Jason M. Klusowski

Orcid: 0000-0001-6484-8682

According to our database1, Jason M. Klusowski authored at least 28 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Global Convergence in Training Large-Scale Transformers.
CoRR, 2024

Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies.
CoRR, 2024

Challenges in Variable Importance Ranking Under Correlation.
CoRR, 2024

Stochastic Gradient Descent for Additive Nonparametric Regression.
CoRR, 2024

On the Implicit Bias of Adam.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Robust Transfer Learning with Unreliable Source Data.
CoRR, 2023

Error Reduction from Stacked Regressions.
CoRR, 2023

Sharp Convergence Rates for Matching Pursuit.
CoRR, 2023

2022
On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation.
CoRR, 2022

2021
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing.
IEEE Trans. Inf. Theory, 2021

Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit.
CoRR, 2021

Universal Consistency of Decision Trees for High Dimensional Additive Models.
CoRR, 2021

Good Classifiers are Abundant in the Interpolating Regime.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Nonparametric Variable Screening with Optimal Decision Stumps.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Sharp Analysis of a Simple Model for Random Forests.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Good linear classifiers are abundant in the interpolating regime.
CoRR, 2020

Sparse Learning with CART.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximization.
IEEE Trans. Inf. Theory, 2019

Global Capacity Measures for Deep ReLU Networks via Path Sampling.
CoRR, 2019

Best Split Nodes for Regression Trees.
CoRR, 2019

Complexity, Statistical Risk, and Metric Entropy of Deep Nets Using Total Path Variation.
CoRR, 2019

2018
Approximation by Combinations of ReLU and Squared ReLU Ridge Functions With ℓ<sup>1</sup> and ℓ<sup>0</sup> Controls.
IEEE Trans. Inf. Theory, 2018

Finite-Sample Risk Bounds for Maximum Likelihood Estimation With Arbitrary Penalties.
IEEE Trans. Inf. Theory, 2018

Approximation and Estimation for High-Dimensional Deep Learning Networks.
CoRR, 2018

Complete Analysis of a Random Forest Model.
CoRR, 2018

Estimating the Number of Connected Components in a Graph via Subgraph Sampling.
CoRR, 2018

Counting Motifs with Graph Sampling.
Proceedings of the Conference On Learning Theory, 2018

2017
Minimax lower bounds for ridge combinations including neural nets.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017


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