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Author Information
Sebastian Farquhar (University of Oxford)
Erik Daxberger (University of Cambridge)
Andreas Look (Bosch Center for Artificial Intelligence)
Matt Benatan (IBM Research UK)
Ruiyi Zhang (Duke University)
I am currently a fourth-year Ph.D. student at Department of Computer Science, Duke University. My research interest is Deep Learning.
Marton Havasi (University of Cambridge)
Fredrik Gustafsson (Uppsala University)
James A Brofos (The MITRE Corporation)
Nabeel Seedat (Cornell University (USA) & University of Witwatersrand (South Africa))
Micha Livne (University of Toronto, Vector Institute, Seraph Computer Vision Labs)
Micha is a PhD candidate towards the end of his PhD, in the [Computational Vision Group][uoft-cv] / Artificial Intelligence Lab, part of the [Department of Computer Science][dcs] at University of Toronto, and in the [Vector Institute][vector], Toronto. He is supervised by prof. [David Fleet][fleet], and has been concentrating for the better part of the last decade on machine learning and optimization problems. Specifically on 3D video tracking combined with physical models. Micha holds a BSc. in Electrical Engineering and a BSc. in Physics from the [Technion, Israel Institute of Technology][technion], both with Summa Cum Laude honors (top 3% of his class). He obtained his MSc. from University of Toronto under [David][fleet]'s supervision, where he researched inferring attributes, such as gender, weight, happiness and anxiety level, from motion capture data and from video tracking. Micha also founded a Computer Vision/Machine Learning lab, with the goal of pushing ahead CV/ML research, while giving smaller start-ups (who typically cannot afford to hire ML researchers) accessibility to one of the biggest revolutions that mankind is about to experience. He also believes that the social aspect of that revolution is ignored, by large. As part of his efforts for a better future for all, he is working to encourage an open discussion about the social implications of the AI revolution, in order reduce fears from AI, and replace it with openness and better understanding of what possibilities lies ahead. In his spare time he enjoys hiking, canoeing, SUP, bike riding, skiing, ice skating, or basically every outdoor/indoor sport activity he finds the time to put into. [fleet]: http://www.cs.toronto.edu/~fleet [dcs]: http://web.cs.toronto.edu/ [uoft-cv]: http://www.cs.toronto.edu/vis/ [vector]: http://vectorinstitute.ai/ [seraphlabs]: http://seraphlabs.ca/ [technion]: https://www.technion.ac.il/en/
Ivan Ustyuzhaninov (University of Tübingen)
Adam Cobb (University of Oxford)
Felix D McGregor (Stellenbosch University)
Patrick McClure (NIH)
Tim R. Davidson (University of Amsterdam, Aiconic)
Gaurush Hiranandani (University of Illinois at Urbana-Champaign)
Sanjeev Arora (Princeton University)
Masha Itkina (Stanford University)
Didrik Nielsen (DTU Compute)
William Harvey (University of British Columbia)
I'm a second year PhD student at the University of British Columbia, supervised by Frank Wood. My research interests are in AI, currently focusing on how attention can improve statistical inference.
Matias Valdenegro-Toro (Heriot-Watt University)
Stefano Peluchetti (Cogent Labs)
Riccardo Moriconi (Imperial College London)
Tianyu Cui (Aalto University)
Vaclav Smidl (Institute of Information Theory and Automation)
Taylan Cemgil (DeepMind)
Jack Fitzsimons (University of Oxford)
He Zhao (Monash University)
mariana vargas vieyra (Inria Lille Nord Europe)
Apratim Bhattacharyya (Max Planck Institute for Informatics)
Rahul Sharma (IIT Kanpur)
I am currently pursuing a Ph.D. degree from IIT Kanpur in the department of Computer Science. My research interest lies in the area of Bayesian machine learning.
Geoffroy Dubourg-Felonneau (Cambridge Cancer Genomics)
Jonathan Warrell (Yale University)
Slava Voloshynovskiy (University of Geneva)
Mihaela Rosca (Google DeepMind)
Jiaming Song (Stanford University)
I am a first year Ph.D. student in Stanford University. I think about problems in machine learning and deep learning under the supervision of Stefano Ermon. I did my undergrad at Tsinghua University, where I was lucky enough to collaborate with Jun Zhu and Lawrence Carin on scalable Bayesian machine learning.
Andrew Ross (Harvard University)
Homa Fashandi (LG Electronics Toronto AI lab)
Ruiqi Gao (University of California, Los Angeles)
Hooshmand Shokri Razaghi (Columbia University)
Joshua Chang (NIH)
Zhenzhong Xiao (University of Oxford)
Vanessa Boehm (UC Berkeley)
Giorgio Giannone (NNAISENSE)
Science is built up with data, as a house is with stones. But a collection of data is no more a science than a heap of stones is a house. (J.H. Poincaré)
Ranganath Krishnan (Intel Labs)
Joe Davison (Harvard University)
Arsenii Ashukha (Samsung AI)
PhD Candidate at Bayesian Methods Research Group and Samsung AI Center Moscow supervised by Dmitriy Vetrov, working on probabilistic deep learning.
Jeremiah Liu (Google Research / Harvard)
Sicong (Sheldon) Huang (Vector Institute, University of Toronto)
I am a student of the human condition. I'm equally interested in people and machine and my long term goal is to empower the former with the latter, towards their full potential. I am interested in deep learning, statistics and information theory, cognitive neuroscience and clinical psychology, brain computer interface and machine learning for health, and music. I am currently a first year PhD Student in CS and Neuroscience at U of T and the Vector Institute advised by Prof. Marzyeh Ghassemi and Prof. Frank Rudzicz, I also work with Prof. Roger Grosse and Prof. Alireza Makhzani, more detailed and up to date information can be found in my CV
Evgenii Nikishin (Cornell University)
Sunho Park (Cleveland Clinic)
Nilesh Ahuja (Intel)
Mahesh Subedar (Intel Corporation)
Artyom Gadetsky (National Research University Higher School of Economics)
Jhosimar Arias Figueroa (Independent)
Tim G. J. Rudner (University of Oxford)
Waseem Aslam (University of Oxford)
Adrián Csiszárik (Alfred Renyi Institute of Mathematics)
John Moberg (Peltarion)
Ali Hebbal (ONERA)
PhD student at ONERA- The French Aerospace Lab
Kathrin Grosse (CISPA Helmholtz Center for Information Security)
Pekka Marttinen (Aalto University)
Bang An (State University of New York at Buffalo)
Hlynur Jónsson (IBM Zürich)
Samuel Kessler (University of Oxford)
Abhishek Kumar (Google)
Mikhail Figurnov (DeepMind)
Omesh Tickoo (Intel)
Steindor Saemundsson (Imperial College London)
Ari Heljakka (Aalto University)
Dániel Varga (Alfréd Rényi Institute of Mathematics)
Niklas Heim (Czech Technical University)
Simone Rossi (EURECOM)
Max Laves (Leibniz Universität Hannover)
Waseem Gharbieh (Element AI)
Nicholas Roberts (Carnegie Mellon University)
Luis Armando Pérez Rey (Eindhoven University of Technology)
Matthew Willetts (University of Oxford)
Prithvijit Chakrabarty (University of Massachusetts, Amherst)
I am currently an applied scientist at Amazon. I recently completed my Master's in Computer Science at University of Massachusetts, Amherst.
