2022
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Agarwal, Abhineet; Tan, Yan Shuo; Ronen, Omer; Singh, Chandan; Yu, Bin, Hierarchical Shrinkage: Improving the Accuracy and Interpretability of Tree-Based Methods, Proceedings of Machine Learning Research, 2022.
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Ajtai, M.; Braverman, V.; Jayram, T.S.; Silwal, S.; Sun, A.; Woodruff, D.P.; Zhou, S., The White-Box Adversarial Data Stream Model, Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022), 15-27, 2022.
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Alali, A.; Rawat, D.; Liu, C., Trajectory and Power Optimization in sub-THz band for UAV Communications, Proceedings of the IEEE International Conference on Communications (ICC 2022), 2022.
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Angelopoulos, Anastasios N; Kohli, Amit P; Bates, Stephen; Jordan, Michael I; Malik, Jitendra; Alshaabi, Thayer; Upadhyayula, Srigokul; Romano, Yaniv, Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging, International Conference on Machine Learning, 2022.
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Baabdullah, T.; Rawat, D.; Liu, C.; Alzahrani, A., An Ensemble-Based Machine Learning for Predicting Fraud of Credit Card Transactions, Proceedings of 2022 Computing Conference, Lecture Notes in Networks and Systems, 2022.
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Bartlett, Peter L.; Indyk, Piotr; Wagner, Tal, Generalization Bounds for Data-Driven Numerical Linear Algebra, Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022.
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Biswas, A.S.; Pyne, E.; Rubinfeld, R., Local Access to Random Walks , January 2022, Innovations in Theoretical Computer Science (ITCS 2022), 2022.
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Braverman, Mark; Derakhshan, Mahsa; Lovett, Antonio Molina, Max-Weight Online Stochastic Matching: Improved Approximations Against the Online Benchmark, 23rd ACM Conference on economics and Computation, 2022.
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Chatterji, Niladri S.; Bartlett, Peter L.; Long, Philip M., Oracle lower bounds for stochastic gradient sampling algorithms, Bernoulli, 28, 2, 1074-1092, 2022.
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Chatterji, Niladri S.; Long, Philip M.; Bartlett, Peter L., The interplay between implicit bias and benign overfitting in two-layer linear networks, Journal of machine learning research, 2022.
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Chen, J.; Eden, T.; Indyk, P.; Narayanan, S.; Rubinfeld, R.; Silwal, S.; Woodruff, D.; Zhang, M., Triangle and Four Cycle Counting with Predictions in Graph Streams, Tenth International Conference on Learning Representations (ICLR 2022), 2022.
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Chen, J.; Indyk, P.; Wagner, T., Streaming Algorithms for Support-Aware Histograms, International Conference on Machine Learning, 3184-3203, 2022.
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Chen, J.; Silwal, S.; Vakilian, A.; Zhang, F., Faster Fundamental Graph Algorithms via Learned Predictions, Proceedings of the 39th International Conference on Machine Learning (PMLR), 3583-3602, 2022.
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Cherapanamjeri, Yeshwanth; Daskalakis, Constantinos; Ilyas, Andrew; Zampetakis, Manolis, Estimation of Standard Auction Models, 23rd ACM Conference on Economics and Computation, 2022.
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Cherapanamjeri, Yeshwanth; Tripuraneni, Nilesh; Bartlett, Peter L.; Jordan, Michael I., Optimal Mean Estimation without a Variance, Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022.
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Chewi, Sinho; Gerber, Patrik R.; Lu, Chen; Le Gouic, Thibaut; Rigollet, Philippe, The query complexity of sampling from strongly log-concave distributions in one dimension, Proceedings of Thirty Fifth Conference on Learning Theory, 178, 2041--2059, 2022.
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Duncan, James; Kapoor, Rush; Agarwal, Abhineet; Singh, Chandan; Yu, Bin, VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS, Journal of Open Source Software, 7, 69, 3895, 2022.
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Dwivedi, Raaz; Mackey, Lester, Generalized Kernel Thinning, Tenth International Conference on Learning Representations (ICLR 2022), 2022.
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Fannjiang, Clara; Bates, Stephen; Angelopoulos, Anastasios N.; Listgarten, Jennifer; Jordan, Michael I., Conformal prediction for the design problem, Proceedings of the National Academy of Sciences of the United States of America, 2022.
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Frei, Spencer; Chatterji, Niladri; Bartlett, Peter L., Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data, Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022.
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Guo, W., No-Regret Learning in Partially-Informed Auctions, International Conference on Machine Learning, 2022.
