Publications

You can also find Jason's articles on his Google Scholar profile.

Decision Trees, Random Forests, and Ensemble Methods

Y. S. Tan, J. M. Klusowski, and K. Balasubramanian, “Statistical-computational Trade-offs for Recursive Adaptive Partitioning Estimators,” Annals of Statistics, 2026 [journal] [preprint]
M. D. Cattaneo, J. M. Klusowski, and W. Underwood, "Inference with Mondrian Random Forests," Journal of the Royal Statistical Society, Series B, 2025 [journal] [preprint] [code]
X. Chen, J. M. Klusowski, Y. S. Tan, and C. Yu, “Revisiting Randomization in Greedy Model Search,” 2025 [preprint]
X. Chen, J. M. Klusowski, and Y. S. Tan, "Error Reduction from Stacked Regressions," 2023+ [preprint]
M. D. Cattaneo, R. Chandak, and J. M. Klusowski, "Convergence Rates of Oblique Regression Trees for Flexible Function Libraries," Annals of Statistics, 2024 [journal] [preprint]
M. D. Cattaneo, J. M. Klusowski, and R. Yu, “Accuracy Limits of Causal Trees for Individualized Treatment Effects,” 2026 [preprint]
- Supersedes the paper M. D. Cattaneo, J. M. Klusowski, and P. M. Tian, “On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation,” Technical report, 2022 [preprint]
J. M. Klusowski and P. M. Tian, "Large Scale Prediction with Decision Trees," Journal of the American Statistical Association, 2024 [journal] [preprint]
J. M. Klusowski and P. M. Tian, "Nonparametric Variable Screening with Optimal Decision Stumps," AISTATS, 2021 [proceedings] [extended version]
J. M. Klusowski, "Sparse Learning with CART" NeurIPS, 2020 [proceedings] [poster] [code]
J. M. Klusowski, "Sharp Analysis of a Simple Model for Random Forests," AISTATS, 2021 [proceedings]

Statistical Theory and Methodology

A. Maleki, S. Sen, S. Balakrishnan, V. Zuber, C. Gao, R. Dudeja, C. Thrampoulidis, A. Zhang, W. Su, J. M. Klusowski, P.-L. Loh, and A. Shojaie, “High-Dimensional Statistics: Reflections on Progress and Open Problems,” 2026 [preprint]
J. Shi, O. Hagrass, and J. M. Klusowski, “Coupled Training with Privileged Information and Unlabeled Data,” ICML, 2026 [proceedings] [preprint]
E. Xia and J. M. Klusowski, “Classification Imbalance as Transfer Learning,” 2026 [preprint]
J. M. Klusowski, I. Kontoyiannis, and C. Rush, "Editorial," To appear in Information Theory, Probability and Statistical Learning: A Festschrift in Honor of Andrew Barron, Springer, 2026 [preprint]
X. Chen and J. M. Klusowski, "Stochastic Gradient Descent for Nonparametric Additive Regression," To appear in Bernoulli, 2026 [preprint]
Z. Bu, J. M. Klusowski, C. Rush, R. Wu, "Sharp Trade-Offs in High-Dimensional Inference via 2-Level SLOPE,” Reject and resubmit in Journal of the Royal Statistical Society, Series B, 2026 [preprint]
J. Fan, C. Gao, and J. M. Klusowski, "Robust Transfer Learning with Unreliable Source Data," Annals of Statistics, 2025 [journal] [preprint]
Z. Bu, J. M. Klusowski, C. Rush, W. J. Su, "Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho–Tanner Limit," Annals of Statistics, 2023 [journal] [preprint] [code]
V. E. Brunel, J. M. Klusowski, and D. Yang, "Estimation of Convex Supports from Noisy Measurements," Bernoulli, 2021 [journal] [preprint]
J. M. Klusowski and Y. Wu, "Estimating the Number of Connected Components in a Graph via Subgraph Sampling," Bernoulli, 2020 [journal] [preprint]
Z. Bu, J. M. Klusowski, C. Rush, and W. J. Su, "Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing" [code]
- NeurIPS, 2019 [proceedings] [poster]
- Longer version in IEEE Transactions on Information Theory, 2021 [journal] [preprint]
J. M. Klusowski, D. Yang, and W. D. Brinda, "Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximization," IEEE Transactions on Information Theory, 2019 [journal] [preprint]
W. D. Brinda, J. M. Klusowski, and D. Yang, "Hölder's Identity," Statistics & Probability Letters, 2019 [journal]
W. D. Brinda and J. M. Klusowski, "Finite-Sample Risk Bounds for Maximum Likelihood Estimation with Arbitrary Penalties," IEEE Transactions on Information Theory, 2018 [journal] [preprint]
J. M. Klusowski and Y. Wu, "Counting Motifs with Graph Sampling," COLT, 2018 [proceedings] [preprint] [poster]
J. M. Klusowski and W. D. Brinda, "Statistical Guarantees for Estimating the Centers of a Two-Component Gaussian Mixture by EM," Technical report, 2016 [preprint]

Neural Networks, Transformers, and Foundation Models

S. Chen, O. Hagrass, and J. M. Klusowski, "Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies," ICLR, 2025 [proceedings] [preprint] [poster]
J. M. Klusowski and J. W. Siegel, "Sharp Convergence Rates for Matching Pursuit," IEEE Transactions on Information Theory, 2025 [journal] [preprint]
C. Gao, Y. Cao, Z. Li, Y. He, M. Wang, H. Liu, J. M. Klusowski, and J. Fan, "Global Convergence in Training Large-Scale Transformers," NeurIPS, 2024 [proceedings] [preprint]
Z. Li, Y. Cao, C. Gao, Y. He, H. Liu, J. M. Klusowski, J. Fan, and M. Wang, "One-Layer Transformer Provably Learns One-Nearest Neighbor In Context," NeurIPS, 2024 [proceedings] [preprint]
M. D. Cattaneo, J. M. Klusowski, and B. Shigida, "On the Implicit Bias of Adam," ICML, 2024 [proceedings] [preprint] [code]
R. Theisen, J. M. Klusowski, and M. W. Mahoney, "Good Classifiers are Abundant in the Interpolating Regime," AISTATS, 2021 [proceedings] [preprint]
A. R. Barron and J. M. Klusowski, "Approximation and Estimation for High-Dimensional Deep Learning Networks," Technical report, 2018 [preprint]
J. M. Klusowski and A. R. Barron, "Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with ℓ₁ and ℓ₀ Controls," IEEE Transactions on Information Theory, 2018 [journal] [preprint]
J. M. Klusowski and A. R. Barron, "Risk Bounds for High-Dimensional Ridge Function Combinations Including Neural Networks," Technical report, 2018 [preprint]
J. M. Klusowski and A. R. Barron, "Minimax Lower Bounds for Ridge Combinations Including Neural Nets," Proceedings IEEE International Symposium on Information Theory, 2017 [proceedings] [preprint]