Sumedh Ghaisas (DeepMind)
Carl Shneider (Dutch National Center for Mathematics and Computer Science (CWI))
Carl Shneider has studied at Rensselaer Polytechnic Institute in the US, the Swiss Federal Institute of Technology (ETH Zurich) in Switzerland, the University of Cambridge in the UK, and at Utrecht University and Leiden University in the Netherlands. He currently works as a postdoc in space weather and machine learning in the Multiscale Dynamics group at the Dutch National Institute for Mathematics and Computer Science (CWI) at the Amsterdam Science Park. He holds a PhD in astrophysics from Leiden Observatory, Leiden University. After his PhD, Carl also worked in precision medicine, first at the Diagnostic Image Analysis Group (DIAG) at the Radboud University Medical Center, followed by a postdoc at the Utrecht University Medical Center where his research focused on cancer genomics and utilized techniques from bioinformatics and machine learning. Carl enjoys participating in public science outreach activities and is the event manager for the Utrecht city branch of the annual Pint of Science Public Science Festival which takes place worldwide. He also has language competencies in Russian, Spanish, German, and Dutch.
Wray Buntine (Monash University)
Wray Buntine is a full professor at Monash University where he is directory of the Machine Learning Group. He was previously at NICTA in Canberra, Helsinki Institute for Information Technology where he ran a semantic search project, NASA Ames Research Center, University of California, Berkeley, and Google. In the '90s he was involved in a string of startups for both Wall Street and Silicon Valley. He is known for Bayesian machine learning, non-parametrics and document analysis, having been a driving force in the use of ensembling, graphical models, and nonparametric algorithms.
Kamil Adamczewski (Max Planck Institute for Intelligent Systems)
Xavier Gitiaux (George Mason University)
Suwen Lin (university of notre dame)
Hao Fu (Duke University)
Gunnar Rätsch (ETH Zürich)
Aidan Gomez (Oxford University)
Erik Bodin (University of Bristol)
Dinh Phung (Monash University)
Lennart Svensson (Chalmers University of Technology, Göteborg)
Juliano Tusi Amaral Laganá Pinto (Chalmers University of Technology)
Milad Alizadeh (University of Oxford)
Jianzhun Du (Harvard University)
Kevin Murphy (Google)
Beatrix Benkő (Eötvös Loránd University)
Shashaank Vattikuti (National Institutes of Health)
Jonathan Gordon (University of Cambridge)
Christopher Kanan (Rochester Institute of Technology)
I'm an assistant professor running a lab that works on lifelong learning, low-show learning, and visual question answering. Most of my lab's work uses deep neural networks. I received my PhD from UC San Diego. I am also a Visiting Assistant Professor at Cornell Tech and a Senior AI Scientist at Paige, where I lead a team working on building deep learning systems for detecting cancer.
Sontje Ihler (Leibniz University Hanover)
Darin Graham (LG Electronics Toronto AI Lab)
Darin has over 20 years of experience leading innovative initiatives that take creative ideas to the marketplace. He brings extensive international experience in leading R&D programs and helping build innovation ecosystems in Canada, the United States, New Zealand, and the United Kingdom. A champion of collaboration, he has worked closely with a variety of unique groups to engage industry with academic and research institutions to enhance technology-based applied outcomes, particularly related to Artificial Intelligence (AI), facilitating economic growth through increasing knowledge and helping develop human capital. Darin has just taken on a new role to head-up R&D strategy and operational AI activities for LG Electronics, establishing their new lab in Toronto. Recently as a member of the founding operational team, Darin helped lead the creation and formation of the Vector Institute – the premier AI research institute in Canada. He also helped build and launch Samsung’s AI lab in Toronto. He has held the primary leadership position in a number of organizations, including ORION (Ontario's Research and Innovation Optical Network), NZi3 (New Zealand's ICT Innovation Institute), and CITO (Communications and Information Technology Ontario, an Ontario Centre of Excellence). Darin received his PhD in Aerospace Engineering from the University of Toronto, with his thesis focused on advanced neural networks for autonomous robotic control systems; MASc in Aerospace Engineering from the University of Toronto, and BMath in Computer Science and Applied Mathematics from the University of Waterloo.
Michael Teng (University of Oxford (visiting at University of British Columbia))
Louis Kirsch (The Swiss AI Lab IDSIA)
Tomas Pevny (Czech Technical University)
Taras Holotyak (University of Geneva)
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Severin Husmann · Hugo Yèche · Gunnar Rätsch · Rita Kuznetsova -
2022 : HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data »
Sophie Wharrie · Zhiyu Yang · Vishnu Raj · Remo Monti · Rahul Gupta · Ying Wang · Alicia Martin · Luke O'Connor · Samuel Kaski · Pekka Marttinen · Pier Palamara · Christoph Lippert · Andrea Ganna -
2022 Workshop: Learning from Time Series for Health »
Sana Tonekaboni · Thomas Hartvigsen · Satya Narayan Shukla · Gunnar Rätsch · Marzyeh Ghassemi · Anna Goldenberg -
2022 Poster: Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity »
Mucong Ding · Tahseen Rabbani · Bang An · Evan Wang · Furong Huang -
2022 Poster: VICE: Variational Interpretable Concept Embeddings »
Lukas Muttenthaler · Charles Zheng · Patrick McClure · Robert Vandermeulen · Martin N Hebart · Francisco Pereira -
2022 Poster: Concrete Score Matching: Generalized Score Matching for Discrete Data »
