Related Events (a corresponding poster, oral, or spotlight)

• 2007 Spotlight: Selecting Observations against Adversarial Objectives »
  Wed. Dec 5th 01:20 -- 01:30 AM Room

More from the Same Authors

• 2021 Spotlight: DiBS: Differentiable Bayesian Structure Learning »
  Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause
• 2021 : Towards Safe Global Optimality in Robot Learning with GoSafe »
  Bhavya Sukhija · Matteo Turchetta · Andreas Krause · Sebastian Trimpe · Dominik Baumann
• 2021 : DiBS: Differentiable Bayesian Structure Learning »
  Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause
• 2021 : Learning Single-Cell Perturbation Responses using Neural Optimal Transport »
  Charlotte Bunne · Stefan Stark · Gabriele Gut · Andreas Krause · Gunnar Rätsch · Lucas Pelkmans · Kjong Lehmann
• 2022 : Differentially Private Adaptive Optimization with Delayed Preconditioners »
  Tian Li · Manzil Zaheer · Ken Liu · Sashank Reddi · H. Brendan McMahan · Virginia Smith
• 2022 : Differentially Private Adaptive Optimization with Delayed Preconditioners »
  Tian Li · Manzil Zaheer · Ken Liu · Sashank Reddi · H. Brendan McMahan · Virginia Smith
• 2022 : Active Bayesian Causal Inference »
  Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen
• 2022 : Active Bayesian Causal inference »
  Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen
• 2022 : Amortized Inference for Causal Structure Learning »
  Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf
• 2022 : MARS: Meta-learning as score matching in the function space »
  Kruno Lehman · Jonas Rothfuss · Andreas Krause
• 2022 : Neural All-Pairs Shortest Path for Reinforcement Learning »
  Cristina Pinneri · Georg Martius · Andreas Krause
• 2023 Poster: (Amplified) Banded Matrix Factorization: A unified approach to private training »
  Christopher Choquette-Choo · Arun Ganesh · Ryan McKenna · H. Brendan McMahan · John Rush · Abhradeep Guha Thakurta · Zheng Xu
• 2023 Poster: Optimistic Active Exploration of Dynamical Systems »
  Bhavya · Lenart Treven · Cansu Sancaktar · Sebastian Blaes · Stelian Coros · Andreas Krause
• 2023 Poster: Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy »
  Anastasiia Koloskova · Ryan McKenna · Zachary Charles · John Rush · H. Brendan McMahan
• 2023 Poster: Riemannian stochastic optimization methods avoid strict saddle points »
  Ya-Ping Hsieh · Mohammad Reza Karimi Jaghargh · Andreas Krause · Panayotis Mertikopoulos
• 2023 Poster: Unleashing the Power of Randomization in Auditing Differentially Private ML »
  Krishna Pillutla · Galen Andrew · Peter Kairouz · H. Brendan McMahan · Alina Oprea · Sewoong Oh
• 2023 Poster: Learning To Dive In Branch And Bound »
  Max Paulus · Andreas Krause
• 2023 Poster: Implicit Manifold Gaussian Process Regression »
  Bernardo Fichera · Viacheslav Borovitskiy · Andreas Krause · Aude G Billard
• 2023 Poster: A Dynamical System View of Langevin-Based Non-Convex Sampling »
  Mohammad Reza Karimi Jaghargh · Ya-Ping Hsieh · Andreas Krause
• 2023 Poster: Anytime Model Selection in Linear Bandits »
  Parnian Kassraie · Aldo Pacchiano · Nicolas Emmenegger · Andreas Krause
• 2023 Poster: Stochastic Approximation Algorithms for Systems of Interacting Particles »
  Mohammad Reza Karimi Jaghargh · Ya-Ping Hsieh · Andreas Krause
• 2023 Poster: Efficient Exploration in Continuous-time Model-based Reinforcement Learning »
  Lenart Treven · Jonas Hübotter · Bhavya · Florian Dorfler · Andreas Krause
• 2023 Poster: Contextual Stochastic Bilevel Optimization »
  Yifan Hu · Jie Wang · Yao Xie · Andreas Krause · Daniel Kuhn
• 2023 Poster: Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning »
  Pier Giuseppe Sessa · Pierre Laforgue · Nicolò Cesa-Bianchi · Andreas Krause
• 2023 Poster: Likelihood Ratio Confidence Sets for Sequential Decision Making »
  Nicolas Emmenegger · Mojmir Mutny · Andreas Krause
• 2023 Workshop: Adaptive Experimental Design and Active Learning in the Real World »