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Indyk, P.; Mallmann-Trenn, F.; Mitrovic, S.; Rubinfeld, R., Online Page Migration with ML Advice, 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 2022.
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Indyk, P.; Narayanan, S.; Woodruff, D.P., Frequency Estimation with One-Sided Error, Proceedings of the annual ACMSIAM symposium on discrete algorithms, 2022.
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Indyk, P.; Wagner, T., Optimal (Euclidean) Metric Compression, SIAM journal on computing, 51, 3, 467-491, 2022.
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Kelner, Jonathan A.; Koehler, Frederic; Meka, Raghu; Rohatgi, Dhruv, On the Power of Preconditioning in Sparse Linear Regression, 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), 550 to 561, 2022.
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Kelner, Jonathan; Marsden, Annie; Sharan, Vatsal; Sidford, Aaron; Valiant, Gregory; Yuan, Honglin, Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, Conference on Learning Theory, 2022.
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Kızılda ̆g, E.; Gamarnik, D.; Zadik, I., Self-regularity of non-negative output weights for overparameterized two-layer neural networks, IEEE transactions on signal processing, 2022.
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Knorr, Eric R.; Lemaire, Baptiste; Lim, Andrew; Luo, Siqiang; Zhang, Huanchen; Idreos, Stratos; Mitzenmacher, Michael, Proteus: A Self-Designing Range Filter, SIGMOD 2022, 1670 to 1684, 2022.
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Kuszmaul, W.; Narayanan, S., Optimal Time-Backlog Tradeoffs for the Variable-Processor Cup Game. Accepted to, International Colloquium on Automata, Languages and Programming (ICALP 2022), 2022.
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Li, Xiaoyu; Liu, Mingrui; Orabona, Francesco, On the Last Iterate Convergence of Momentum Methods, Proceedings of Machine Learning Research, 2022.
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Liu, Z.; Lu, M.; Wang, Z.; Jordan, M. I.; Yang, Z., Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy, International Conference on Machine Learning, 2022.
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Mitzenmacher, Michael; Dell'Amico, Matteo, The Supermarket Model with Known and Predicted Service Times, IEEE Transactions on Parallel and Distributed Systems, 1 to 1, 2022.
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Mitzenmacher, Michael; Vassilvitskii, Sergei, Algorithms with predictions, Communications of the ACM, 65, 7, 33 to 35, 2022.
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Mou, Wenlong; Flammarion, Nicolas; Wainwright, Martin J.; Bartlett, Peter L., Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity, Bernoulli, 28, 3, 1577-1601, 2022.
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Mou, Wenlong; Pananjady, Ashwin; Wainwright, Martin J.; Bartlett, Peter L., Optimal and instance-dependent guarantees for Markovian linear stochastic approximation, Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022.
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Narayanan, S., Private High-Dimensional Hypothesis Testing, Conference on Learning Theory (COLT 2022), 2022.
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Nasseri, Keyan; Singh, Chandan; Duncan, James; Kornblith, Aaron; Yu, Bin, Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data, ArXiv.org, 2022.
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Perchet, Vianney; Rigollet, Philippe; Le Gouic, Thibaut, An Algorithmic Solution to the Blotto Game using Multi-marginal Couplings, Proceedings of the 23rd ACM Conference on Economics and Computation, 208-209, 2022.
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Price, E.; Silwal, S.; Zhou, S., Hardness and Algorithms for Robust and Sparse Optimization 162:17926-17944, 2022., Proceedings of the 39th International Conference on Machine Learning (PMLR), 162, 17926-17944, 2022.
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Scully, Z; Grosof, I.; Mitzenmacher, M., Uniform Bounds for Scheduling with Job Size Estimates, 13th Innovations in Theoretical Computer Science Conference, ITCS, 2022.
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Shetty, Abhishek; Dwivedi, Raaz; Mackey, Lester, Distribution Compression in Near-linear Time, Tenth International Conference on Learning Representations (ICLR 2022)., 2022.
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Silwal, S., A concentration inequality for the facility location problem, Operations research letters, 50, 2, 213-217, 2022.
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Singh, Chandan; Ha, Wooseok; Yu, Bin, Interpreting and Improving Deep-Learning Models with Reality Checks, Lecture notes in computer science, 2022.
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T Eden, T.; Indyk, P.; Xu, H., Embeddings and labeling schemes for A*, Innovations in Theoretical Computer Science (ITCS), 2022.