Chenlin Meng · Kristy Choi · Jiaming Song · Stefano Ermon -
2022 Poster: LISA: Learning Interpretable Skill Abstractions from Language »
Divyansh Garg · Skanda Vaidyanath · Kuno Kim · Jiaming Song · Stefano Ermon -
2022 Poster: New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound »
Arushi Gupta · Nikunj Saunshi · Dingli Yu · Kaifeng Lyu · Sanjeev Arora -
2022 Poster: Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent »
Zhiyuan Li · Tianhao Wang · Jason Lee · Sanjeev Arora -
2022 Poster: Denoising Diffusion Restoration Models »
Bahjat Kawar · Michael Elad · Stefano Ermon · Jiaming Song -
2022 Poster: Why neural networks find simple solutions: The many regularizers of geometric complexity »
Benoit Dherin · Michael Munn · Mihaela Rosca · David Barrett -
2022 Poster: Stochastic Multiple Target Sampling Gradient Descent »
Hoang Phan · Ngoc Tran · Trung Le · Toan Tran · Nhat Ho · Dinh Phung -
2022 Poster: Exploring through Random Curiosity with General Value Functions »
Aditya Ramesh · Louis Kirsch · Sjoerd van Steenkiste · Jürgen Schmidhuber -
2022 Poster: Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations »
Alexander Immer · Tycho van der Ouderaa · Gunnar Rätsch · Vincent Fortuin · Mark van der Wilk -
2022 Poster: Deconfounded Representation Similarity for Comparison of Neural Networks »
Tianyu Cui · Yogesh Kumar · Pekka Marttinen · Samuel Kaski -
2022 Poster: Machine Learning on Graphs: A Model and Comprehensive Taxonomy »
Ines Chami · Sami Abu-El-Haija · Bryan Perozzi · Christopher Ré · Kevin Murphy -
2022 Poster: All You Need is a Good Functional Prior for Bayesian Deep Learning »
Ba-Hien Tran · Simone Rossi · Dimitrios Milios · Maurizio Filippone -
2022 Poster: Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction »
Kaifeng Lyu · Zhiyuan Li · Sanjeev Arora -
2022 Poster: Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data »
Nabeel Seedat · Jonathan Crabbé · Ioana Bica · Mihaela van der Schaar -
2022 Poster: On the SDEs and Scaling Rules for Adaptive Gradient Algorithms »
Sadhika Malladi · Kaifeng Lyu · Abhishek Panigrahi · Sanjeev Arora -
2022 Poster: Transferring Fairness under Distribution Shifts via Fair Consistency Regularization »
Bang An · Zora Che · Mucong Ding · Furong Huang -
2021 : Invited talk 2 »
Sanjeev Arora -
2021 : VAEs meet Diffusion Models: Efficient and High-Fidelity Generation »
Kushagra Pandey · Avideep Mukherjee · Piyush Rai · Abhishek Kumar -
2021 Workshop: 4th Robot Learning Workshop: Self-Supervised and Lifelong Learning »
Alex Bewley · Masha Itkina · Hamidreza Kasaei · Jens Kober · Nathan Lambert · Julien PEREZ · Ransalu Senanayake · Vincent Vanhoucke · Markus Wulfmeier · Igor Gilitschenski -
2021 Workshop: Bayesian Deep Learning »
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
Neil Band · Tim G. J. Rudner · Qixuan Feng · Angelos Filos · Zachary Nado · Mike Dusenberry · Ghassen Jerfel · Dustin Tran · Yarin Gal -
2021 : Invited Talk 2 »
Mihaela Rosca -
2021 Oral: Evaluating Gradient Inversion Attacks and Defenses in Federated Learning »
Yangsibo Huang · Samyak Gupta · Zhao Song · Kai Li · Sanjeev Arora -
2021 Poster: Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces »
Kirill Struminsky · Artyom Gadetsky · Denis Rakitin · Danil Karpushkin · Dmitry Vetrov -
2021 Poster: Multi-Facet Clustering Variational Autoencoders »
Fabian Falck · Haoting Zhang · Matthew Willetts · George Nicholson · Christopher Yau · Chris C Holmes -
2021 Poster: On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) »
Zhiyuan Li · Sadhika Malladi · Sanjeev Arora -
2021 Poster: On Path Integration of Grid Cells: Group Representation and Isotropic Scaling »
Ruiqi Gao · Jianwen Xie · Xue-Xin Wei · Song-Chun Zhu · Ying Nian Wu -
2021 Poster: Imitation with Neural Density Models »
Kuno Kim · Akshat Jindal · Yang Song · Jiaming Song · Yanan Sui · Stefano Ermon -
2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine -
2021 Poster: Similarity and Matching of Neural Network Representations »
Adrián Csiszárik · Péter Kőrösi-Szabó · Ákos Matszangosz · Gergely Papp · Dániel Varga -
2021 Poster: Evaluating Gradient Inversion Attacks and Defenses in Federated Learning »
Yangsibo Huang · Samyak Gupta · Zhao Song · Kai Li · Sanjeev Arora -
2021 Poster: D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation »
Abhishek Sinha · Jiaming Song · Chenlin Meng · Stefano Ermon -
2021 Poster: Model Selection for Bayesian Autoencoders »
Ba-Hien Tran · Simone Rossi · Dimitrios Milios · Pietro Michiardi · Edwin Bonilla · Maurizio Filippone -
2021 Poster: Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems »
Jiayu Chen · Yuanxin Zhang · Yuanfan Xu · Huimin Ma · Huazhong Yang · Jiaming Song · Yu Wang · Yi Wu -
2021 Poster: Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias »
Kaifeng Lyu · Zhiyuan Li · Runzhe Wang · Sanjeev Arora -
2021 Poster: Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models »
Phil Chen · Masha Itkina · Ransalu Senanayake · Mykel J Kochenderfer -
2021 Poster: Diversity Enhanced Active Learning with Strictly Proper Scoring Rules »
Wei Tan · Lan Du · Wray Buntine -
2021 Poster: Pseudo-Spherical Contrastive Divergence »
Lantao Yu · Jiaming Song · Yang Song · Stefano Ermon -
2021 Poster: IQ-Learn: Inverse soft-Q Learning for Imitation »
Divyansh Garg · Shuvam Chakraborty · Chris Cundy · Jiaming Song · Stefano Ermon -
2021 Poster: Exploiting Domain-Specific Features to Enhance Domain Generalization »
Manh-Ha Bui · Toan Tran · Anh Tran · Dinh Phung -
2021 Poster: On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources »
Trung Phung · Trung Le · Tung-Long Vuong · Toan Tran · Anh Tran · Hung Bui · Dinh Phung -
2021 Poster: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations »
Tim G. J. Rudner · Cong Lu · Michael A Osborne · Yarin Gal · Yee Teh -
