Signal Decomposition using Multiscale Admixture Models. - Matus Telgarsky; John D. Lafferty (ICASSP (2), 2007)

Hartigan's Method: k-means Clustering without Voronoi. - Matus Telgarsky; Andrea Vattani (AISTATS, 2010)

The Fast Convergence of Boosting. - Matus Telgarsky (NIPS, 2011)

The Convergence Rate of AdaBoost and Friends - Matus Telgarsky (CoRR, 2011)

Blackwell Approachability and Minimax Theory - Matus Telgarsky (CoRR, 2011)

Agglomerative Bregman Clustering. - Matus Telgarsky; Sanjoy Dasgupta (ICML, 2012)

Statistical Consistency of Finite-dimensional Unregularized Linear Classification - Matus Telgarsky (CoRR, 2012)

A Primal-Dual Convergence Analysis of Boosting. - Matus Telgarsky (J. Mach. Learn. Res., 2012)

Boosting with the Logistic Loss is Consistent. - Matus Telgarsky (COLT, 2013)

Margins, Shrinkage, and Boosting. - Matus Telgarsky (ICML (2), 2013)

Moment-based Uniform Deviation Bounds for k-means and Friends. - Matus Telgarsky; Sanjoy Dasgupta (NIPS, 2013)

Moment-based Uniform Deviation Bounds for $k$-means and Friends. - Matus Telgarsky; Sanjoy Dasgupta (CoRR, 2013)

Dirichlet draws are sparse with high probability - Matus Telgarsky (CoRR, 2013)

Duality and Data Dependence in Boosting /. - Matus Telgarsky (2013)

Scalable Non-linear Learning with Adaptive Polynomial Expansions. - Alekh Agarwal; Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Matus Telgarsky (NIPS, 2014)

Scalable Nonlinear Learning with Adaptive Polynomial Expansions. - Alekh Agarwal; Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Matus Telgarsky (CoRR, 2014)

Tensor decompositions for learning latent variable models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (J. Mach. Learn. Res., 2014)

Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). - Anima Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (ALT, 2015)

Convex Risk Minimization and Conditional Probability Estimation. - Matus Telgarsky; Miroslav Dudík (COLT, 2015)

Representation Benefits of Deep Feedforward Networks. - Matus Telgarsky (CoRR, 2015)

Convex Risk Minimization and Conditional Probability Estimation. - Matus Telgarsky; Miroslav Dudík; Robert E. Schapire (CoRR, 2015)

benefits of depth in neural networks. - Matus Telgarsky (COLT, 2016)

Rate of Price Discovery in Iterative Combinatorial Auctions. - Jacob D. Abernethy; Sébastien Lahaie; Matus Telgarsky (EC, 2016)

Greedy bi-criteria approximations for k-medians and k-means. - Daniel J. Hsu; Matus Telgarsky (CoRR, 2016)

Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. - Maxim Raginsky; Alexander Rakhlin; Matus Telgarsky (COLT, 2017)

Neural Networks and Rational Functions. - Matus Telgarsky (ICML, 2017)

Spectrally-normalized margin bounds for neural networks. - Peter L. Bartlett; Dylan J. Foster; Matus Telgarsky (NIPS, 2017)

Size-Noise Tradeoffs in Generative Networks. - Bolton Bailey; Matus Telgarsky (NeurIPS, 2018)

Social Welfare and Profit Maximization from Revealed Preferences. - Ziwei Ji; Ruta Mehta; Matus Telgarsky (WINE, 2018)

Risk and parameter convergence of logistic regression. - Ziwei Ji; Matus Telgarsky (CoRR, 2018)

The implicit bias of gradient descent on nonseparable data. - Ziwei Ji; Matus Telgarsky (COLT, 2019)

Gradient descent aligns the layers of deep linear networks. - Ziwei Ji; Matus Telgarsky (ICLR (Poster), 2019)

A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. - Yucheng Chen; Matus Telgarsky; Chao Zhang 014; Bolton Bailey; Daniel Hsu 001; Jian Peng 001 (ICML, 2019)

A refined primal-dual analysis of the implicit bias. - Ziwei Ji; Matus Telgarsky (CoRR, 2019)

Gradient descent follows the regularization path for general losses. - Ziwei Ji; Miroslav Dudík; Robert E. Schapire; Matus Telgarsky (COLT, 2020)

Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks. - Ziwei Ji; Matus Telgarsky (ICLR, 2020)

Neural tangent kernels, transportation mappings, and universal approximation. - Ziwei Ji; Matus Telgarsky; Ruicheng Xian (ICLR, 2020)

Directional convergence and alignment in deep learning. - Ziwei Ji; Matus Telgarsky (NeurIPS, 2020)

Characterizing the implicit bias via a primal-dual analysis. - Ziwei Ji; Matus Telgarsky (ALT, 2021)

Generalization bounds via distillation. - Daniel Hsu 001; Ziwei Ji; Matus Telgarsky; Lan Wang (ICLR, 2021)

Fast margin maximization via dual acceleration. - Ziwei Ji; Nathan Srebro; Matus Telgarsky (ICML, 2021)

Early-stopped neural networks are consistent. - Ziwei Ji; Justin D. Li; Matus Telgarsky (NeurIPS, 2021)

Stochastic linear optimization never overfits with quadratically-bounded losses on general data. - Matus Telgarsky (COLT, 2022)

Actor-critic is implicitly biased towards high entropy optimal policies. - Yuzheng Hu; Ziwei Ji; Matus Telgarsky (ICLR, 2022)

Convex Analysis at Infinity: An Introduction to Astral Space. - Miroslav Dudík; Ziwei Ji; Robert E. Schapire; Matus Telgarsky (CoRR, 2022)

Feature selection with gradient descent on two-layer networks in low-rotation regimes. - Matus Telgarsky (CoRR, 2022)

On Achieving Optimal Adversarial Test Error. - Justin D. Li; Matus Telgarsky (ICLR, 2023)

Feature selection and low test error in shallow low-rotation ReLU networks. - Matus Telgarsky (ICLR, 2023)

Representational Strengths and Limitations of Transformers. - Clayton Sanford; Daniel J. Hsu; Matus Telgarsky (NeurIPS, 2023)

Spectrum Extraction and Clipping for Implicitly Linear Layers. - Ali Ebrahimpour Boroojeny; Matus Telgarsky; Hari Sundaram (AISTATS, 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 (COLT, 2024)

Transformers, parallel computation, and logarithmic depth. - Clayton Sanford; Daniel Hsu 001; Matus Telgarsky (ICML, 2024)

One-layer transformers fail to solve the induction heads task. - Clayton Sanford; Daniel Hsu 001; Matus Telgarsky (CoRR, 2024)

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. - Jingfeng Wu; Peter L. Bartlett; Matus Telgarsky; Bin Yu (CoRR, 2025)