Matus Telgarsky

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 52

Top Topics

Latent Variable Models

Combinatorial Auctions

Top Venues

Publications

Clayton Sanford, Daniel Hsu, Matus Telgarsky
Transformers, parallel computation, and logarithmic depth. CoRR (2024)
Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. CoRR (2024)
Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. COLT (2024)
Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram
Spectrum Extraction and Clipping for Implicitly Linear Layers. CoRR (2024)
Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram
Spectrum Extraction and Clipping for Implicitly Linear Layers. AISTATS (2024)
Justin D. Li, Matus Telgarsky
On Achieving Optimal Adversarial Test Error. CoRR (2023)
Matus Telgarsky
Feature selection and low test error in shallow low-rotation ReLU networks. ICLR (2023)
Justin D. Li, Matus Telgarsky
On Achieving Optimal Adversarial Test Error. ICLR (2023)
Clayton Sanford, Daniel J. Hsu, Matus Telgarsky
Representational Strengths and Limitations of Transformers. NeurIPS (2023)
Clayton Sanford, Daniel Hsu, Matus Telgarsky
Representational Strengths and Limitations of Transformers. CoRR (2023)
Matus Telgarsky
Stochastic linear optimization never overfits with quadratically-bounded losses on general data. CoRR (2022)
Miroslav Dudík, Ziwei Ji, Robert E. Schapire, Matus Telgarsky
Convex Analysis at Infinity: An Introduction to Astral Space. CoRR (2022)
Matus Telgarsky
Stochastic linear optimization never overfits with quadratically-bounded losses on general data. COLT (2022)
Yuzheng Hu, Ziwei Ji, Matus Telgarsky
Actor-critic is implicitly biased towards high entropy optimal policies. ICLR (2022)
Matus Telgarsky
Feature selection with gradient descent on two-layer networks in low-rotation regimes. CoRR (2022)
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
Generalization bounds via distillation. CoRR (2021)
Ziwei Ji, Matus Telgarsky
Characterizing the implicit bias via a primal-dual analysis. ALT (2021)
Yuzheng Hu, Ziwei Ji, Matus Telgarsky
Actor-critic is implicitly biased towards high entropy optimal policies. CoRR (2021)
Ziwei Ji, Nathan Srebro, Matus Telgarsky
Fast Margin Maximization via Dual Acceleration. CoRR (2021)
Ziwei Ji, Nathan Srebro, Matus Telgarsky
Fast margin maximization via dual acceleration. ICML (2021)
Ziwei Ji, Justin D. Li, Matus Telgarsky
Early-stopped neural networks are consistent. CoRR (2021)
Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang
Generalization bounds via distillation. ICLR (2021)
Ziwei Ji, Justin D. Li, Matus Telgarsky
Early-stopped neural networks are consistent. NeurIPS (2021)
Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky
Gradient descent follows the regularization path for general losses. COLT (2020)
Ziwei Ji, Matus Telgarsky
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks. ICLR (2020)
Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky
Gradient descent follows the regularization path for general losses. CoRR (2020)
Ziwei Ji, Matus Telgarsky
Directional convergence and alignment in deep learning. NeurIPS (2020)
Ziwei Ji, Matus Telgarsky
Directional convergence and alignment in deep learning. CoRR (2020)
Ziwei Ji, Matus Telgarsky, Ruicheng Xian
Neural tangent kernels, transportation mappings, and universal approximation. ICLR (2020)
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization. CoRR (2019)
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. ICML (2019)
Ziwei Ji, Matus Telgarsky
A refined primal-dual analysis of the implicit bias. CoRR (2019)
Ziwei Ji, Matus Telgarsky
The implicit bias of gradient descent on nonseparable data. COLT (2019)
Ziwei Ji, Matus Telgarsky
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks. CoRR (2019)