2021 Poster: CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation »
Yusuke Tashiro · Jiaming Song · Yang Song · Stefano Ermon -
2021 Poster: Understanding the Generalization Benefit of Model Invariance from a Data Perspective »
Sicheng Zhu · Bang An · Furong Huang -
2021 : Q&A Oral presentations »
Matias Valdenegro-Toro · Andres Munoz Medina · Johan Obando Ceron · Anil Batra -
2021 : Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings »
Matias Valdenegro-Toro -
2021 Poster: Meta Learning Backpropagation And Improving It »
Louis Kirsch · Jürgen Schmidhuber -
2021 Poster: A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models »
Severi Rissanen · Pekka Marttinen -
2020 : 1 - Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs »
Giorgio Giannone -
2020 : Keynote speech: Sanjeev Arora (PGDL) »
Sanjeev Arora · Yiding Jiang -
2020 : QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings »
Dai Quoc Nguyen · Dinh Phung -
2020 : Quaternion Graph Neural Networks »
Dai Quoc Nguyen · Tu Dinh Nguyen · Dinh Phung -
2020 : A Metric for Linear Symmetry-Based Disentanglement »
Luis Armando Pérez Rey · Loek Tonnaer · Vlado Menkovski · Mike Holenderski · Jim Portegies -
2020 : Q/A for invited talk #4 »
Louis Kirsch -
2020 : General meta-learning »
Louis Kirsch -
2020 Workshop: 3rd Robot Learning Workshop »
Masha Itkina · Alex Bewley · Roberto Calandra · Igor Gilitschenski · Julien PEREZ · Ransalu Senanayake · Markus Wulfmeier · Vincent Vanhoucke -
2020 : Introduction »
Masha Itkina -
2020 Poster: Walsh-Hadamard Variational Inference for Bayesian Deep Learning »
Simone Rossi · Sebastien Marmin · Maurizio Filippone -
2020 Poster: Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding »
Gergely Flamich · Marton Havasi · José Miguel Hernández-Lobato -
2020 Poster: OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling »
Viet Huynh · He Zhao · Dinh Phung -
2020 Poster: Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations »
Sebastian Farquhar · Lewis Smith · Yarin Gal -
2020 Poster: Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness »
Jeremiah Liu · Zi Lin · Shreyas Padhy · Dustin Tran · Tania Bedrax Weiss · Balaji Lakshminarayanan -
2020 Poster: Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate »
Zhiyuan Li · Kaifeng Lyu · Sanjeev Arora -
2020 Poster: Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes »
Andrew Foong · Wessel Bruinsma · Jonathan Gordon · Yann Dubois · James Requeima · Richard Turner -
2020 Poster: Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality »
Yi Zhang · Orestis Plevrakis · Simon Du · Xingguo Li · Zhao Song · Sanjeev Arora -
2020 Poster: Belief Propagation Neural Networks »
Jonathan Kuck · Shuvam Chakraborty · Hao Tang · Rachel Luo · Jiaming Song · Ashish Sabharwal · Stefano Ermon -
2020 Poster: Probabilistic Active Meta-Learning »
Jean Kaddour · Steindor Saemundsson · Marc Deisenroth -
2020 Poster: Improving model calibration with accuracy versus uncertainty optimization »
Ranganath Krishnan · Omesh Tickoo -
2020 Poster: Explicit Regularisation in Gaussian Noise Injections »
Alexander Camuto · Matthew Willetts · Umut Simsekli · Stephen J Roberts · Chris C Holmes -
2020 Poster: Fair Performance Metric Elicitation »
Gaurush Hiranandani · Harikrishna Narasimhan · Sanmi Koyejo -
2020 Poster: Neural Power Units »
Niklas Heim · Tomas Pevny · Vasek Smidl -
2020 : Women at DeepMind: Applying for technical roles »
Feryal Behbahani · Mihaela Rosca · Kate Parkyn -
2020 Poster: Autoregressive Score Matching »
Chenlin Meng · Lantao Yu · Yang Song · Jiaming Song · Stefano Ermon -
2020 Poster: Deep Automodulators »
Ari Heljakka · Yuxin Hou · Juho Kannala · Arno Solin -
2020 Poster: Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot »
Jingtong Su · Yihang Chen · Tianle Cai · Tianhao Wu · Ruiqi Gao · Liwei Wang · Jason Lee -
2020 Poster: Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders »
Masha Itkina · Boris Ivanovic · Ransalu Senanayake · Mykel J Kochenderfer · Marco Pavone -
2020 Poster: Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs »
Jianzhun Du · Joseph Futoma · Finale Doshi-Velez -
2020 Poster: Multi-label Contrastive Predictive Coding »
Jiaming Song · Stefano Ermon -
2020 Poster: The Autoencoding Variational Autoencoder »
Taylan Cemgil · Sumedh Ghaisas · Krishnamurthy Dvijotham · Sven Gowal · Pushmeet Kohli -
2020 Spotlight: The Autoencoding Variational Autoencoder »
Taylan Cemgil · Sumedh Ghaisas · Krishnamurthy Dvijotham · Sven Gowal · Pushmeet Kohli -
2020 Oral: Multi-label Contrastive Predictive Coding »
Jiaming Song · Stefano Ermon -
2020 : QA Long Presentation II »
Matias Valdenegro-Toro · Gefersom Lima · Nicolas Araque · Matías Molina -
2020 : Unsupervised Difficulty Estimation »
Octavio Arriaga · Matias Valdenegro-Toro -
2019 : Poster Session 2 »
Mayur Saxena · Nicholas Frosst · Vivien Cabannes · Gene Kogan · Austin Dill · Anurag Sarkar · Joel Ruben Antony Moniz · Vibert Thio · Scott Sievert · Lia Coleman · Frederik De Bleser · Brian Quanz · Jonathon Kereliuk · Panos Achlioptas · Mohamed Elhoseiny · Songwei Ge · Aidan Gomez · Jamie Brew -
2019 : Poster Session »
