Timezone: »
Whether in robotics, protein design, or physical sciences, one often faces decisions regarding which data to collect or which experiments to perform. There is thus a pressing need for algorithms and sampling strategies that make intelligent decisions about data collection processes that allow for data-efficient learning. Experimental design and active learning have been major research focuses within machine learning and statistics, aiming to answer both theoretical and algorithmic aspects of efficient data collection schemes. The goal of this workshop is to identify missing links that hinder the direct application of these principled research ideas into practically relevant solutions.
Author Information
Mojmir Mutny (ETH Zurich)
Andreas Krause (ETH Zurich)
Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. He also serves as Academic Co-Director of the Swiss Data Science Center. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received ERC Starting Investigator and ERC Consolidator grants, the Deutscher Mustererkennungspreis, an NSF CAREER award, the Okawa Foundation Research Grant recognizing top young researchers in telecommunications as well as the ETH Golden Owl teaching award. His research on machine learning and adaptive systems has received awards at several premier conferences and journals, including the ACM SIGKDD Test of Time award 2019 and the ICML Test of Time award 2020. Andreas Krause served as Program Co-Chair for ICML 2018, and is regularly serving as Area Chair or Senior Program Committee member for ICML, NeurIPS, AAAI and IJCAI, and as Action Editor for the Journal of Machine Learning Research.
Stefano Ermon (Stanford University)
Yisong Yue (Caltech)
Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for interactive machine learning and structured prediction. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive planning & allocation problems.
Ilija Bogunovic (University College London (UCL))
Willie Neiswanger (Stanford University)
More from the Same Authors
-
2021 : Synthetic Benchmarks for Scientific Research in Explainable Machine Learning »
· Yang Liu · Colin White · Willie Neiswanger -
2022 : Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges »
Ya-Ping Hsieh · Charlotte Bunne · Marco Cuturi · Andreas Krause -
2022 : Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges »
Ya-Ping Hsieh · Charlotte Bunne · Marco Cuturi · Andreas Krause -
2022 : Transform Once: Efficient Operator Learning in Frequency Domain »
Michael Poli · Stefano Massaroli · Federico Berto · Jinkyoo Park · Tri Dao · Christopher Re · Stefano Ermon -
2022 : Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges »
Charlotte Bunne · Ya-Ping Hsieh · Marco Cuturi · Andreas Krause -
2022 : FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness »
Tri Dao · Dan Fu · Stefano Ermon · Atri Rudra · Christopher Re -
2022 : Score-based generative models »
Stefano Ermon -
2022 Poster: Imitation Learning by Estimating Expertise of Demonstrators »
Mark Beliaev · Andy Shih · Stefano Ermon · Dorsa Sadigh · Ramtin Pedarsani -
2022 Poster: Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning »
Max Paulus · Giulia Zarpellon · Andreas Krause · Laurent Charlin · Chris Maddison -
2022 Poster: Investigating Generalization by Controlling Normalized Margin »
Alexander Farhang · Jeremy Bernstein · Kushal Tirumala · Yang Liu · Yisong Yue -
2022 Poster: Meta-Learning Hypothesis Spaces for Sequential Decision-making »
Parnian Kassraie · Jonas Rothfuss · Andreas Krause -
2022 Spotlight: Meta-Learning Hypothesis Spaces for Sequential Decision-making »
Parnian Kassraie · Jonas Rothfuss · Andreas Krause -
