Minimax lower bounds for ridge combinations including neural nets. - Jason M. Klusowski; Andrew R. Barron (ISIT, 2017)

Counting Motifs with Graph Sampling. - Jason M. Klusowski; Yihong Wu (COLT, 2018)

Estimating the Number of Connected Components in a Graph via Subgraph Sampling. - Jason M. Klusowski; Yihong Wu (CoRR, 2018)

Complete Analysis of a Random Forest Model. - Jason M. Klusowski (CoRR, 2018)

Approximation and Estimation for High-Dimensional Deep Learning Networks. - Andrew R. Barron; Jason M. Klusowski (CoRR, 2018)

Finite-Sample Risk Bounds for Maximum Likelihood Estimation With Arbitrary Penalties. - W. D. Brinda; Jason M. Klusowski (IEEE Trans. Inf. Theory, 2018)

Approximation by Combinations of ReLU and Squared ReLU Ridge Functions With ℓ1 and ℓ0 Controls. - Jason M. Klusowski; Andrew R. Barron (IEEE Trans. Inf. Theory, 2018)

Complexity, Statistical Risk, and Metric Entropy of Deep Nets Using Total Path Variation. - Andrew R. Barron; Jason M. Klusowski (CoRR, 2019)

Best Split Nodes for Regression Trees. - Jason M. Klusowski (CoRR, 2019)

Global Capacity Measures for Deep ReLU Networks via Path Sampling. - Ryan Theisen; Jason M. Klusowski; Huan Wang 014; Nitish Shirish Keskar; Caiming Xiong; Richard Socher (CoRR, 2019)

Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximization. - Jason M. Klusowski; Dana Yang; W. D. Brinda (IEEE Trans. Inf. Theory, 2019)

Sparse Learning with CART. - Jason M. Klusowski (NeurIPS, 2020)

Good linear classifiers are abundant in the interpolating regime. - Ryan Theisen; Jason M. Klusowski; Michael W. Mahoney (CoRR, 2020)

Sharp Analysis of a Simple Model for Random Forests. - Jason M. Klusowski (AISTATS, 2021)

Nonparametric Variable Screening with Optimal Decision Stumps. - Jason M. Klusowski; Peter M. Tian (AISTATS, 2021)

Good Classifiers are Abundant in the Interpolating Regime. - Ryan Theisen; Jason M. Klusowski; Michael W. Mahoney (AISTATS, 2021)

Universal Consistency of Decision Trees for High Dimensional Additive Models. - Jason M. Klusowski (CoRR, 2021)

Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit. - Zhiqi Bu; Jason M. Klusowski; Cynthia Rush; Weijie J. Su (CoRR, 2021)

Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. - Zhiqi Bu; Jason M. Klusowski; Cynthia Rush; Weijie J. Su (IEEE Trans. Inf. Theory, 2021)

On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation. - Matias D. Cattaneo; Jason M. Klusowski; Peter M. Tian (CoRR, 2022)

Error Reduction from Stacked Regressions. - Xin Chen; Jason M. Klusowski; Yan Shuo Tan (CoRR, 2023)

Robust Transfer Learning with Unreliable Source Data. - Jianqing Fan; Cheng Gao; Jason M. Klusowski (CoRR, 2023)

On the Implicit Bias of Adam. - Matias D. Cattaneo; Jason M. Klusowski; Boris Shigida (ICML, 2024)

Global Convergence in Training Large-Scale Transformers. - Cheng Gao; Yuan Cao; Zihao Li; Yihan He; Mengdi Wang; Han Liu; Jason M. Klusowski; Jianqing Fan (NeurIPS, 2024)

One-Layer Transformer Provably Learns One-Nearest Neighbor In Context. - Zihao Li; Yuan Cao; Cheng Gao; Yihan He; Han Liu; Jason M. Klusowski; Jianqing Fan; Mengdi Wang (NeurIPS, 2024)

Stochastic Gradient Descent for Additive Nonparametric Regression. - Xin Chen; Jason M. Klusowski (CoRR, 2024)

Challenges in Variable Importance Ranking Under Correlation. - Annie Liang; Thomas Jemielita; Andy Liaw; Vladimir Svetnik; Lingkang Huang; Richard Baumgartner; Jason M. Klusowski (CoRR, 2024)

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators. - Yan Shuo Tan; Jason M. Klusowski; Krishnakumar Balasubramanian 002 (CoRR, 2024)

Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies. - Sijin Chen; Omar Hagrass; Jason Matthew Klusowski (ICLR, 2025)

Revisiting Randomization in Greedy Model Search. - Xin Chen; Jason M. Klusowski; Yan Shuo Tan; Chang Yu (CoRR, 2025)

Sharp Convergence Rates for Matching Pursuit. - Jason M. Klusowski; Jonathan W. Siegel (IEEE Trans. Inf. Theory, 2025)