← Authors

Matus Telgarsky

26 papers · 2010–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (15) 🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (21) 🐺 Lone Wolf (7) πŸ† Keyword Champion (2) πŸ’Ž Century Club (26) πŸ—ƒοΈ Keyword Collector (68) πŸ”₯ Unstoppable (7) πŸš€ Conference Pioneer

Conferences

COLT (8) ICLR (7) ICML (6) AISTATS (2) JMLR (2) ALT (1)

Top co-authors

Ziwei Ji (9) Daniel Hsu (4) Jingfeng Wu (2) Miroslav DudΓ­k (2) Bin Yu (2) Alexander Rakhlin (1) Yuzheng Hu (1) Justin D. Li (1) Rong Ge (1) Lan Wang (1)

Keywords

gradient descent (5) empirical risk minimization (3) convex optimization (3) convergence rate (3) optimal transport (2) logistic regression (2) approximation theory (2) mirror descent (2) generalization bound (2) logistic loss (2) dual optimization (2) implicit bia (2) stochastic optimization (2) margin maximization (2) maximum margin (2) convergence analysis (1) primal-dual optimization (1) non-convex optimization (1) batch normalization (1) statistical consistency (1)

Papers

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression ICML 2025
Spectrum Extraction and Clipping for Implicitly Linear Layers AISTATS 2024
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency COLT 2024
Transformers, parallel computation, and logarithmic depth ICML 2024
On Achieving Optimal Adversarial Test Error ICLR 2023
Feature selection and low test error in shallow low-rotation ReLU networks ICLR 2023
Actor-critic is implicitly biased towards high entropy optimal policies ICLR 2022
Stochastic linear optimization never overfits with quadratically-bounded losses on general data COLT 2022
Fast margin maximization via dual acceleration ICML 2021
Characterizing the implicit bias via a primal-dual analysis ALT 2021
Generalization bounds via distillation ICLR 2021
Neural tangent kernels, transportation mappings, and universal approximation ICLR 2020
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks ICLR 2020
Gradient descent follows the regularization path for general losses COLT 2020
Gradient descent aligns the layers of deep linear networks ICLR 2019
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization ICML 2019
The implicit bias of gradient descent on nonseparable data COLT 2019
Neural Networks and Rational Functions ICML 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis COLT 2017
benefits of depth in neural networks COLT 2016
Convex Risk Minimization and Conditional Probability Estimation COLT 2015
Tensor Decompositions for Learning Latent Variable Models JMLR 2014
Margins, Shrinkage, and Boosting ICML 2013
Boosting with the Logistic Loss is Consistent COLT 2013
A Primal-Dual Convergence Analysis of Boosting JMLR 2012
Hartigan’s Method: k-means Clustering without Voronoi AISTATS 2010