2022 Spotlight: Imitation Learning by Estimating Expertise of Demonstrators »
Mark Beliaev · Andy Shih · Stefano Ermon · Dorsa Sadigh · Ramtin Pedarsani -
2022 Spotlight: Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning »
Max Paulus · Giulia Zarpellon · Andreas Krause · Laurent Charlin · Chris Maddison -
2022 Spotlight: Investigating Generalization by Controlling Normalized Margin »
Alexander Farhang · Jeremy Bernstein · Kushal Tirumala · Yang Liu · Yisong Yue -
2022 Poster: A General Recipe for Likelihood-free Bayesian Optimization »
Jiaming Song · Lantao Yu · Willie Neiswanger · Stefano Ermon -
2022 Poster: Interactively Learning Preference Constraints in Linear Bandits »
David Lindner · Sebastian Tschiatschek · Katja Hofmann · Andreas Krause -
2022 Oral: A General Recipe for Likelihood-free Bayesian Optimization »
Jiaming Song · Lantao Yu · Willie Neiswanger · Stefano Ermon -
2022 Spotlight: Interactively Learning Preference Constraints in Linear Bandits »
David Lindner · Sebastian Tschiatschek · Katja Hofmann · Andreas Krause -
2022 Poster: ButterflyFlow: Building Invertible Layers with Butterfly Matrices »
Chenlin Meng · Linqi Zhou · Kristy Choi · Tri Dao · Stefano Ermon -
2022 Poster: Bit Prioritization in Variational Autoencoders via Progressive Coding »
Rui Shu · Stefano Ermon -
2022 Poster: LyaNet: A Lyapunov Framework for Training Neural ODEs »
Ivan Dario Jimenez Rodriguez · Aaron Ames · Yisong Yue -
2022 Poster: Adaptive Gaussian Process Change Point Detection »
Edoardo Caldarelli · Philippe Wenk · Stefan Bauer · Andreas Krause -
2022 Poster: Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation »
Pier Giuseppe Sessa · Maryam Kamgarpour · Andreas Krause -
2022 Poster: Modular Conformal Calibration »
Shengjia Zhao · Charles Marx · Willie Neiswanger · Stefano Ermon -
2022 Spotlight: Modular Conformal Calibration »
Shengjia Zhao · Charles Marx · Willie Neiswanger · Stefano Ermon -
2022 Spotlight: Bit Prioritization in Variational Autoencoders via Progressive Coding »
Rui Shu · Stefano Ermon -
2022 Spotlight: Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation »
Pier Giuseppe Sessa · Maryam Kamgarpour · Andreas Krause -
2022 Spotlight: Adaptive Gaussian Process Change Point Detection »
Edoardo Caldarelli · Philippe Wenk · Stefan Bauer · Andreas Krause -
2022 Spotlight: LyaNet: A Lyapunov Framework for Training Neural ODEs »
Ivan Dario Jimenez Rodriguez · Aaron Ames · Yisong Yue -
2022 Spotlight: ButterflyFlow: Building Invertible Layers with Butterfly Matrices »
Chenlin Meng · Linqi Zhou · Kristy Choi · Tri Dao · Stefano Ermon -
2021 : Data Summarization via Bilevel Coresets »
Andreas Krause -
2021 : Personalized Preference Learning - from Spinal Cord Stimulation to Exoskeletons »
Yisong Yue -
2021 : Invited Talk 5 (Stefano Ermon): Maximum Likelihood Training of Score-Based Diffusion Models »
Stefano Ermon -
2021 Poster: Temporal Predictive Coding For Model-Based Planning In Latent Space »
Tung Nguyen · Rui Shu · Tuan Pham · Hung Bui · Stefano Ermon -
2021 Poster: PopSkipJump: Decision-Based Attack for Probabilistic Classifiers »
Carl-Johann Simon-Gabriel · Noman Ahmed Sheikh · Andreas Krause -
2021 Spotlight: Temporal Predictive Coding For Model-Based Planning In Latent Space »
Tung Nguyen · Rui Shu · Tuan Pham · Hung Bui · Stefano Ermon -
2021 Spotlight: PopSkipJump: Decision-Based Attack for Probabilistic Classifiers »
Carl-Johann Simon-Gabriel · Noman Ahmed Sheikh · Andreas Krause -
2021 Poster: PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees »
Jonas Rothfuss · Vincent Fortuin · Martin Josifoski · Andreas Krause -
2021 Poster: Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information »
Willie Neiswanger · Ke Alexander Wang · Stefano Ermon -