  Willie Neiswanger · Mojmir Mutny · Ilija Bogunovic · Ava Soleimany · Zi Wang · Stefano Ermon · Andreas Krause
• 2022 Spotlight: Learning from a Sample in Online Algorithms »
  C.J. Argue · Alan Frieze · Anupam Gupta · Christopher Seiler
• 2022 Spotlight: Lightning Talks 1A-3 »
  Kimia Noorbakhsh · Ronan Perry · Qi Lyu · Jiawei Jiang · Christian Toth · Olivier Jeunen · Xin Liu · Yuan Cheng · Lei Li · Manuel Rodriguez · Julius von Kügelgen · Lars Lorch · Nicolas Donati · Lukas Burkhalter · Xiao Fu · Zhongdao Wang · Songtao Feng · Ciarán Gilligan-Lee · Rishabh Mehrotra · Fangcheng Fu · Jing Yang · Bernhard Schölkopf · Ya-Li Li · Christian Knoll · Maks Ovsjanikov · Andreas Krause · Shengjin Wang · Hong Zhang · Mounia Lalmas · Bolin Ding · Bo Du · Yingbin Liang · Franz Pernkopf · Robert Peharz · Anwar Hithnawi · Julius von Kügelgen · Bo Li · Ce Zhang
• 2022 Spotlight: Active Bayesian Causal Inference »
  Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen
• 2022 Poster: Supervised Training of Conditional Monge Maps »
  Charlotte Bunne · Andreas Krause · Marco Cuturi
• 2022 Poster: Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions »
  Anupam Gupta · Debmalya Panigrahi · Bernardo Subercaseaux · Kevin Sun
• 2022 Poster: Near-Optimal Multi-Agent Learning for Safe Coverage Control »
  Manish Prajapat · Matteo Turchetta · Melanie Zeilinger · Andreas Krause
• 2022 Poster: Amortized Inference for Causal Structure Learning »
  Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf
• 2022 Poster: Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams »
  Sergey Denisov · H. Brendan McMahan · John Rush · Adam Smith · Abhradeep Guha Thakurta
• 2022 Poster: Movement Penalized Bayesian Optimization with Application to Wind Energy Systems »
  Shyam Sundhar Ramesh · Pier Giuseppe Sessa · Andreas Krause · Ilija Bogunovic
• 2022 Poster: Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces »
  Mojmir Mutny · Andreas Krause
• 2022 Poster: Graph Neural Network Bandits »
  Parnian Kassraie · Andreas Krause · Ilija Bogunovic
• 2022 Poster: Learning from a Sample in Online Algorithms »
  C.J. Argue · Alan Frieze · Anupam Gupta · Christopher Seiler
• 2022 Poster: Active Bayesian Causal Inference »
  Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen
• 2022 Poster: A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits »
  Ilija Bogunovic · Zihan Li · Andreas Krause · Jonathan Scarlett
• 2022 Poster: Active Exploration for Inverse Reinforcement Learning »
  David Lindner · Andreas Krause · Giorgia Ramponi
• 2022 Poster: Learning Long-Term Crop Management Strategies with CyclesGym »
  Matteo Turchetta · Luca Corinzia · Scott Sussex · Amanda Burton · Juan Herrera · Ioannis Athanasiadis · Joachim M Buhmann · Andreas Krause
• 2021 : Meta-Learning Reliable Priors in the Function Space »
  Jonas Rothfuss · Dominique Heyn · jinfan Chen · Andreas Krause
• 2021 Poster: Learning Graph Models for Retrosynthesis Prediction »
  Vignesh Ram Somnath · Charlotte Bunne · Connor Coley · Andreas Krause · Regina Barzilay
• 2021 Poster: Risk-averse Heteroscedastic Bayesian Optimization »
  Anastasia Makarova · Ilnura Usmanova · Ilija Bogunovic · Andreas Krause
• 2021 Poster: Hierarchical Skills for Efficient Exploration »
  Jonas Gehring · Gabriel Synnaeve · Andreas Krause · Nicolas Usunier
• 2021 Poster: Multi-Scale Representation Learning on Proteins »
  Vignesh Ram Somnath · Charlotte Bunne · Andreas Krause
• 2021 Poster: Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems »
  Andreas Schlaginhaufen · Philippe Wenk · Andreas Krause · Florian Dorfler
• 2021 Poster: Information Directed Reward Learning for Reinforcement Learning »
  David Lindner · Matteo Turchetta · Sebastian Tschiatschek · Kamil Ciosek · Andreas Krause
• 2021 Poster: Robust Generalization despite Distribution Shift via Minimum Discriminating Information »
  Tobias Sutter · Andreas Krause · Daniel Kuhn
