| 2024 | Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening (Extended Abstract). | Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis |
| 2024 | Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise. | Matthew Joseph, Alexander Yu |
| 2024 | Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract). | Yujia Jin, Ishani Karmarkar, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh |
| 2024 | Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. | Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei |
| 2024 | Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space. | Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian |
| 2024 | Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. | Arun Jambulapati, Aaron Sidford, Kevin Tian |
| 2024 | Black-Box k-to-1-PCA Reductions: Theory and Applications. | Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian |
| 2024 | Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. | Shinji Ito, Taira Tsuchiya, Junya Honda |
| 2024 | Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy. | Bingshan Hu, Nishant A. Mehta |
| 2024 | Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. | Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang |
| 2024 | Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs. | Dong Huang, Xianwen Song, Pengkun Yang |
| 2024 | Reconstructing the Geometry of Random Geometric Graphs (Extended Abstract). | Han Huang, Pakawut Jiradilok, Elchanan Mossel |
| 2024 | Adversarially-Robust Inference on Trees via Belief Propagation. | Samuel B. Hopkins, Anqi Li |
| 2024 | Open problem: Direct Sums in Learning Theory. | Steve Hanneke, Shay Moran, Tom Waknine |
| 2024 | List Sample Compression and Uniform Convergence. | Steve Hanneke, Shay Moran, Tom Waknine |
| 2024 | The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement. | Steve Hanneke |
| 2024 | Prediction from compression for models with infinite memory, with applications to hidden Markov and renewal processes. | Yanjun Han, Tianze Jiang, Yihong Wu |
| 2024 | Minimax Linear Regression under the Quantile Risk. | Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu |
| 2024 | Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation. | Shivam Gupta, Samuel B. Hopkins, Eric C. Price |
| 2024 | Community detection in the hypergraph stochastic block model and reconstruction on hypertrees. | Yuzhou Gu, Aaradhya Pandey |
| 2024 | Stochastic Constrained Contextual Bandits via Lyapunov Optimization Based Estimation to Decision Framework. | Hengquan Guo, Xin Liu |
| 2024 | Principal eigenstate classical shadows. | Daniel Grier, Hakop Pashayan, Luke Schaeffer |
| 2024 | On the Computability of Robust PAC Learning. | Pascale Gourdeau, Tosca Lechner, Ruth Urner |
| 2024 | Identification of mixtures of discrete product distributions in near-optimal sample and time complexity. | Spencer L. Gordon, Erik Jahn, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman |
| 2024 | Omnipredictors for regression and the approximate rank of convex functions. | Parikshit Gopalan, Princewill Okoroafor, Prasad Raghavendra, Abhishek Sherry, Mihir Singhal |