Telgarsky, Matus

29 publications

ICML 2025 Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression Jingfeng Wu, Peter Bartlett, Matus Telgarsky, Bin Yu

COLT 2024 Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

AISTATS 2024 Spectrum Extraction and Clipping for Implicitly Linear Layers Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram

ICML 2024 Transformers, Parallel Computation, and Logarithmic Depth Clayton Sanford, Daniel Hsu, Matus Telgarsky

ICLR 2023 Feature Selection and Low Test Error in Shallow Low-Rotation ReLU Networks Matus Telgarsky

ICLR 2023 On Achieving Optimal Adversarial Test Error Justin D. Li, Matus Telgarsky

NeurIPSW 2023 Spectrum Extraction and Clipping for Implicitly Linear Layers Ali Ebrahimpour-Boroojeny, Matus Telgarsky, Hari Sundaram

ICLR 2022 Actor-Critic Is Implicitly Biased Towards High Entropy Optimal Policies Yuzheng Hu, Ziwei Ji, Matus Telgarsky

COLT 2022 Stochastic Linear Optimization Never Overfits with Quadratically-Bounded Losses on General Data Matus Telgarsky

ALT 2021 Characterizing the Implicit Bias via a Primal-Dual Analysis Ziwei Ji, Matus Telgarsky

ICML 2021 Fast Margin Maximization via Dual Acceleration Ziwei Ji, Nathan Srebro, Matus Telgarsky

ICLR 2021 Generalization Bounds via Distillation Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang

COLT 2020 Gradient Descent Follows the Regularization Path for General Losses Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky

ICLR 2020 Neural Tangent Kernels, Transportation Mappings, and Universal Approximation Ziwei Ji, Matus Telgarsky, Ruicheng Xian

ICLR 2020 Polylogarithmic Width Suffices for Gradient Descent to Achieve Arbitrarily Small Test Error with Shallow ReLU Networks Ziwei Ji, Matus Telgarsky

ICML 2019 A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng

ICLR 2019 Gradient Descent Aligns the Layers of Deep Linear Networks Ziwei Ji, Matus Telgarsky

COLT 2019 The Implicit Bias of Gradient Descent on Nonseparable Data Ziwei Ji, Matus Telgarsky

ICML 2017 Neural Networks and Rational Functions Matus Telgarsky

COLT 2017 Non-Convex Learning via Stochastic Gradient Langevin Dynamics: A Nonasymptotic Analysis Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky

COLT 2016 Benefits of Depth in Neural Networks Matus Telgarsky

COLT 2015 Convex Risk Minimization and Conditional Probability Estimation Matus Telgarsky, Miroslav Dudík

ALT 2015 Tensor Decompositions for Learning Latent Variable Models (a Survey for ALT) Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky

JMLR 2014 Tensor Decompositions for Learning Latent Variable Models Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky

COLT 2013 Boosting with the Logistic Loss Is Consistent Matus Telgarsky

ICML 2013 Margins, Shrinkage, and Boosting Matus Telgarsky

JMLR 2012 A Primal-Dual Convergence Analysis of Boosting Matus Telgarsky

ICML 2012 Agglomerative Bregman Clustering Matus Telgarsky, Sanjoy Dasgupta

AISTATS 2010 Hartigan’s Method: K-Means Clustering Without Voronoi Matus Telgarsky, Andrea Vattani