Ziwei Ji, Matus Telgarsky, Ruicheng Xian
Neural tangent kernels, transportation mappings, and universal approximation. CoRR (2019)
Ziwei Ji, Matus Telgarsky
Gradient descent aligns the layers of deep linear networks. ICLR (Poster) (2019)
Bolton Bailey, Matus Telgarsky
Size-Noise Tradeoffs in Generative Networks. CoRR (2018)
Ziwei Ji, Matus Telgarsky
Risk and parameter convergence of logistic regression. CoRR (2018)
Ziwei Ji, Matus Telgarsky
Gradient descent aligns the layers of deep linear networks. CoRR (2018)
Ziwei Ji, Ruta Mehta, Matus Telgarsky
Social Welfare and Profit Maximization from Revealed Preferences. WINE (2018)
Bolton Bailey, Matus Telgarsky
Size-Noise Tradeoffs in Generative Networks. NeurIPS (2018)
Matus Telgarsky
Neural Networks and Rational Functions. ICML (2017)
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT (2017)
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR (2017)
Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky
Spectrally-normalized margin bounds for neural networks. CoRR (2017)
Matus Telgarsky
Neural networks and rational functions. CoRR (2017)
Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky
Spectrally-normalized margin bounds for neural networks. NIPS (2017)
Ziwei Ji, Ruta Mehta, Matus Telgarsky
Social Welfare and Profit Maximization from Revealed Preferences. CoRR (2017)
Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky
Rate of Price Discovery in Iterative Combinatorial Auctions. EC (2016)
Matus Telgarsky
Benefits of depth in neural networks. CoRR (2016)
Matus Telgarsky
benefits of depth in neural networks. COLT (2016)
Daniel J. Hsu, Matus Telgarsky
Greedy bi-criteria approximations for k-medians and k-means. CoRR (2016)
Matus Telgarsky
Representation Benefits of Deep Feedforward Networks. CoRR (2015)
Matus Telgarsky, Miroslav Dudík, Robert E. Schapire
Convex Risk Minimization and Conditional Probability Estimation. CoRR (2015)
Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky
Rate of Price Discovery in Iterative Combinatorial Auctions. CoRR (2015)
Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). ALT (2015)
Matus Telgarsky, Miroslav Dudík
Convex Risk Minimization and Conditional Probability Estimation. COLT (2015)
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR (2014)
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS (2014)
Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15 (1) (2014)
Matus Telgarsky, Sanjoy Dasgupta
Moment-based Uniform Deviation Bounds for $k$-means and Friends. CoRR (2013)
Matus Telgarsky, Sanjoy Dasgupta
-means and Friends. NIPS (2013)
Matus Telgarsky
Margins, Shrinkage, and Boosting. ICML (2) (2013)
Matus Telgarsky
Boosting with the Logistic Loss is Consistent. COLT (2013)
Matus Telgarsky
Dirichlet draws are sparse with high probability CoRR (2013)
Matus Telgarsky
Boosting with the Logistic Loss is Consistent CoRR (2013)
Matus Telgarsky
Margins, Shrinkage, and Boosting CoRR (2013)
Matus Telgarsky
Statistical Consistency of Finite-dimensional Unregularized Linear Classification CoRR (2012)
Matus Telgarsky
A Primal-Dual Convergence Analysis of Boosting. J. Mach. Learn. Res. 13 (2012)
Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
Tensor decompositions for learning latent variable models CoRR (2012)
Matus Telgarsky, Sanjoy Dasgupta
Agglomerative Bregman Clustering. ICML (2012)
Matus Telgarsky
Blackwell Approachability and Minimax Theory CoRR (2011)
Matus Telgarsky
The Convergence Rate of AdaBoost and Friends CoRR (2011)
Matus Telgarsky
The Fast Convergence of Boosting. NIPS (2011)
Matus Telgarsky, Andrea Vattani
Hartigan's Method: k-means Clustering without Voronoi. AISTATS (2010)
Matus Telgarsky, John D. Lafferty
Signal Decomposition using Multiscale Admixture Models. ICASSP (2) (2007)