Times are in Central European Time. Note: difference with US Eastern time is 5 hours during the conference.
Tuesday, March 16, 2021 | |
| 17:00-17:10 | Welcome notes, Room A (Katrina Ligett and Vitaly Feldman) |
| 17:10-18:10 | Session 1, Room A (Chair: Vianney Perchet) |
| Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization Jacob D Abernethy, Kevin A Lai, Andre Wibisono | |
| Last Round Convergence and No-Dynamic Regret in Asymmetric Repeated Games Le Cong Dinh, Tri-Dung Nguyen, Alain Zemkoho, Long Tran-Thanh | |
| Efficient Algorithms for Stochastic Repeated Second-price Auctions Juliette Achddou, Olivier Cappé, Aurélien Garivier | |
| Online Learning of Facility Locations Stephen U Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster | |
| 18:20-19:20 | Session 2: Award talks, Room A (Chair: Katrina Ligett) |
| Near-tight closure bounds for the Littlestone and threshold dimensions Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi | |
| Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions Gellert Weisz, Philip Amortila, Csaba Szepesvari | |
| Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound Steve Hanneke, Aryeh Kontorovich | |
| 19:40-20:40 | Session 3, Room A (Chair: Naman Agarwal) |
| Contrastive learning, multi-view redundancy, and linear models Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu | |
| Efficient Learning with Arbitrary Covariate Shift Adam Tauman Kalai, Varun Kanade | |
| No-substitution k-means Clustering with Adversarial Order Robi Bhattacharjee, Michal Moshkovitz | |
| Unexpected Effects of Online no-Substitution k-means Clustering Michal Moshkovitz | |
| 20:50-21:35 | Session 4, Room A (Chair: Phil Long) |
| Characterizing the implicit bias via a primal-dual analysis Ziwei Ji, Matus Telgarsky | |
| A Deep Conditioning Treatment of Neural Networks Naman Agarwal, Pranjal Awasthi, Satyen Kale | |
| A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer Manfred K. Warmuth, Wojciech Kotlowski, Ehsan Amid | |
| 21:45-22:45 | Women in ML Social Event (registration required) |
Wednesday, March 17, 2021 | |
| 17:00-18:00 | Session 1, Room A (Chair: Pranjal Awasthi) |
| Uncertainty quantification using martingales for misspecified Gaussian processes Willie Neiswanger, Aaditya Ramdas | |
| Precise Minimax Regret for Logistic Regression with Categorical Feature Values Philippe Jacquet, Gil I Shamir, Wojciech Szpankowski | |
| Submodular Combinatorial Information Measures with Applications in Machine Learning Rishabh Iyer, Ninad A Khargonkar, Jeffrey A Bilmes, Himanshu Asnani | |
| Intervention Efficient Algorithms for Approximate Learning of Causal Graphs Raghavendra Addanki, Andrew McGregor, Cameron Musco | |
| 18:10-19:10 | Keynote: Safe and Sound Reinforcement Learning Joelle Pineau, (Seminar Room) |
| 19:30-20:30 | Session 3, Room A (Chair: Nicolò Cesa-Bianchi) |
| Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time Thibaut Cuvelier, Richard Combes, Eric Gourdin | |
| Stochastic Dueling Bandits with Adversarial Corruption Arpit Agarwal, Shivani Agarwal, Prathamesh Patil | |
| Non-uniform Consistency of Online Learning with Random Sampling Changlong Wu, Narayana Santhanam | |
| An Efficient Algorithm for Cooperative Semi-Bandits Riccardo Della Vecchia, Tommaso R. Cesari | |
| 20:40-21:25 | Session 4, Room A (Chair: Aurélien Garivier) |
| Adaptive Reward-Free Exploration Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko | |
| Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric | |
| Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited Omar Darwiche Domingues, Pierre Menard, Emilie Kaufmann, Michal Valko | |
| 21:30-22:30 | Business meeting, (Seminar Room) |
Thursday, March 18, 2021 | |
| 17:00-18:15 | Session 1, Room A (Chair: Gautam Kamath) |
| Testing Product Distributions: A Closer Look Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, N. V. Vinodchandran | |
| Learning and Testing Irreducible Markov Chains via the k-Cover Time Siu On Chan, Qinghua Ding, Sing Hei Li | |
| Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance Jie Shen, Chicheng Zhang | |
| Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun | |
| Learning a mixture of two subspaces over finite fields Aidao Chen, Anindya De, Aravindan Vijayaraghavan | |
| 18:30-20:00 | Tutorial: What Is The Sample Complexity of Differentially Private Learning? Shay Moran (Seminar Room) |
| 20:15-21:30 | Session 3, Room A (Chair: Shay Moran) |
| Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data Di Wang, Huanyu Zhang, Marco Gaboradi, Jinhui Xu | |
| On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath | |
| Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics Mark B Cesar, Ryan Rogers | |
| Differentially Private Assouad, Fano, and Le Cam Jayadev Acharya, Ziteng Sun, Huanyu Zhang | |
| Descent-to-Delete: Gradient-Based Methods for Machine Unlearning Seth Neel, Aaron Roth, Saeed Sharifi -Malvajerdi | |
| 21:30-22:30 | Social hour (Lounge) |
Friday, March 19, 2021 | |
| 17:00-18:00 | Keynote: Equilibrium Computation and the Foundations of Deep Learning Constantinos Daskalakis (Seminar Room) |
| 18:10-19:25 | Session 2, Room A (Chair: Claire Vernade) |
| Self-Tuning Bandits over Unknown Covariate-Shifts Joseph Suk, Samory Kpotufe | |
| Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback Marc Jourdan, Mojmir Mutny, Johannes Kirschner, Andreas Krause | |
| Stochastic Top-$K$ Subset Bandits with Linear Space and Non-Linear Feedback Mridul Agarwal, Vaneet Aggarwal, Chris Quinn, Abhishek Kumar Umrawal | |
| Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe | |
| Sequential prediction under log-loss with side information Alankrita Bhatt, Young-Han Kim | |
| 19:45-21:00 | Session 3, Room A (Chair: Pranjal Awasthi) |
| Online Boosting with Bandit Feedback Nataly Brukhim, Elad Hazan | |
| Learning with Comparison Feedback: Online Estimation of Sample Statistics Michela Meister, Sloan Nietert | |
| Statistical guarantees for generative models without domination Nicolas Schreuder, Arnak Dalalyan, Victor-Emmanuel Brunel | |
| Efficient sampling from the Bingham distribution Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski | |
| Subspace Embeddings under Nonlinear Transformations Aarshvi Gajjar, Cameron Musco |