• 2021 Poster: Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning »
  Scott Sussex · Caroline Uhler · Andreas Krause
• 2021 Poster: Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models »
  Lenart Treven · Philippe Wenk · Florian Dorfler · Andreas Krause
• 2021 Poster: Meta-Learning Reliable Priors in the Function Space »
  Jonas Rothfuss · Dominique Heyn · jinfan Chen · Andreas Krause
• 2021 Poster: Misspecified Gaussian Process Bandit Optimization »
  Ilija Bogunovic · Andreas Krause
• 2021 Poster: DiBS: Differentiable Bayesian Structure Learning »
  Lars Lorch · Jonas Rothfuss · Bernhard Schölkopf · Andreas Krause
• 2021 Poster: Regret Bounds for Gaussian-Process Optimization in Large Domains »
  Manuel Wuethrich · Bernhard Schölkopf · Andreas Krause
• 2020 : Invited speaker: Adaptive Sampling for Stochastic Risk-Averse Learning, Andreas Krause »
  Andreas Krause
• 2020 Poster: Adaptive Sampling for Stochastic Risk-Averse Learning »
  Sebastian Curi · Kfir Y. Levy · Stefanie Jegelka · Andreas Krause
• 2020 Poster: Contextual Games: Multi-Agent Learning with Side Information »
  Pier Giuseppe Sessa · Ilija Bogunovic · Andreas Krause · Maryam Kamgarpour
• 2020 Poster: Coresets via Bilevel Optimization for Continual Learning and Streaming »
  Zalan Borsos · Mojmir Mutny · Andreas Krause
• 2020 Poster: Gradient Estimation with Stochastic Softmax Tricks »
  Max Paulus · Dami Choi · Danny Tarlow · Andreas Krause · Chris Maddison
• 2020 Oral: Gradient Estimation with Stochastic Softmax Tricks »
  Max Paulus · Dami Choi · Danny Tarlow · Andreas Krause · Chris Maddison
• 2020 Poster: Neutralizing Self-Selection Bias in Sampling for Sortition »
  Bailey Flanigan · Paul Gölz · Anupam Gupta · Ariel Procaccia
• 2020 Poster: Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning »
  Sebastian Curi · Felix Berkenkamp · Andreas Krause
• 2020 Poster: Learning to Play Sequential Games versus Unknown Opponents »
  Pier Giuseppe Sessa · Ilija Bogunovic · Maryam Kamgarpour · Andreas Krause
• 2020 Spotlight: Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning »
  Sebastian Curi · Felix Berkenkamp · Andreas Krause
• 2020 Poster: Safe Reinforcement Learning via Curriculum Induction »
  Matteo Turchetta · Andrey Kolobov · Shital Shah · Andreas Krause · Alekh Agarwal
• 2020 Spotlight: Safe Reinforcement Learning via Curriculum Induction »
  Matteo Turchetta · Andrey Kolobov · Shital Shah · Andreas Krause · Alekh Agarwal
• 2019 Poster: Efficiently Learning Fourier Sparse Set Functions »
  Andisheh Amrollahi · Amir Zandieh · Michael Kapralov · Andreas Krause
• 2019 Spotlight: Efficiently Learning Fourier Sparse Set Functions »
  Andisheh Amrollahi · Amir Zandieh · Michael Kapralov · Andreas Krause
• 2019 Poster: Stochastic Bandits with Context Distributions »
  Johannes Kirschner · Andreas Krause
• 2019 Poster: A Domain Agnostic Measure for Monitoring and Evaluating GANs »
  Paulina Grnarova · Kfir Y. Levy · Aurelien Lucchi · Nathanael Perraudin · Ian Goodfellow · Thomas Hofmann · Andreas Krause
• 2019 Poster: No-Regret Learning in Unknown Games with Correlated Payoffs »
  Pier Giuseppe Sessa · Ilija Bogunovic · Maryam Kamgarpour · Andreas Krause
• 2019 Poster: Teaching Multiple Concepts to a Forgetful Learner »
  Anette Hunziker · Yuxin Chen · Oisin Mac Aodha · Manuel Gomez Rodriguez · Andreas Krause · Pietro Perona · Yisong Yue · Adish Singla
• 2019 Poster: Adaptive Sequence Submodularity »
  Marko Mitrovic · Ehsan Kazemi · Moran Feldman · Andreas Krause · Amin Karbasi
• 2019 Poster: Safe Exploration for Interactive Machine Learning »
  Matteo Turchetta · Felix Berkenkamp · Andreas Krause
• 2018 Poster: Provable Variational Inference for Constrained Log-Submodular Models »
  Josip Djolonga · Stefanie Jegelka · Andreas Krause
• 2018 Poster: Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features »
  Mojmir Mutny · Andreas Krause
• 2018 Spotlight: Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features »