2021 Spotlight: Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information »
Willie Neiswanger · Ke Alexander Wang · Stefano Ermon -
2021 Spotlight: PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees »
Jonas Rothfuss · Vincent Fortuin · Martin Josifoski · Andreas Krause -
2021 Poster: Learning by Turning: Neural Architecture Aware Optimisation »
Yang Liu · Jeremy Bernstein · Markus Meister · Yisong Yue -
2021 Poster: Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems »
Pier Giuseppe Sessa · Ilija Bogunovic · Andreas Krause · Maryam Kamgarpour -
2021 Poster: Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving »
Yang Song · Chenlin Meng · Renjie Liao · Stefano Ermon -
2021 Spotlight: Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving »
Yang Song · Chenlin Meng · Renjie Liao · Stefano Ermon -
2021 Spotlight: Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems »
Pier Giuseppe Sessa · Ilija Bogunovic · Andreas Krause · Maryam Kamgarpour -
2021 Spotlight: Learning by Turning: Neural Architecture Aware Optimisation »
Yang Liu · Jeremy Bernstein · Markus Meister · Yisong Yue -
2021 Poster: No-regret Algorithms for Capturing Events in Poisson Point Processes »
Mojmir Mutny · Andreas Krause -
2021 Poster: Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning »
Sebastian Curi · Ilija Bogunovic · Andreas Krause -
2021 Poster: Reward Identification in Inverse Reinforcement Learning »
Kuno Kim · Shivam Garg · Kirankumar Shiragur · Stefano Ermon -
2021 Spotlight: No-regret Algorithms for Capturing Events in Poisson Point Processes »
Mojmir Mutny · Andreas Krause -
2021 Spotlight: Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning »
Sebastian Curi · Ilija Bogunovic · Andreas Krause -
2021 Spotlight: Reward Identification in Inverse Reinforcement Learning »
Kuno Kim · Shivam Garg · Kirankumar Shiragur · Stefano Ermon -
2021 Poster: Bias-Robust Bayesian Optimization via Dueling Bandits »
Johannes Kirschner · Andreas Krause -
2021 Poster: Fast Projection Onto Convex Smooth Constraints »
Ilnura Usmanova · Maryam Kamgarpour · Andreas Krause · Kfir Levy -
2021 Spotlight: Fast Projection Onto Convex Smooth Constraints »
Ilnura Usmanova · Maryam Kamgarpour · Andreas Krause · Kfir Levy -
2021 Spotlight: Bias-Robust Bayesian Optimization via Dueling Bandits »
Johannes Kirschner · Andreas Krause -
2020 : Constrained Maximization of Lattice Submodular Functions »
Aytunc Sahin · Joachim Buhmann · Andreas Krause -
2020 Workshop: Real World Experiment Design and Active Learning »
Ilija Bogunovic · Willie Neiswanger · Yisong Yue -
2020 Poster: From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models »
Aytunc Sahin · Yatao Bian · Joachim Buhmann · Andreas Krause -
2020 Poster: Predictive Coding for Locally-Linear Control »
Rui Shu · Tung Nguyen · Yinlam Chow · Tuan Pham · Khoat Than · Mohammad Ghavamzadeh · Stefano Ermon · Hung Bui -
2020 Poster: Bridging the Gap Between f-GANs and Wasserstein GANs »
Jiaming Song · Stefano Ermon -
2020 Poster: Learning Calibratable Policies using Programmatic Style-Consistency »
Eric Zhan · Albert Tseng · Yisong Yue · Adith Swaminathan · Matthew Hausknecht -
2020 Poster: Individual Calibration with Randomized Forecasting »
Shengjia Zhao · Tengyu Ma · Stefano Ermon -
2020 Poster: Domain Adaptive Imitation Learning »
Kuno Kim · Yihong Gu · Jiaming Song · Shengjia Zhao · Stefano Ermon -
2020 Poster: Training Deep Energy-Based Models with f-Divergence Minimization »
Lantao Yu · Yang Song · Jiaming Song · Stefano Ermon -
2020 Poster: Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis »
Jung Yeon Park · Kenneth Carr · Stephan Zheng · Yisong Yue · Rose Yu -