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Tan, Yan Shuo; Agarwal, Abhineet; Yu, Bin, A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds, Proeedings of the International Workshop on Artificial Intelligence and Statistics, 2022.
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Tan, Yan Shuo; Singh, Chandan; Nasseri, Keyan; Agarwal, Abhineet; Yu, Bin, Fast Interpretable Greedy-Tree Sums (FIGS), ArXiv.org, 2022.
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Wei, Alexander; Hu, Wei; Steinhardt, Jacob, More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize, International Conference on Machine Learning, 2022.
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Wu, J.; Zhang, Z.; Feng, Z.; Wang, Z.; Yang, Z.; Jordan, M. I.; Xu, H., Markov Persuasion Processes and Reinforcement Learning, ACM Conference on Economics and Computation, 2022.
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Zanette, Andrea; Brunskill, Emma; Wainwright, Martin J., Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning, NEURIPS Conference 2021, 2021.
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Zanette, Andrea; Wainwright, Martin, Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning., International Conference on Machine Learning, 2022.
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Zanette, Andrea; Wainwright, Martin J., Bellman Residual Orthogonalization for Offline Reinforcement Learning, ArXiv.org, 2022.
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Zhang, J.; Li, H.; Sra, S.; Jadbabaie, A., Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective, Thirty-ninth International Conference on Machine Learning (ICML 2022), 26330-26346, 2022.
2021
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Backurs, A; Indyk, P; Musco, C; Wagner, T, Faster Kernel Matrix Algebra via Density Estimation, International Conference on Machine Learning, 500-510, 2021.
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Bartlett, Peter L.; Bubeck, Sebastien; Cherapanamjeri, Yeshwanth, Adversarial Examples in Multi-Layer Random ReLU Networks, Advances in Neural Information Processing Systems, 34, 2021.
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Bartlett, Peter L.; Montanari, Andrea; Rakhlin, Alexander, Deep learning: a statistical viewpoint, Acta Numerica, 30, 87 to 201, 2021.
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Bhatia, Kush; Bartlett, Peter L.; Dragan, Anca D.; Steinhardt, Jacob, Agnostic Learning with Unknown Utilities, Leibniz international proceedings in informatics, 185, 55:1-55:20, 2021.
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Biswas, A. S.; Eden, T.; Rubinfeld, R., Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time, Random, 2021.
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Brown, Gavin; Bun, Mark; Feldman, Vitaly; Smith, Adam; Talwar, Kunal, When is memorization of irrelevant training data necessary for high-accuracy learning?, ACM Symposium on the Theory of Computation (STOC), 123 to 132, 2021.
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Cen, Sarah and, Regulating algorithmic filtering on social media, Advances in neural information processing systems, 34, 2021.
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Chatterji, Niladri S.; Long, Philip M.; Bartlett, Peter L., When does gradient descent with logistic loss find interpolating two-layer networks?, Journal of machine learning research, 22, 159, 1-48, 2021.
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Chatterji, Niladri; Pacchiano, Aldo; Bartlett, Peter L.; Jordan, Michael I., On the Theory of Reinforcement Learning with Once-per-Episode Feedback, Advances in neural information processing systems, 34, 2021.
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Chewi, Sinho, Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm, Proceedings of Thirty Fourth Conference on Learning Theory, 134, 2021.
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Chewi, Sinho; Clancy, Julien; Le Gouic, Thibaut; Rigollet, Philippe; Stepaniants, George; Stromme, Austin, Fast and Smooth Interpolation on Wasserstein Space, Proceedings of Machine Learning Research, 130, 3061--3069, 2021.
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Cosson, Romain; Shah, Devavrat, Quantifying Variational Approximation for Log-Partition Function, Annual Conference on Learning Theory, 134, 2021.
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Daniels, Max; Maunu, Tyler; Hand, Paul, Score-based Generative Neural Networks for Large-Scale Optimal Transport, Advances in neural information processing systems, 34, 2021.
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Daskalakis, Constantinos; Skoulakis, Stratis; Zampetakis, Manolis, The Complexity of Constrained Min-Max Optimization, Proceedings of the Annual ACM Symposium on Theory of Computing, 53, 2021.
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Daskalakis, Constantinos; Stefanou, Patroklos; Yao, Rui; Zampetakis, Manolis, Efficient Truncated Linear Regression with Unknown Noise Variance, Advances in neural information processing systems, 2021.
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Du, Elbert; Wang, Franklyn; Mitzenmacher, Michael, Putting the “Learning†into Learning-Augmented Algorithms for Frequency Estimation, International Conference on Machine Learning, 2021.