  Mojmir Mutny · Andreas Krause
• 2018 Poster: Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making »
  Hoda Heidari · Claudio Ferrari · Krishna Gummadi · Andreas Krause
• 2018 Poster: Learning to Optimize Tensor Programs »
  Tianqi Chen · Lianmin Zheng · Eddie Yan · Ziheng Jiang · Thierry Moreau · Luis Ceze · Carlos Guestrin · Arvind Krishnamurthy
• 2018 Spotlight: Learning to Optimize Tensor Programs »
  Tianqi Chen · Lianmin Zheng · Eddie Yan · Ziheng Jiang · Thierry Moreau · Luis Ceze · Carlos Guestrin · Arvind Krishnamurthy
• 2018 Poster: Training Deep Models Faster with Robust, Approximate Importance Sampling »
  Tyler Johnson · Carlos Guestrin
• 2017 : Invited talk: Towards Safe Bayesian Optimization »
  Andreas Krause
• 2017 Workshop: Discrete Structures in Machine Learning »
  Yaron Singer · Jeff A Bilmes · Andreas Krause · Stefanie Jegelka · Amin Karbasi
• 2017 Poster: Interactive Submodular Bandit »
  Lin Chen · Andreas Krause · Amin Karbasi
• 2017 Poster: Safe Model-based Reinforcement Learning with Stability Guarantees »
  Felix Berkenkamp · Matteo Turchetta · Angela Schoellig · Andreas Krause
• 2017 Poster: Differentiable Learning of Submodular Functions »
  Josip Djolonga · Andreas Krause
• 2017 Spotlight: Differentiable Learning of Submodular Functions »
  Josip Djolonga · Andreas Krause
• 2017 Poster: Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms »
  Yatao Bian · Kfir Levy · Andreas Krause · Joachim M Buhmann
• 2017 Poster: Stochastic Submodular Maximization: The Case of Coverage Functions »
  Mohammad Karimi · Mario Lucic · Hamed Hassani · Andreas Krause
• 2016 : Invited talk, Carlos Guestrin »
  Carlos Guestrin
• 2016 Poster: Variational Inference in Mixed Probabilistic Submodular Models »
  Josip Djolonga · Sebastian Tschiatschek · Andreas Krause
• 2016 Poster: Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation »
  Ilija Bogunovic · Jonathan Scarlett · Andreas Krause · Volkan Cevher
• 2016 Poster: Cooperative Graphical Models »
  Josip Djolonga · Stefanie Jegelka · Sebastian Tschiatschek · Andreas Krause
• 2016 Poster: Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization »
  Tyler Johnson · Carlos Guestrin
• 2016 Poster: Fast and Provably Good Seedings for k-Means »
  Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause
• 2016 Oral: Fast and Provably Good Seedings for k-Means »
  Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause
• 2016 Poster: Safe Exploration in Finite Markov Decision Processes with Gaussian Processes »
  Matteo Turchetta · Felix Berkenkamp · Andreas Krause
• 2015 : Safe Exploration for Bayesian Optimization »
  Andreas Krause
• 2015 Poster: Distributed Submodular Cover: Succinctly Summarizing Massive Data »
  Baharan Mirzasoleiman · Amin Karbasi · Ashwinkumar Badanidiyuru · Andreas Krause
• 2015 Poster: Sampling from Probabilistic Submodular Models »
  Alkis Gotovos · Hamed Hassani · Andreas Krause
• 2015 Spotlight: Distributed Submodular Cover: Succinctly Summarizing Massive Data »
  Baharan Mirzasoleiman · Amin Karbasi · Ashwinkumar Badanidiyuru · Andreas Krause
• 2015 Oral: Sampling from Probabilistic Submodular Models »
  Alkis Gotovos · Hamed Hassani · Andreas Krause
• 2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
  David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters
• 2014 Workshop: Discrete Optimization in Machine Learning »
  Jeffrey A Bilmes · Andreas Krause · Stefanie Jegelka · S Thomas McCormick · Sebastian Nowozin · Yaron Singer · Dhruv Batra · Volkan Cevher
• 2014 Poster: Divide-and-Conquer Learning by Anchoring a Conical Hull »
  Tianyi Zhou · Jeffrey A Bilmes · Carlos Guestrin
• 2014 Poster: Efficient Sampling for Learning Sparse Additive Models in High Dimensions »
  Hemant Tyagi · Bernd Gärtner · Andreas Krause
• 2014 Poster: From MAP to Marginals: Variational Inference in Bayesian Submodular Models »
  Josip Djolonga · Andreas Krause
• 2014 Poster: Efficient Partial Monitoring with Prior Information »