Pravish Sainath · Mohamed Akrout · Charles Delahunt · Nathan Kutz · Guangyu Robert Yang · Joseph Marino · L F Abbott · Nicolas Vecoven · Damien Ernst · andrew warrington · Michael Kagan · Kyunghyun Cho · Kameron Harris · Leopold Grinberg · John J. Hopfield · Dmitry Krotov · Taliah Muhammad · Erick Cobos · Edgar Walker · Jacob Reimer · Andreas Tolias · Alexander Ecker · Janaki Sheth · Yu Zhang · Maciej Wołczyk · Jacek Tabor · Szymon Maszke · Roman Pogodin · Dane Corneil · Wulfram Gerstner · Baihan Lin · Guillermo Cecchi · Jenna M Reinen · Irina Rish · Guillaume Bellec · Darjan Salaj · Anand Subramoney · Wolfgang Maass · Yueqi Wang · Ari Pakman · Jin Hyung Lee · Liam Paninski · Bryan Tripp · Colin Graber · Alex Schwing · Luke Prince · Gabriel Ocker · Michael Buice · Benjamin Lansdell · Konrad Kording · Jack Lindsey · Terrence Sejnowski · Matthew Farrell · Eric Shea-Brown · Nicolas Farrugia · Victor Nepveu · Jiwoong Im · Kristin Branson · Brian Hu · Ramakrishnan Iyer · Stefan Mihalas · Sneha Aenugu · Hananel Hazan · Sihui Dai · Tan Nguyen · Doris Tsao · Richard Baraniuk · Anima Anandkumar · Hidenori Tanaka · Aran Nayebi · Stephen Baccus · Surya Ganguli · Dean Pospisil · Eilif Muller · Jeffrey S Cheng · Gaël Varoquaux · Kamalaker Dadi · Dimitrios C Gklezakos · Rajesh PN Rao · Anand Louis · Christos Papadimitriou · Santosh Vempala · Naganand Yadati · Daniel Zdeblick · Daniela M Witten · Nicholas Roberts · Vinay Prabhu · Pierre Bellec · Poornima Ramesh · Jakob H Macke · Santiago Cadena · Guillaume Bellec · Franz Scherr · Owen Marschall · Robert Kim · Hannes Rapp · Marcio Fonseca · Oliver Armitage · Jiwoong Im · Thomas Hardcastle · Abhishek Sharma · Wyeth Bair · Adrian Valente · Shane Shang · Merav Stern · Rutuja Patil · Peter Wang · Sruthi Gorantla · Peter Stratton · Tristan Edwards · Jialin Lu · Martin Ester · Yurii Vlasov · Siavash Golkar -
2019 : Coffee Break & Poster Session 2 »
Juho Lee · Yoonho Lee · Yee Whye Teh · Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing · Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Christian Bueno · Aditya Sanghi · Pradeep Kumar Jayaraman · Ignacio Arroyo-Fernández · Andrew Hryniowski · Vinayak Mathur · Sanjay Singh · Shahrzad Haddadan · Vasco Portilheiro · Luna Zhang · Mert Yuksekgonul · Jhosimar Arias Figueroa · Deepak Maurya · Balaraman Ravindran · Frank NIELSEN · Philip Pham · Justin Payan · Andrew McCallum · Jinesh Mehta · Ke SUN -
2019 : Conditional Flow Variational Autoencoders for Structured Sequence Prediction »
Apratim Bhattacharyya -
2019 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 : Coffee Break & Poster Session 1 »
Yan Zhang · Jonathon Hare · Adam Prugel-Bennett · Po Leung · Patrick Flaherty · Pitchaya Wiratchotisatian · Alessandro Epasto · Silvio Lattanzi · Sergei Vassilvitskii · Morteza Zadimoghaddam · Theja Tulabandhula · Fabian Fuchs · Adam Kosiorek · Ingmar Posner · William Hang · Anna Goldie · Sujith Ravi · Azalia Mirhoseini · Yuwen Xiong · Mengye Ren · Renjie Liao · Raquel Urtasun · Haici Zhang · Michele Borassi · Shengda Luo · Andrew Trapp · Geoffroy Dubourg-Felonneau · Yasmeen Kussad · Christopher Bender · Manzil Zaheer · Junier Oliva · Michał Stypułkowski · Maciej Zieba · Austin Dill · Chun-Liang Li · Songwei Ge · Eunsu Kang · Oiwi Parker Jones · Kelvin Ka Wing Wong · Joshua Payne · Yang Li · Azade Nazi · Erkut Erdem · Aykut Erdem · Kevin O'Connor · Juan J Garcia · Maciej Zamorski · Jan Chorowski · Deeksha Sinha · Harry Clifford · John W Cassidy -
2019 : Coffee + Posters »
Changhao Chen · Nils Gählert · Edouard Leurent · Johannes Lehner · Apratim Bhattacharyya · Harkirat Singh Behl · Teck Yian Lim · Shiho Kim · Jelena Novosel · Błażej Osiński · Arindam Das · Ruobing Shen · Jeffrey Hawke · Joachim Sicking · Babak Shahian Jahromi · Theja Tulabandhula · Claudio Michaelis · Evgenia Rusak · WENHANG BAO · Hazem Rashed · JP Chen · Amin Ansari · Jaekwang Cha · Mohamed Zahran · Daniele Reda · Jinhyuk Kim · Kim Dohyun · Ho Suk · Junekyo Jhung · Alexander Kister · Matthias Fahrland · Adam Jakubowski · Piotr Miłoś · Jean Mercat · Bruno Arsenali · Silviu Homoceanu · Xiao-Yang Liu · Philip Torr · Ahmad El Sallab · Ibrahim Sobh · Anurag Arnab · Krzysztof Galias -
2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 : Poster Session 2 »
Gabriele Prato · Urmish Thakker · Laura Galindez Olascoaga · Tianyu Zhang · Vahid Partovi Nia · Kamil Adamczewski -
2019 : Coffee/Poster session 2 »
Xingyou Song · Puneet Mangla · David Salinas · Zhenxun Zhuang · Leo Feng · Shell Xu Hu · Raul Puri · Wesley Maddox · Aniruddh Raghu · Prudencio Tossou · Mingzhang Yin · Ishita Dasgupta · Kangwook Lee · Ferran Alet · Zhen Xu · Jörg Franke · James Harrison · Jonathan Warrell · Guneet Dhillon · Arber Zela · Xin Qiu · Julien Niklas Siems · Russell Mendonca · Louis Schlessinger · Jeffrey Li · Georgiana Manolache · Debojyoti Dutta · Lucas Glass · Abhishek Singh · Gregor Koehler -
2019 : Posters »
Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner -
2019 : Outstanding Contribution Talk: Probabilistic End-to-End Graph-based Semi-Supervised Learning »
mariana vargas vieyra -
2019 : Poster Session I »
Shuangjia Zheng · Arnav Kapur · Umar Asif · Eyal Rozenberg · Cyprien Gilet · Oleksii Sidorov · Yogesh Kumar · Tom Van Steenkiste · William Boag · David Ouyang · Paul Jaeger · Sheng Liu · Aparna Balagopalan · Deepta Rajan · Marta Skreta · Nikhil Pattisapu · Jann Goschenhofer · Viraj Prabhu · Di Jin · Laura-Jayne Gardiner · Irene Li · sriram kumar · Qiyuan Hu · Mehul Motani · Justin Lovelace · Usman Roshan · Lucy Lu Wang · Ilya Valmianski · Hyeonwoo Lee · Sunil Mallya · Elias Chaibub Neto · Jonas Kemp · Marie Charpignon · Amber Nigam · Wei-Hung Weng · Sabri Boughorbel · Alexis Bellot · Lovedeep Gondara · Haoran Zhang · Taha Bahadori · John Zech · Rulin Shao · Edward Choi · Laleh Seyyed-Kalantari · Emily Aiken · Ioana Bica · Yiqiu Shen · Kieran Chin-Cheong · Subhrajit Roy · Ioana Baldini · So Yeon Min · Dirk Deschrijver · Pekka Marttinen · Damian Pascual Ortiz · Supriya Nagesh · Niklas Rindtorff · Andriy Mulyar · Katharina Hoebel · Martha Shaka · Pierre Machart · Leon Gatys · Nathan Ng · Matthias Hüser · Devin Taylor · Dennis Barbour · Natalia Martinez · Clara McCreery · Benjamin Eyre · Vivek Natarajan · Ren Yi · Ruibin Ma · Chirag Nagpal · Nan Du · Chufan Gao · Anup Tuladhar · Sam Shleifer · Jason Ren · Pouria Mashouri · Ming Yang Lu · Farideh Bagherzadeh-Khiabani · Olivia Choudhury · Maithra Raghu · Scott Fleming · Mika Jain · GUO YANG · Alena Harley · Stephen Pfohl · Elisabeth Rumetshofer · Alex Fedorov · Saloni Dash · Jacob Pfau · Sabina Tomkins · Colin Targonski · Michael Brudno · Xinyu Li · Yiyang Yu · Nisarg Patel -
