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ORCIDMatus Telgarsky
Person information
- affiliation: University of Illinois at Urbana-Champaign, Department of Computer Science, IL, USA
- affiliation: Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
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2020 – today
- 2024
[c35]Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram:
Spectrum Extraction and Clipping for Implicitly Linear Layers. AISTATS 2024: 2971-2979- [c34]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: 5019-5073 - [i39]Clayton Sanford, Daniel Hsu, Matus Telgarsky:
Transformers, parallel computation, and logarithmic depth. CoRR abs/2402.09268 (2024) - [i38]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 abs/2402.15926 (2024) - [i37]Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram:
Spectrum Extraction and Clipping for Implicitly Linear Layers. CoRR abs/2402.16017 (2024) - 2023
- [c33]Justin D. Li, Matus Telgarsky:
On Achieving Optimal Adversarial Test Error. ICLR 2023 - [c32]Matus Telgarsky:
Feature selection and low test error in shallow low-rotation ReLU networks. ICLR 2023 - [c31]Clayton Sanford, Daniel J. Hsu, Matus Telgarsky:
Representational Strengths and Limitations of Transformers. NeurIPS 2023 - [i36]Clayton Sanford, Daniel Hsu, Matus Telgarsky:
Representational Strengths and Limitations of Transformers. CoRR abs/2306.02896 (2023) - [i35]Justin D. Li, Matus Telgarsky:
On Achieving Optimal Adversarial Test Error. CoRR abs/2306.07544 (2023) - 2022
- [c30]Matus Telgarsky:
Stochastic linear optimization never overfits with quadratically-bounded losses on general data. COLT 2022: 5453-5488 - [c29]Yuzheng Hu, Ziwei Ji, Matus Telgarsky:
Actor-critic is implicitly biased towards high entropy optimal policies. ICLR 2022 - [i34]Matus Telgarsky:
Stochastic linear optimization never overfits with quadratically-bounded losses on general data. CoRR abs/2202.06915 (2022) - [i33]Miroslav Dudík, Ziwei Ji, Robert E. Schapire, Matus Telgarsky:
Convex Analysis at Infinity: An Introduction to Astral Space. CoRR abs/2205.03260 (2022) - [i32]Matus Telgarsky:
Feature selection with gradient descent on two-layer networks in low-rotation regimes. CoRR abs/2208.02789 (2022) - 2021
- [c28]Ziwei Ji, Matus Telgarsky:
Characterizing the implicit bias via a primal-dual analysis. ALT 2021: 772-804 - [c27]Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang:
Generalization bounds via distillation. ICLR 2021 - [c26]Ziwei Ji, Nathan Srebro, Matus Telgarsky:
Fast margin maximization via dual acceleration. ICML 2021: 4860-4869 - [c25]Ziwei Ji, Justin D. Li, Matus Telgarsky:
Early-stopped neural networks are consistent. NeurIPS 2021: 1805-1817 - [i31]Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang:
Generalization bounds via distillation. CoRR abs/2104.05641 (2021) - [i30]Ziwei Ji, Justin D. Li, Matus Telgarsky:
Early-stopped neural networks are consistent. CoRR abs/2106.05932 (2021) - [i29]Ziwei Ji, Nathan Srebro, Matus Telgarsky:
Fast Margin Maximization via Dual Acceleration. CoRR abs/2107.00595 (2021) - [i28]Yuzheng Hu, Ziwei Ji, Matus Telgarsky:
Actor-critic is implicitly biased towards high entropy optimal policies. CoRR abs/2110.11280 (2021) - 2020
- [c24]Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky:
Gradient descent follows the regularization path for general losses. COLT 2020: 2109-2136 - [c23]Ziwei Ji, Matus Telgarsky:
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks. ICLR 2020 - [c22]Ziwei Ji, Matus Telgarsky, Ruicheng Xian:
Neural tangent kernels, transportation mappings, and universal approximation. ICLR 2020 - [c21]Ziwei Ji, Matus Telgarsky:
Directional convergence and alignment in deep learning. NeurIPS 2020 - [i27]Ziwei Ji, Matus Telgarsky:
Directional convergence and alignment in deep learning. CoRR abs/2006.06657 (2020) - [i26]Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky:
Gradient descent follows the regularization path for general losses. CoRR abs/2006.11226 (2020)
2010 – 2019
- 2019
- [c20]Ziwei Ji, Matus Telgarsky:
The implicit bias of gradient descent on nonseparable data. COLT 2019: 1772-1798 - [c19]Ziwei Ji, Matus Telgarsky:
Gradient descent aligns the layers of deep linear networks. ICLR (Poster) 2019 - [c18]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: 1071-1080 - [i25]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 abs/1906.03471 (2019) - [i24]Ziwei Ji, Matus Telgarsky:
A refined primal-dual analysis of the implicit bias. CoRR abs/1906.04540 (2019) - [i23]Ziwei Ji, Matus Telgarsky:
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks. CoRR abs/1909.12292 (2019) - [i22]Ziwei Ji, Matus Telgarsky, Ruicheng Xian:
Neural tangent kernels, transportation mappings, and universal approximation. CoRR abs/1910.06956 (2019) - 2018
- [c17]Bolton Bailey, Matus Telgarsky:
