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #137
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Claudio Gentile · Nikos Parotsidis · Fabio Vitale
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #138
Generalized Inverse Optimization through Online Learning
Chaosheng Dong · Yiran Chen · Bo Zeng
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #139
Adaptive Online Learning in Dynamic Environments
Lijun Zhang · Shiyin Lu · Zhi-Hua Zhou
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #140
Online convex optimization for cumulative constraints
Jianjun Yuan · Andrew Lamperski
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #141
Efficient online algorithms for fast-rate regret bounds under sparsity
Pierre Gaillard · Olivier Wintenberger
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #142
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Shinji Ito · Daisuke Hatano · Sumita Hanna · Akihiro Yabe · Takuro Fukunaga · Naonori Kakimura · Ken-Ichi Kawarabayashi
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #144
Policy Regret in Repeated Games
Raman Arora · Michael Dinitz · Teodor Vanislavov Marinov · Mehryar Mohri
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #145
Query Complexity of Bayesian Private Learning
Kuang Xu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #146
The Limits of Post-Selection Generalization
Jonathan Ullman · Adam Smith · Kobbi Nissim · Uri Stemmer · Thomas Steinke
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #147
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
Emilie Kaufmann · Wouter Koolen · Aurélien Garivier
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #148
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
Lixing Chen · Jie Xu · Zhuo Lu
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #149
TopRank: A practical algorithm for online stochastic ranking
Tor Lattimore · Branislav Kveton · Shuai Li · Csaba Szepesvari
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #150
A Bandit Approach to Sequential Experimental Design with False Discovery Control
Kevin Jamieson · Lalit Jain
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #151
Adaptation to Easy Data in Prediction with Limited Advice
Tobias Thune · Yevgeny Seldin
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #152
Differentially Private Contextual Linear Bandits
Roshan Shariff · Or Sheffet
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #153
Community Exploration: From Offline Optimization to Online Learning
Xiaowei Chen · Weiran Huang · Wei Chen · John C. S. Lui
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #154
Adaptive Learning with Unknown Information Flows
Yonatan Gur · Ahmadreza Momeni
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #155
Multi-armed Bandits with Compensation
Siwei Wang · Longbo Huang
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #156
Bandit Learning with Implicit Feedback
Yi Qi · Qingyun Wu · Hongning Wang · Jie Tang · Maosong Sun
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #157
Optimistic optimization of a Brownian
Jean-Bastien Grill · Michal Valko · Remi Munos
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #158
Bandit Learning with Positive Externalities
Virag Shah · Jose Blanchet · Ramesh Johari
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #159
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Shi Dong · Benjamin Van Roy
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #160
Distributed Multi-Player Bandits - a Game of Thrones Approach
Ilai Bistritz · Amir Leshem
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #161
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu · Karen Feng · Barbara Engelhardt
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #162
Non-delusional Q-learning and value-iteration
Tyler Lu · Dale Schuurmans · Craig Boutilier
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #163
Differentiable MPC for End-to-end Planning and Control
Brandon Amos · Ivan Jimenez · Jacob Sacks · Byron Boots · J. Zico Kolter
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #164
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #165
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #166
Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger · Thomas Schön
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #167
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman · Danijar Hafner · George Tucker · Eugene Brevdo · Honglak Lee
Poster
Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 & 230 AB #168
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
Andrea Tirinzoni · Marek Petrik · Xiangli Chen · Brian Ziebart