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ORCID32nd ALT 2021: Virtual Event
- Vitaly Feldman, Katrina Ligett, Sivan Sabato:
Algorithmic Learning Theory, 16-19 March 2021, Virtual Conference, Worldwide. Proceedings of Machine Learning Research 132, PMLR 2021 - Algorithmic Learning Theory 2021: Preface. 1-2
- Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono:
Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. 3-47 - Jayadev Acharya, Ziteng Sun, Huanyu Zhang:
Differentially Private Assouad, Fano, and Le Cam. 48-78 - Jayadev Acharya, Peter Kairouz, Yuhan Liu
, Ziteng Sun:
Estimating Sparse Discrete Distributions Under Privacy and Communication Constraints. 79-98 - Juliette Achddou, Olivier Cappé, Aurélien Garivier:
Efficient Algorithms for Stochastic Repeated Second-price Auctions. 99-150 - Raghavendra Addanki, Andrew McGregor, Cameron Musco:
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs. 151-184 - Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath:
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. 185-216 - Arpit Agarwal, Shivani Agarwal, Prathamesh Patil:
Stochastic Dueling Bandits with Adversarial Corruption. 217-248 - Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. 249-305 - Mridul Agarwal, Vaneet Aggarwal, Christopher J. Quinn, Abhishek K. Umrawal:
Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback. 306-339 - Alankrita Bhatt, Young-Han Kim:
Sequential prediction under log-loss with side information. 340-344 - Robi Bhattacharjee, Michal Moshkovitz:
No-substitution k-means Clustering with Adversarial Order. 345-366 - Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, N. V. Vinodchandran:
Testing Product Distributions: A Closer Look. 367-396 - Nataly Brukhim, Elad Hazan:
Online Boosting with Bandit Feedback. 397-420 - Mark Cesar, Ryan Rogers:
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics. 421-457 - Siu On Chan, Qinghua Ding, Sing Hei Li:
Learning and Testing Irreducible Markov Chains via the k-Cover Time. 458-480 - Aidao Chen, Anindya De, Aravindan Vijayaraghavan:
Learning a mixture of two subspaces over finite fields. 481-504 - Thibaut Cuvelier, Richard Combes, Eric Gourdin:
Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time. 505-528 - Riccardo Della Vecchia, Tommaso Cesari:
An Efficient Algorithm for Cooperative Semi-Bandits. 529-552 - Le Cong Dinh, Tri-Dung Nguyen, Alain B. Zemkoho, Long Tran-Thanh:
Last Round Convergence and No-Dynamic Regret in Asymmetric Repeated Games. 553-577 - Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko:
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. 578-598 - Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe:
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds. 599-618 - Louis Faury, Yoan Russac, Marc Abeille, Clément Calauzènes:
A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions. 619-626 - Aarshvi Gajjar, Cameron Musco:
Subspace Embeddings under Nonlinear Transformations. 656-672 - Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski:
Efficient sampling from the Bingham distribution. 673-685 - Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Near-tight closure b ounds for the Littlestone and threshold dimensions. 686-696 - Steve Hanneke, Aryeh Kontorovich:
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. 697-721 - Rishabh K. Iyer, Ninad Khargoankar, Jeff A. Bilmes, Himanshu Asanani:
Submodular combinatorial information measures with applications in machine learning. 722-754 - Philippe Jacquet, Gil I. Shamir, Wojciech Szpankowski:
Precise Minimax Regret for Logistic Regression with Categorical Feature Values. 755-771 - Ziwei Ji, Matus Telgarsky:
Characterizing the implicit bias via a primal-dual analysis. 772-804 - Marc Jourdan, Mojmír Mutný, Johannes Kirschner, Andreas Krause:
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback. 805-849 - Adam Tauman Kalai, Varun Kanade:
Efficient Learning with Arbitrary Covariate Shift. 850-864 - Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko:
Adaptive Reward-Free Exploration. 865-891 - Michal Moshkovitz:
Unexpected Effects of Online no-Substitution k-means Clustering. 892-930 - Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. 931-962 - Willie Neiswanger, Aaditya Ramdas:
Uncertainty quantification using martingales for misspecified Gaussian processes. 963-982 - Michela Meister, Sloan Nietert:
Learning with Comparison Feedback: Online Estimation of Sample Statistics. 983-1001 - Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster:
Online Learning of Facility Locations. 1002-1050 - Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak S. Dalalyan:
Statistical guarantees for generative models without domination. 1051-1071 - Jie Shen, Chicheng Zhang:
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance. 1072-1113 - Joseph Suk, Samory Kpotufe:
Self-Tuning Bandits over Unknown Covariate-Shifts. 1114-1156 - Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model. 1157-1178 - Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu:
Contrastive learning, multi-view redundancy, and linear models. 1179-1206 - Di Wang, Huangyu Zhang, Marco Gaboardi, Jinhui Xu:
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. 1207-1213 - Manfred K. Warmuth, Wojciech Kotlowski, Ehsan Amid:
A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer. 1214-1236 - Gellért Weisz, Philip Amortila, Csaba Szepesvári:
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. 1237-1264 - Changlong Wu, Narayana Santhanam:
Non-uniform Consistency of Online Learning with Random Sampling. 1265-1285
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