Size-Noise Tradeoffs in Generative Networks. NeurIPS 2018: 6490-6500 - [c16]Ziwei Ji, Ruta Mehta, Matus Telgarsky:
Social Welfare and Profit Maximization from Revealed Preferences. WINE 2018: 264-281 - [i21]Ziwei Ji, Matus Telgarsky:
Risk and parameter convergence of logistic regression. CoRR abs/1803.07300 (2018) - [i20]Ziwei Ji, Matus Telgarsky:
Gradient descent aligns the layers of deep linear networks. CoRR abs/1810.02032 (2018) - [i19]Bolton Bailey, Matus Telgarsky:
Size-Noise Tradeoffs in Generative Networks. CoRR abs/1810.11158 (2018) - 2017
- [c15]Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky:
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT 2017: 1674-1703 - [c14]Matus Telgarsky:
Neural Networks and Rational Functions. ICML 2017: 3387-3393 - [c13]Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky:
Spectrally-normalized margin bounds for neural networks. NIPS 2017: 6240-6249 - [i18]Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky:
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR abs/1702.03849 (2017) - [i17]Matus Telgarsky:
Neural networks and rational functions. CoRR abs/1706.03301 (2017) - [i16]Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky:
Spectrally-normalized margin bounds for neural networks. CoRR abs/1706.08498 (2017) - [i15]Ziwei Ji, Ruta Mehta, Matus Telgarsky:
Social Welfare and Profit Maximization from Revealed Preferences. CoRR abs/1711.02211 (2017) - 2016
- [c12]Matus Telgarsky:
benefits of depth in neural networks. COLT 2016: 1517-1539 - [c11]Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky:
Rate of Price Discovery in Iterative Combinatorial Auctions. EC 2016: 809 - [i14]Matus Telgarsky:
Benefits of depth in neural networks. CoRR abs/1602.04485 (2016) - [i13]Daniel J. Hsu, Matus Telgarsky:
Greedy bi-criteria approximations for k-medians and k-means. CoRR abs/1607.06203 (2016) - 2015
- [c10]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: 19-38 - [c9]Matus Telgarsky, Miroslav Dudík:
Convex Risk Minimization and Conditional Probability Estimation. COLT 2015: 1629-1682 - [i12]Matus Telgarsky, Miroslav Dudík, Robert E. Schapire:
Convex Risk Minimization and Conditional Probability Estimation. CoRR abs/1506.04513 (2015) - [i11]Matus Telgarsky:
Representation Benefits of Deep Feedforward Networks. CoRR abs/1509.08101 (2015) - [i10]Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky:
Rate of Price Discovery in Iterative Combinatorial Auctions. CoRR abs/1511.06017 (2015) - 2014
- [j2]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): 2773-2832 (2014) - [c8]Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky:
Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS 2014: 2051-2059 - [i9]Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky:
Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR abs/1410.0440 (2014) - 2013
- [b1]Matus Telgarsky:
Duality and Data Dependence in Boosting /. University of California, San Diego, USA, 2013 - [c7]Matus Telgarsky:
Boosting with the Logistic Loss is Consistent. COLT 2013: 911-965 - [c6]Matus Telgarsky:
Margins, Shrinkage, and Boosting. ICML (2) 2013: 307-315 - [c5]Matus Telgarsky, Sanjoy Dasgupta:
Moment-based Uniform Deviation Bounds for k-means and Friends. NIPS 2013: 2940-2948 - [i8]Matus Telgarsky:
Dirichlet draws are sparse with high probability. CoRR abs/1301.4917 (2013) - [i7]Matus Telgarsky:
Margins, Shrinkage, and Boosting. CoRR abs/1303.4172 (2013) - [i6]Matus Telgarsky:
Boosting with the Logistic Loss is Consistent. CoRR abs/1305.2648 (2013) - [i5]Matus Telgarsky, Sanjoy Dasgupta:
Moment-based Uniform Deviation Bounds for $k$-means and Friends. CoRR abs/1311.1903 (2013) - 2012
- [j1]Matus Telgarsky:
A Primal-Dual Convergence Analysis of Boosting. J. Mach. Learn. Res. 13: 561-606 (2012) - [c4]Matus Telgarsky, Sanjoy Dasgupta:
Agglomerative Bregman Clustering. ICML 2012 - [i4]Matus Telgarsky:
Statistical Consistency of Finite-dimensional Unregularized Linear Classification. CoRR abs/1206.3072 (2012) - [i3]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor decompositions for learning latent variable models. CoRR abs/1210.7559 (2012) - 2011
- [c3]Matus Telgarsky:
The Fast Convergence of Boosting. NIPS 2011: 1593-1601 - [i2]Matus Telgarsky:
The Convergence Rate of AdaBoost and Friends. CoRR abs/1101.4752 (2011) - [i1]Matus Telgarsky:
Blackwell Approachability and Minimax Theory. CoRR abs/1110.1514 (2011) - 2010
- [c2]Matus Telgarsky, Andrea Vattani:
Hartigan's Method: k-means Clustering without Voronoi. AISTATS 2010: 820-827
2000 – 2009
- 2007
- [c1]Matus Telgarsky, John D. Lafferty:
Signal Decomposition using Multiscale Admixture Models. ICASSP (2) 2007: 449-452
Coauthor Index
aka: Daniel J. Hsu
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last updated on 2024-07-25 19:30 CEST by the dblp team
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