2019 : Poster Session »
Ahana Ghosh · Javad Shafiee · Akhilan Boopathy · Alex Tamkin · Theodoros Vasiloudis · Vedant Nanda · Ali Baheri · Paul Fieguth · Andrew Bennett · Guanya Shi · Hao Liu · Arushi Jain · Jacob Tyo · Benjie Wang · Boxiao Chen · Carroll Wainwright · Chandramouli Shama Sastry · Chao Tang · Daniel S. Brown · David Inouye · David Venuto · Dhruv Ramani · Dimitrios Diochnos · Divyam Madaan · Dmitrii Krashenikov · Joel Oren · Doyup Lee · Eleanor Quint · elmira amirloo · Matteo Pirotta · Gavin Hartnett · Geoffroy Dubourg-Felonneau · Gokul Swamy · Pin-Yu Chen · Ilija Bogunovic · Jason Carter · Javier Garcia-Barcos · Jeet Mohapatra · Jesse Zhang · Jian Qian · John Martin · Oliver Richter · Federico Zaiter · Tsui-Wei Weng · Karthik Abinav Sankararaman · Kyriakos Polymenakos · Lan Hoang · mahdieh abbasi · Marco Gallieri · Mathieu Seurin · Matteo Papini · Matteo Turchetta · Matthew Sotoudeh · Mehrdad Hosseinzadeh · Nathan Fulton · Masatoshi Uehara · Niranjani Prasad · Oana-Maria Camburu · Patrik Kolaric · Philipp Renz · Prateek Jaiswal · Reazul Hasan Russel · Riashat Islam · Rishabh Agarwal · Alexander Aldrick · Sachin Vernekar · Sahin Lale · Sai Kiran Narayanaswami · Samuel Daulton · Sanjam Garg · Sebastian East · Shun Zhang · Soheil Dsidbari · Justin Goodwin · Victoria Krakovna · Wenhao Luo · Wesley Chung · Yuanyuan Shi · Yuh-Shyang Wang · Hongwei Jin · Ziping Xu -
2019 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2019 Workshop: Information Theory and Machine Learning »
Shengjia Zhao · Jiaming Song · Yanjun Han · Kristy Choi · Pratyusha Kalluri · Ben Poole · Alex Dimakis · Jiantao Jiao · Tsachy Weissman · Stefano Ermon -
2019 Poster: Training Language GANs from Scratch »
Cyprien de Masson d'Autume · Shakir Mohamed · Mihaela Rosca · Jack Rae -
2019 Poster: Bayesian Batch Active Learning as Sparse Subset Approximation »
Robert Pinsler · Jonathan Gordon · Eric Nalisnick · José Miguel Hernández-Lobato -
2019 Poster: Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets »
Rohith Kuditipudi · Xiang Wang · Holden Lee · Yi Zhang · Zhiyuan Li · Wei Hu · Rong Ge · Sanjeev Arora -
2019 Poster: Implicit Regularization in Deep Matrix Factorization »
Sanjeev Arora · Nadav Cohen · Wei Hu · Yuping Luo -
2019 Poster: Convergence of Adversarial Training in Overparametrized Neural Networks »
Ruiqi Gao · Tianle Cai · Haochuan Li · Cho-Jui Hsieh · Liwei Wang · Jason Lee -
2019 Spotlight: Convergence of Adversarial Training in Overparametrized Neural Networks »
Ruiqi Gao · Tianle Cai · Haochuan Li · Cho-Jui Hsieh · Liwei Wang · Jason Lee -
2019 Spotlight: Implicit Regularization in Deep Matrix Factorization »
Sanjeev Arora · Nadav Cohen · Wei Hu · Yuping Luo -
2019 Poster: Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting »
Aditya Grover · Jiaming Song · Ashish Kapoor · Kenneth Tran · Alekh Agarwal · Eric Horvitz · Stefano Ermon -
2019 Poster: Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes »
James Requeima · Jonathan Gordon · John Bronskill · Sebastian Nowozin · Richard Turner -
2019 Spotlight: Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes »
James Requeima · Jonathan Gordon · John Bronskill · Sebastian Nowozin · Richard Turner -
2019 Poster: Accurate Uncertainty Estimation and Decomposition in Ensemble Learning »
Jeremiah Liu · John Paisley · Marianthi-Anna Kioumourtzoglou · Brent Coull -
2019 Poster: Multiclass Performance Metric Elicitation »
Gaurush Hiranandani · Shant Boodaghians · Ruta Mehta · Sanmi Koyejo -
2019 Poster: Language as an Abstraction for Hierarchical Deep Reinforcement Learning »
YiDing Jiang · Shixiang (Shane) Gu · Kevin Murphy · Chelsea Finn -
2019 Poster: Unsupervised learning of object structure and dynamics from videos »
Matthias Minderer · Chen Sun · Ruben Villegas · Forrester Cole · Kevin Murphy · Honglak Lee -
2019 Poster: Accurate, reliable and fast robustness evaluation »
Wieland Brendel · Jonas Rauber · Matthias Kümmerer · Ivan Ustyuzhaninov · Matthias Bethge -
2019 Poster: Reward Constrained Interactive Recommendation with Natural Language Feedback »
Ruiyi Zhang · Tong Yu · Yilin Shen · Hongxia Jin · Changyou Chen -
2019 Poster: On Exact Computation with an Infinitely Wide Neural Net »
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang -
2019 Spotlight: On Exact Computation with an Infinitely Wide Neural Net »
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang -
2018 : TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer »
Sicong (Sheldon) Huang · Cem Anil · Xuchan Bao -
2018 : Poster Session I »
Aniruddh Raghu · Daniel Jarrett · Kathleen Lewis · Elias Chaibub Neto · Nicholas Mastronarde · Shazia Akbar · Chun-Hung Chao · Henghui Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adityanarayanan Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Tzu Ming Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang -
2018 : Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference »
Jonathan Gordon -
2018 : Plenary Talk 1 »
Sanjeev Arora -
2018 : Poster spotlight session. »
Abdullah Salama · Wei-Cheng Chang · Aidan Gomez · Raphael Tang · FUXUN YU · Zhendong Zhang · Yuxin Zhang · Ji Lin · Stephen Tiedemann · Kun Bai · Sivaramakrishnan Sankarapandian · Marton Havasi · Jack Turner · Hsin-Pai Cheng · Yue Wang · Xiaofan Xu · Ruizhou Ding · Haoji Hu · Mohammad Shafiee · Christopher Blake · Chieh-Chi Kao · Daniel Kang · Yew Ken Chia · Amir Ashouri · Sourya Basu · Simon Wiedemann · Thorsten Laude -
2018 : Poster Session »
