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

Neural Networks and Deep Learning

• 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 [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 [preprint]
• S. Chen, O. Hagrass, and J. M. Klusowski, "Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies," ICLR, 2025 [preprint] [slides]
• 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]

Decision Trees and Ensemble Methods

• Y. S. Tan, J. M. Klusowski, and K. Balasubramanian, "Statistical-computational Trade-offs for Recursive Adaptive Partitioning Estimators," 2024 [preprint]
• M. D. Cattaneo, J. M. Klusowski, and W. Underwood, "Inference with Mondrian Random Forests," Reject and resubmit at Annals of Statistics, 2023+ [preprint] [code] [slides]
• 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," Reject and resubmit at Annals of Statistics, 2022+ [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]
• 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" [code]
  • NeurIPS, 2020 [proceedings] [poster]
  • Longer version: "Sparse Learning with CART for Noiseless Regression Models," Forthcoming in IEEE Transactions on Information Theory, 2022 [preprint]
• J. M. Klusowski, "Sharp Analysis of a Simple Model for Random Forests," AISTATS, 2021 [proceedings]

Optimization and Learning Theory

• X. Chen and J. M. Klusowski, "Stochastic Gradient Descent for Nonparametric Regression," 2024 [preprint]
• M. D. Cattaneo, J. M. Klusowski, and B. Shigida, "On the Implicit Bias of Adam," ICML, 2024 [proceedings] [preprint] [code]
• J. M. Klusowski and J. W. Siegel, "Sharp Convergence Rates for Matching Pursuit," Forthcoming in IEEE Transactions on Information Theory, 2024 [preprint]
• X. Chen, J. M. Klusowski, and Y. S. Tan, "Error Reduction from Stacked Regressions," Reject and resubmit at Annals of Statistics, 2023+ [preprint]
• R. Theisen, J. M. Klusowski, and M. W. Mahoney, "Good Classifiers are Abundant in the Interpolating Regime," AISTATS, 2021 [proceedings] [preprint]

High-dimensional Statistics

• 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]
• 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]

Mixture Models and EM Algorithm

• 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]
• 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]

Network Analysis and Graph Theory

• J. M. Klusowski and Y. Wu, "Estimating the Number of Connected Components in a Graph via Subgraph Sampling," Bernoulli, 2020 [journal] [preprint]
• J. M. Klusowski and Y. Wu, "Counting Motifs with Graph Sampling," Conference on Learning Theory (COLT), 2018 [proceedings] [preprint] [poster]

Shape Constrained Estimation

• V. E. Brunel, J. M. Klusowski, and D. Yang, "Estimation of Convex Supports from Noisy Measurements," Bernoulli, 2021 [journal] [preprint]

Transfer Learning

• J. Fan, C. Gao, and J. M. Klusowski, "Robust Transfer Learning with Unreliable Source Data," Major revision at Annals of Statistics, 2023+ [preprint]

Miscellaneous

• A. Liang, T. Jemielita, A. Liaw, V. Svetnik, L. Huang, R. Baumgartner, J. M. Klusowski, "Challenges in Variable Importance Ranking Under Correlation," Technical report, 2024 [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]