  Hastagiri P Vanchinathan · Gábor Bartók · Andreas Krause
• 2013 Workshop: Machine Learning for Sustainability »
  Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao
• 2013 Workshop: Bayesian Optimization in Theory and Practice »
  Matthew Hoffman · Jasper Snoek · Nando de Freitas · Michael A Osborne · Ryan Adams · Sebastien Bubeck · Philipp Hennig · Remi Munos · Andreas Krause
• 2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
  Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan
• 2013 Poster: High-Dimensional Gaussian Process Bandits »
  Josip Djolonga · Andreas Krause · Volkan Cevher
• 2013 Poster: Distributed Submodular Maximization: Identifying Representative Elements in Massive Data »
  Baharan Mirzasoleiman · Amin Karbasi · Rik Sarkar · Andreas Krause
• 2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
  Stefanie Jegelka · Andreas Krause · Jeffrey A Bilmes · Pradeep Ravikumar
• 2012 Demonstration: GraphLab: A Framework For Machine Learning in the Cloud »
  Yucheng Low · Haijie Gu · Carlos Guestrin
• 2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
  Andreas Krause · Pradeep Ravikumar · Stefanie S Jegelka · Jeffrey A Bilmes
• 2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
  Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu
• 2011 Oral: Scalable Training of Mixture Models via Coresets »
  Dan Feldman · Matthew Faulkner · Andreas Krause
• 2011 Poster: Scalable Training of Mixture Models via Coresets »
  Dan Feldman · Matthew Faulkner · Andreas Krause
• 2011 Poster: Contextual Gaussian Process Bandit Optimization »
  Andreas Krause · Cheng Soon Ong
• 2011 Poster: Crowdclustering »
  Ryan G Gomes · Peter Welinder · Andreas Krause · Pietro Perona
• 2011 Poster: Linear Submodular Bandits and their Application to Diversified Retrieval »
  Yisong Yue · Carlos Guestrin
• 2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
  Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes · Stefanie Jegelka
• 2010 Poster: Evidence-Specific Structures for Rich Tractable CRFs »
  Anton Chechetka · Carlos Guestrin
• 2010 Spotlight: Efficient Minimization of Decomposable Submodular Functions »
  Peter G Stobbe · Andreas Krause
• 2010 Poster: Discriminative Clustering by Regularized Information Maximization »
  Ryan G Gomes · Andreas Krause · Pietro Perona
• 2010 Poster: Efficient Minimization of Decomposable Submodular Functions »
  Peter G Stobbe · Andreas Krause
• 2010 Poster: Near-Optimal Bayesian Active Learning with Noisy Observations »
  Daniel Golovin · Andreas Krause · Debajyoti Ray
• 2010 Poster: Inference with Multivariate Heavy-Tails in Linear Models »
  Danny Bickson · Carlos Guestrin
• 2009 Workshop: Learning with Orderings »
  Tiberio Caetano · Carlos Guestrin · Jonathan Huang · Risi Kondor · Guy Lebanon · Marina Meila
• 2009 Workshop: Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity »
  Andreas Krause · Pradeep Ravikumar · Jeffrey A Bilmes
• 2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
  Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin
• 2009 Poster: Online Learning of Assignments »
  Matthew Streeter · Daniel Golovin · Andreas Krause
• 2009 Poster: Riffled Independence for Ranked Data »
  Jonathan Huang · Carlos Guestrin
• 2009 Spotlight: Riffled Independence for Ranked Data »
  Jonathan Huang · Carlos Guestrin
• 2009 Spotlight: Online Learning of Assignments »
  Matthew Streeter · Daniel Golovin · Andreas Krause
• 2007 Oral: Efficient Inference forDistributions on Permutations »
  Jonathan Huang · Carlos Guestrin · Leonidas Guibas
• 2007 Poster: Efficient Inference forDistributions on Permutations »
  Jonathan Huang · Carlos Guestrin · Leonidas Guibas
• 2007 Poster: Efficient Principled Learning of Thin Junction Trees »
  Anton Chechetka · Carlos Guestrin
• 2006 Poster: Distributed Inference in Dynamical Systems »
  Stanislav Funiak · Carlos Guestrin · Mark A Paskin · Rahul Sukthankar