Lorenzo Masoero · Tammo Rukat · Runjing Liu · Sayak Ray Chowdhury · Daniel Coelho de Castro · Claudia Wehrhahn · Feras Saad · Archit Verma · Kelvin Hsu · Irineo Cabreros · Sandhya Prabhakaran · Yiming Sun · Maxime Rischard · Linfeng Liu · Adam Farooq · Jeremiah Liu · Melanie F. Pradier · Diego Romeres · Neill Campbell · Kai Xu · Mehmet M Dundar · Tucker Keuter · Prashnna Gyawali · Eli Sennesh · Alessandro De Palma · Daniel Flam-Shepherd · Takatomi Kubo -
2018 : Poster Session 1 (note there are numerous missing names here, all papers appear in all poster sessions) »
Akhilesh Gotmare · Kenneth Holstein · Jan Brabec · Michal Uricar · Kaleigh Clary · Cynthia Rudin · Sam Witty · Andrew Ross · Shayne O'Brien · Babak Esmaeili · Jessica Forde · Massimo Caccia · Ali Emami · Scott Jordan · Bronwyn Woods · D. Sculley · Rebekah Overdorf · Nicolas Le Roux · Peter Henderson · Brandon Yang · Tzu-Yu Liu · David Jensen · Niccolo Dalmasso · Weitang Liu · Paul Marc TRICHELAIR · Jun Ki Lee · Akanksha Atrey · Matt Groh · Yotam Hechtlinger · Emma Tosch -
2018 : Posters 1 »
Wei Wei · Flavio Calmon · Travis Dick · Leilani Gilpin · Maroussia Lévesque · Malek Ben Salem · Michael Wang · Jack Fitzsimons · Dimitri Semenovich · Linda Gu · Nathaniel Fruchter -
2018 : Coffee Break and Poster Session I »
Pim de Haan · Bin Wang · Dequan Wang · Aadil Hayat · Ibrahim Sobh · Muhammad Asif Rana · Thibault Buhet · Nicholas Rhinehart · Arjun Sharma · Alex Bewley · Michael Kelly · Lionel Blondé · Ozgur S. Oguz · Vaibhav Viswanathan · Jeroen Vanbaar · Konrad Żołna · Negar Rostamzadeh · Rowan McAllister · Sanjay Thakur · Alexandros Kalousis · Chelsea Sidrane · Sujoy Paul · Daphne Chen · Michal Garmulewicz · Henryk Michalewski · Coline Devin · Hongyu Ren · Jiaming Song · Wen Sun · Hanzhang Hu · Wulong Liu · Emilie Wirbel -
2018 : Poster Session 1 »
Stefan Gadatsch · Danil Kuzin · Navneet Kumar · Patrick Dallaire · Tom Ryder · Remus-Petru Pop · Nathan Hunt · Adam Kortylewski · Sophie Burkhardt · Mahmoud Elnaggar · Dieterich Lawson · Yifeng Li · Jongha (Jon) Ryu · Juhan Bae · Micha Livne · Tim Pearce · Mariia Vladimirova · Jason Ramapuram · Jiaming Zeng · Xinyu Hu · Jiawei He · Danielle Maddix · Arunesh Mittal · Albert Shaw · Tuan Anh Le · Alexander Sagel · Lisha Chen · Victor Gallego · Mahdi Karami · Zihao Zhang · Tal Kachman · Noah Weber · Matt Benatan · Kumar K Sricharan · Vincent Cartillier · Ivan Ovinnikov · Buu Phan · Mahmoud Hossam · Liu Ziyin · Valerii Kharitonov · Eugene Golikov · Qiang Zhang · Jae Myung Kim · Sebastian Farquhar · Jishnu Mukhoti · Xu Hu · Gregory Gundersen · Lavanya Sita Tekumalla · Paris Perdikaris · Ershad Banijamali · Siddhartha Jain · Ge Liu · Martin Gottwald · Katy Blumer · Sukmin Yun · Ranganath Krishnan · Roman Novak · Yilun Du · Yu Gong · Beliz Gokkaya · Jessica Ai · Daniel Duckworth · Johannes von Oswald · Christian Henning · Louis-Philippe Morency · Ali Ghodsi · Mahesh Subedar · Jean-Pascal Pfister · Rémi Lebret · Chao Ma · Aleksander Wieczorek · Laurence Perreault Levasseur -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Poster: Implicit Reparameterization Gradients »
Mikhail Figurnov · Shakir Mohamed · Andriy Mnih -
2018 Poster: Modular Networks: Learning to Decompose Neural Computation »
Louis Kirsch · Julius Kunze · David Barber -
2018 Poster: Boosting Black Box Variational Inference »
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Ratsch -
2018 Poster: SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient »
Aaron Mishkin · Frederik Kunstner · Didrik Nielsen · Mark Schmidt · Mohammad Emtiyaz Khan -
2018 Poster: Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo »
Marton Havasi · José Miguel Hernández-Lobato · Juan J. Murillo-Fuentes -
2018 Poster: Human-in-the-Loop Interpretability Prior »
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez -
2018 Spotlight: Human-in-the-Loop Interpretability Prior »
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez -
2018 Spotlight: Implicit Reparameterization Gradients »
Mikhail Figurnov · Shakir Mohamed · Andriy Mnih -
2018 Spotlight: Boosting Black Box Variational Inference »
Francesco Locatello · Gideon Dresdner · Rajiv Khanna · Isabel Valera · Gunnar Ratsch -
2018 Poster: Distributed Weight Consolidation: A Brain Segmentation Case Study »
Patrick McClure · Charles Zheng · Jakub R Kaczmarzyk · John Rogers-Lee · Satra Ghosh · Dylan Nielson · Peter A Bandettini · Francisco Pereira -
2018 Poster: Multi-Agent Generative Adversarial Imitation Learning »
Jiaming Song · Hongyu Ren · Dorsa Sadigh · Stefano Ermon -
2018 Poster: Dirichlet belief networks for topic structure learning »
He Zhao · Lan Du · Wray Buntine · Mingyuan Zhou -
2018 Poster: Delta-encoder: an effective sample synthesis method for few-shot object recognition »
Eli Schwartz · Leonid Karlinsky · Joseph Shtok · Sivan Harary · Mattias Marder · Abhishek Kumar · Rogerio Feris · Raja Giryes · Alex Bronstein -
2018 Spotlight: Delta-encoder: an effective sample synthesis method for few-shot object recognition »
Eli Schwartz · Leonid Karlinsky · Joseph Shtok · Sivan Harary · Mattias Marder · Abhishek Kumar · Rogerio Feris · Raja Giryes · Alex Bronstein -
2018 Poster: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Spotlight: Bias and Generalization in Deep Generative Models: An Empirical Study »
Shengjia Zhao · Hongyu Ren · Arianna Yuan · Jiaming Song · Noah Goodman · Stefano Ermon -
2018 Poster: Bayesian Distributed Stochastic Gradient Descent »
Michael Teng · Frank Wood -
2018 Poster: Co-regularized Alignment for Unsupervised Domain Adaptation »
Abhishek Kumar · Prasanna Sattigeri · Kahini Wadhawan · Leonid Karlinsky · Rogerio Feris · Bill Freeman · Gregory Wornell -
2017 : Poster session »
Xun Zheng · Tim G. J. Rudner · Christopher Tegho · Patrick McClure · Yunhao Tang · ASHWIN D'CRUZ · Juan Camilo Gamboa Higuera · Chandra Sekhar Seelamantula · Jhosimar Arias Figueroa · Andrew Berlin · Maxime Voisin · Alexander Amini · Thang Long Doan · Hengyuan Hu · Aleksandar Botev · Niko Suenderhauf · CHI ZHANG · John Lambert -