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Eden, T.; Mossel, S.; Rubinfeld, R., Sampling Multiple Edges Efficiently, Random, 2021.
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Eden, Talya; Indyk, Piotr; Narayanan, Shyam; Rubinfeld, Ronitt; Silwal, Sandeep; Wagner, Tal, Learning-based Support Estimation in Sublinear Time, International Conference on Learning Representations, 2021.
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Flaspohler, Genevieve E; Orabona, Francesco; Cohen, Judah; Mouatadid, Soukayna; Oprescu, Miruna; Orenstein, Paulo; Mackey, Lester, Online Learning with Optimism and Delay, Proceedings of Machine Learning Research, 139, 2021.
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Gamarnik, D.; Kizildag, E.C.; Ilias Zadik, I., Inference in high-dimensional linear regression via lattice basis reduction and integer relation detection, IEEE transactions on information theory, 2021.
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Gamarnik, David; Kızıldağ, Eren C.; Zadik, Ilias, Self-Regularity of Non-Negative Output Weightsfor Overparameterized Two-Layer Neural Networks, International Symposium on Information Theory, 2021.
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Guo, Wenshuo; Jordan, Michael; Zampetakis, Emmanouil, Robust Learning of Optimal Auctions, Advances in neural information processing systems, 2021.
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Ha, Wooseok; Singh, Chandan; Lanusse, Francois; Upadhyayula, Srigokul; Yu, Bin, Adaptive wavelet distillation from neural networks through interpretations, Advances in neural information processing systems, 2021.
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Hand, Paul; Leong, Oscar; Voroninski, Vladislav, Optimal Sample Complexity of Subgradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration, Communications in mathematical sciences, 19, 7, 2035 - 2047, 2021.
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Jain, Prateek; Rush, John; Smith, Adam; Song, Shuang; Thakurta, Abhradeep Guha, Differentially Private Model Personalization, Advances in neural information processing systems, 29723-29735, 2021.
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Lam, Maximillian; Wei, Gu-Yeon; Brooks, David; Reddi, Vijay; Mitzenmacher, Michael, Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix, International Conference on Machine Learning, 2021.
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Li, Xiaoyu; Zhuang, Zhenxun; Orabona, Francesco, A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance, Proceedings of Machine Learning Research, 139, 2021.
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Macke, S.; Aliakbarpour, M.; Diakonikolas, I.; Parameswaran, A.; Rubinfeld, R., Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees, IEEE International Conference on Data Engineering workshop, 2021.
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Mitzenmacher, Michael, Queues with Small Advice, SIAM Conference on Applied and Computational Discrete Algorithms, 2021.
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Muehlebach, Michael; Jordan, Michael I, On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems, ArXiv.org, 2021.
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Narayanan, S; Silwal, S; Indyk, P; Zamir, O, Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering, International Conference on Machine Learning, 7948-7957, 2021.
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Pacchiano, Aldo; Ghavamzadeh, Mohammad; Bartlett, Peter L.; Jiang, Heinrich, Stochastic Bandits with Linear Constraints, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 130, 2827-2835, 2021.
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Pacchiano, Aldo; Lee, Jonathan; Bartlett, Peter L.; Nachum, Ofir, Near Optimal Policy Optimization via REPS, Advances in neural information processing systems, 34, 2021.
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Perdomo, Juan; Simchowitz, Max; Agarwal, Alekh; Bartlett, Peter L., Towards a Dimension-Free Understanding of Adaptive Linear Control, Proceedings of the 34th Conference on Learning Theory (COLT2021), 2021.
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Shah, Abhin, A Computationally Efficient Method for Learning Exponential Family Distributions, Advances in neural information processing systems, 34, 2021.
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Turner, Paxton, Efficient Interpolation of Density Estimators, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 130, 2021.
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Turner, Paxton; Liu, Jingbo; Rigollet, Philippe, A Statistical Perspective on Coreset Density Estimation, Proceedings of Machine Learning Research, 130, 2512--2520, 2021.
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Vargaftik, S.; Ben Basat, R.; Fortnoy, A.; Mendleson, G.; Ben-Itzhak, Y.; Mitzenmacher, M., DRIVE: One-bit Distributed Mean Estimation, Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021.
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Zrnic, Tijana; Mazumdar, Eric; Sastry, Shankar; Jordan, Michael I, Who Leads and Who Follows in Strategic Classification?, ArXiv.org, 2021.