2020 Poster: Fair Generative Modeling via Weak Supervision »
Kristy Choi · Aditya Grover · Trisha Singh · Rui Shu · Stefano Ermon -
2020 Test Of Time: Test of Time: Gaussian Process Optimization in the Bandit Settings: No Regret and Experimental Design »
Niranjan Srinivas · Andreas Krause · Sham Kakade · Matthias Seeger -
2019 Workshop: Real-world Sequential Decision Making: Reinforcement Learning and Beyond »
Hoang Le · Yisong Yue · Adith Swaminathan · Byron Boots · Ching-An Cheng -
2019 Poster: Batch Policy Learning under Constraints »
Hoang Le · Cameron Voloshin · Yisong Yue -
2019 Poster: Online Variance Reduction with Mixtures »
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause -
2019 Poster: Calibrated Model-Based Deep Reinforcement Learning »
Ali Malik · Volodymyr Kuleshov · Jiaming Song · Danny Nemer · Harlan Seymour · Stefano Ermon -
2019 Poster: Graphite: Iterative Generative Modeling of Graphs »
Aditya Grover · Aaron Zweig · Stefano Ermon -
2019 Poster: Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces »
Johannes Kirschner · Mojmir Mutny · Nicole Hiller · Rasmus Ischebeck · Andreas Krause -
2019 Poster: Adaptive Antithetic Sampling for Variance Reduction »
Hongyu Ren · Shengjia Zhao · Stefano Ermon -
2019 Oral: Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces »
Johannes Kirschner · Mojmir Mutny · Nicole Hiller · Rasmus Ischebeck · Andreas Krause -
2019 Oral: Online Variance Reduction with Mixtures »
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause -
2019 Oral: Adaptive Antithetic Sampling for Variance Reduction »
Hongyu Ren · Shengjia Zhao · Stefano Ermon -
2019 Oral: Graphite: Iterative Generative Modeling of Graphs »
Aditya Grover · Aaron Zweig · Stefano Ermon -
2019 Oral: Calibrated Model-Based Deep Reinforcement Learning »
Ali Malik · Volodymyr Kuleshov · Jiaming Song · Danny Nemer · Harlan Seymour · Stefano Ermon -
2019 Oral: Batch Policy Learning under Constraints »
Hoang Le · Cameron Voloshin · Yisong Yue -
2019 Poster: Control Regularization for Reduced Variance Reinforcement Learning »
Richard Cheng · Abhinav Verma · Gabor Orosz · Swarat Chaudhuri · Yisong Yue · Joel Burdick -
2019 Poster: Learning Generative Models across Incomparable Spaces »
Charlotte Bunne · David Alvarez-Melis · Andreas Krause · Stefanie Jegelka -
2019 Poster: AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs »
Gabriele Abbati · Philippe Wenk · Michael A Osborne · Andreas Krause · Bernhard Schölkopf · Stefan Bauer -
2019 Oral: Learning Generative Models across Incomparable Spaces »
Charlotte Bunne · David Alvarez-Melis · Andreas Krause · Stefanie Jegelka -
2019 Oral: Control Regularization for Reduced Variance Reinforcement Learning »
Richard Cheng · Abhinav Verma · Gabor Orosz · Swarat Chaudhuri · Yisong Yue · Joel Burdick -
2019 Oral: AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs »
Gabriele Abbati · Philippe Wenk · Michael A Osborne · Andreas Krause · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: Multi-Agent Adversarial Inverse Reinforcement Learning »
Lantao Yu · Jiaming Song · Stefano Ermon -
2019 Poster: Neural Joint Source-Channel Coding »
Kristy Choi · Kedar Tatwawadi · Aditya Grover · Tsachy Weissman · Stefano Ermon -
2019 Poster: Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference »
Yatao Bian · Joachim Buhmann · Andreas Krause -
2019 Oral: Neural Joint Source-Channel Coding »
Kristy Choi · Kedar Tatwawadi · Aditya Grover · Tsachy Weissman · Stefano Ermon -
2019 Oral: Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference »
Yatao Bian · Joachim Buhmann · Andreas Krause -
2019 Oral: Multi-Agent Adversarial Inverse Reinforcement Learning »