2017 : Poster session + Coffee break »
Mikael Kågebäck · Igor Melnyk · Amir-Hossein Karimi · Gino Brunner · Ershad Banijamali · Chris Donahue · Jake Zhao · Giambattista Parascandolo · Valentin Thomas · Abhishek Kumar · Chris Burgess · Amanda Nilsson · Maria Larsson · Cian Eastwood · Momchil Peychev -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Workshop: Deep Learning: Bridging Theory and Practice »
Sanjeev Arora · Maithra Raghu · Russ Salakhutdinov · Ludwig Schmidt · Oriol Vinyals -
2017 : Poster Session »
Shunsuke Horii · Heejin Jeong · Tobias Schwedes · Qing He · Ben Calderhead · Ertunc Erdil · Jaan Altosaar · Patrick Muchmore · Rajiv Khanna · Ian Gemp · Pengfei Zhang · Yuan Zhou · Chris Cremer · Maria DeYoreo · Alexander Terenin · Brendan McVeigh · Rachit Singh · Yaodong Yang · Erik Bodin · Trefor Evans · Henry Chai · Shandian Zhe · Jeffrey Ling · Vincent ADAM · Lars Maaløe · Andrew Miller · Ari Pakman · Josip Djolonga · Hong Ge -
2017 : Poster spotlights »
Hiroshi Kuwajima · Masayuki Tanaka · Qingkai Liang · Matthieu Komorowski · Fanyu Que · Thalita F Drumond · Aniruddh Raghu · Leo Anthony Celi · Christina Göpfert · Andrew Ross · Sarah Tan · Rich Caruana · Yin Lou · Devinder Kumar · Graham Taylor · Forough Poursabzi-Sangdeh · Jennifer Wortman Vaughan · Hanna Wallach -
2017 : Coffee break and Poster Session I »
Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · Chen Fang · Sibt ul Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros -
2017 Poster: Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference »
Abhishek Kumar · Prasanna Sattigeri · Tom Fletcher -
2017 Poster: Attention is All you Need »
Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan Gomez · Łukasz Kaiser · Illia Polosukhin -
2017 Spotlight: Attention is All you Need »
Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan Gomez · Łukasz Kaiser · Illia Polosukhin -
2017 Poster: Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees »
Francesco Locatello · Michael Tschannen · Gunnar Ratsch · Martin Jaggi -
2017 Poster: A-NICE-MC: Adversarial Training for MCMC »
Jiaming Song · Shengjia Zhao · Stefano Ermon -
2017 Poster: Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes »
Jeremiah Liu · Brent Coull -
2017 Poster: The Reversible Residual Network: Backpropagation Without Storing Activations »
Aidan Gomez · Mengye Ren · Raquel Urtasun · Roger Grosse -
2017 Poster: InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations »
Yunzhu Li · Jiaming Song · Stefano Ermon -
2017 Poster: YASS: Yet Another Spike Sorter »
Jin Hyung Lee · David Carlson · Hooshmand Shokri Razaghi · Weichi Yao · Georges A Goetz · Espen Hagen · Eleanor Batty · E.J. Chichilnisky · Gaute T. Einevoll · Liam Paninski -
2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Poster: PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions »
Mikhail Figurnov · Aizhan Ibraimova · Dmitry Vetrov · Pushmeet Kohli -
2015 Poster: Bayesian dark knowledge »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling -
2015 Poster: Infinite Factorial Dynamical Model »
Isabel Valera · Francisco Ruiz · Lennart Svensson · Fernando Perez-Cruz -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2014 Workshop: Machine Learning for Clinical Data Analysis, Healthcare and Genomics »
Gunnar Rätsch · Madalina Fiterau · Julia Vogt -
2013 Poster: Summary Statistics for Partitionings and Feature Allocations »
Isik B Fidaner · Taylan Cemgil -
2012 Poster: Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression »
Piyush Rai · Abhishek Kumar · Hal Daumé III -
2012 Poster: Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders »
Sanjeev Arora · Rong Ge · Ankur Moitra · Sushant Sachdeva -
2012 Session: Oral Session 4 »
Gunnar Rätsch -
2011 Workshop: Machine Learning in Computational Biology »
Jean-Philippe Vert · Gunnar Rätsch · Yanjun Qi · Tomer Hertz · Anna Goldenberg · Christina Leslie -
2011 Poster: Co-regularized Multi-view Spectral Clustering »
Abhishek Kumar · Piyush Rai · Hal Daumé III -
2011 Poster: Generalised Coupled Tensor Factorisation »
Kenan Y Yılmaz · Taylan Cemgil · Umut Simsekli -
2011 Poster: Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation »
Nico Goernitz · Christian Widmer · Georg Zeller · Andre Kahles · Soeren Sonnenburg · Gunnar Rätsch -
2010 Workshop: Machine Learning in Computational Biology »
Gunnar Rätsch · Jean-Philippe Vert · Tomer Hertz · Yanjun Qi -
2010 Poster: Co-regularization Based Semi-supervised Domain Adaptation »
Hal Daumé III · Abhishek Kumar · Avishek Saha -
2008 Workshop: Machine Learning in Computational Biology »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch -
2008 Mini Symposium: Machine Learning in Computational Biology »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch -
2008 Poster: An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis »
Gabriele B Schweikert · Christian Widmer · Bernhard Schölkopf · Gunnar Rätsch -
2007 Workshop: Machine Learning in Computational Biology (Part 2) »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch · Koji Tsuda -
2007 Workshop: Machine Learning in Computational Biology (Part 1) »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch · Koji Tsuda -
2007 Spotlight: Boosting Algorithms for Maximizing the Soft Margin »
Manfred K. Warmuth · Karen Glocer · Gunnar Rätsch -
2007 Poster: Boosting Algorithms for Maximizing the Soft Margin »
Manfred K. Warmuth · Karen Glocer · Gunnar Rätsch -
2006 Workshop: New Problems and Methods in Computational Biology »
Gal Chechik · Quaid Morris · Koji Tsuda · Gunnar Rätsch · Christina Leslie · William S Noble -
2006 Poster: Large Scale Hidden Semi-Markov SVMs »
Gunnar Rätsch · Soeren Sonnenburg -
2006 Demonstration: SHOGUN Machine Learning Toolbox »
Soeren Sonnenburg · Gunnar Rätsch