Lantao Yu · Jiaming Song · Stefano Ermon -
2018 Poster: Iterative Amortized Inference »
Joe Marino · Yisong Yue · Stephan Mandt -
2018 Poster: Hierarchical Imitation and Reinforcement Learning »
Hoang Le · Nan Jiang · Alekh Agarwal · Miro Dudik · Yisong Yue · Hal Daumé III -
2018 Poster: Modeling Sparse Deviations for Compressed Sensing using Generative Models »
Manik Dhar · Aditya Grover · Stefano Ermon -
2018 Oral: Modeling Sparse Deviations for Compressed Sensing using Generative Models »
Manik Dhar · Aditya Grover · Stefano Ermon -
2018 Oral: Iterative Amortized Inference »
Joe Marino · Yisong Yue · Stephan Mandt -
2018 Oral: Hierarchical Imitation and Reinforcement Learning »
Hoang Le · Nan Jiang · Alekh Agarwal · Miro Dudik · Yisong Yue · Hal Daumé III -
2018 Poster: Accelerating Natural Gradient with Higher-Order Invariance »
Yang Song · Jiaming Song · Stefano Ermon -
2018 Poster: Accurate Uncertainties for Deep Learning Using Calibrated Regression »
Volodymyr Kuleshov · Nathan Fenner · Stefano Ermon -
2018 Oral: Accelerating Natural Gradient with Higher-Order Invariance »
Yang Song · Jiaming Song · Stefano Ermon -
2018 Oral: Accurate Uncertainties for Deep Learning Using Calibrated Regression »
Volodymyr Kuleshov · Nathan Fenner · Stefano Ermon -
2018 Poster: Stagewise Safe Bayesian Optimization with Gaussian Processes »
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue -
2018 Oral: Stagewise Safe Bayesian Optimization with Gaussian Processes »
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue -
2018 Tutorial: Imitation Learning »
Yisong Yue · Hoang Le -
2017 Poster: Guarantees for Greedy Maximization of Non-submodular Functions with Applications »
Yatao Bian · Joachim Buhmann · Andreas Krause · Sebastian Tschiatschek -
2017 Poster: Differentially Private Submodular Maximization: Data Summarization in Disguise »
Marko Mitrovic · Mark Bun · Andreas Krause · Amin Karbasi -
2017 Poster: Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" »
Baharan Mirzasoleiman · Amin Karbasi · Andreas Krause -
2017 Poster: Probabilistic Submodular Maximization in Sub-Linear Time »
Serban A Stan · Morteza Zadimoghaddam · Andreas Krause · Amin Karbasi -
2017 Talk: Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" »
Baharan Mirzasoleiman · Amin Karbasi · Andreas Krause -
2017 Talk: Probabilistic Submodular Maximization in Sub-Linear Time »
Serban A Stan · Morteza Zadimoghaddam · Andreas Krause · Amin Karbasi -
2017 Talk: Guarantees for Greedy Maximization of Non-submodular Functions with Applications »
Yatao Bian · Joachim Buhmann · Andreas Krause · Sebastian Tschiatschek -
2017 Talk: Differentially Private Submodular Maximization: Data Summarization in Disguise »
Marko Mitrovic · Mark Bun · Andreas Krause · Amin Karbasi -
2017 Poster: Distributed and Provably Good Seedings for k-Means in Constant Rounds »
Olivier Bachem · Mario Lucic · Andreas Krause -
2017 Poster: Uniform Deviation Bounds for k-Means Clustering »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Poster: Coordinated Multi-Agent Imitation Learning »
Hoang Le · Yisong Yue · Peter Carr · Patrick Lucey -
2017 Talk: Coordinated Multi-Agent Imitation Learning »
Hoang Le · Yisong Yue · Peter Carr · Patrick Lucey -
2017 Talk: Uniform Deviation Bounds for k-Means Clustering »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Talk: Distributed and Provably Good Seedings for k-Means in Constant Rounds »
Olivier Bachem · Mario Lucic · Andreas Krause -
2017 Poster: Learning Hierarchical Features from Deep Generative Models »
Shengjia Zhao · Jiaming Song · Stefano Ermon -
2017 Talk: Learning Hierarchical Features from Deep Generative Models »
Shengjia Zhao · Jiaming Song · Stefano Ermon