Matus Telgarsky

1. Spectrally-normalized margin bounds for neural networks
   2017/06/26 by Peter L. Bartlett, Dylan J. Foster, Bartlett, Peter +3 · 83 citations
   Computer Science · #Neural Networks and Applications #Machine Learning and ELM #Face and Expression Recognition
2. Benefits of depth in neural networks
   2016/02/14 by Matus Telgarsky, Telgarsky, Matus · 33 citations
   Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)
3. Non-convex learning via Stochastic Gradient Langevin Dynamics: a\n nonasymptotic analysis
   2017/02/13 by Maxim Raginsky, Alexander Rakhlin, Raginsky, Maxim +3 · 27 citations
   Computer Science · Engineering · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Probability (math.PR) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
4. Gradient descent aligns the layers of deep linear networks
   2018/10/04 by Ziwei Ji, Ji, Ziwei, Matus Telgarsky +1 · 16 citations
   Engineering · Computer Science · Medicine · #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Advanced Neuroimaging Techniques and Applications
5. Directional convergence and alignment in deep learning
   2020/06/11 by Ziwei Ji, Matus Telgarsky, Ji, Ziwei +1 · 16 citations
   Computer Science · #Stochastic Gradient Optimization Techniques #Domain Adaptation and Few-Shot Learning #Adversarial Robustness in Machine Learning
6. Representational Strengths and Limitations of Transformers
   2023/06/05 by Clayton Sanford, Daniel Hsu, Sanford, Clayton +3 · 11 citations
   Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Advanced Neural Network Applications #Advanced Memory and Neural Computing
7. Gradient descent follows the regularization path for general losses
   2020/06/19 by Ziwei Ji, Ji, Ziwei, Miroslav Dudı́k +5 · 4 citations
   Computer Science · Engineering · #Advancements in Semiconductor Devices and Circuit Design #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
8. A gradual, semi-discrete approach to generative network training via\n explicit Wasserstein minimization
   2019/06/08 by Yucheng Chen, Chen, Yucheng, Matus Telgarsky +9 · 3 citations
   Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #Adversarial Robustness in Machine Learning #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)
9. Generalization bounds via distillation
   2021/04/12 by Daniel Hsu, Hsu, Daniel, Ziwei Ji +5 · 3 citations
   Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
10. Early-stopped neural networks are consistent
    2021/06/10 by Ziwei Ji, Justin D. Li, Ji, Ziwei +3 · 2 citations
    Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Stochastic Gradient Optimization Techniques
11. Actor-critic is implicitly biased towards high entropy optimal policies
    2021/10/21 by Yuzheng Hu, Hu, Yuzheng, Ziwei Ji +3 · 2 citations
    Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Reinforcement Learning in Robotics
12. Dirichlet draws are sparse with high probability
    2013/01/21 by Matus Telgarsky, Telgarsky, Matus · 1 citation
    Computer Science · Biochemistry, Genetics and Molecular Biology · Mathematics · #Bayesian Methods and Mixture Models #Diffusion and Search Dynamics #Stochastic processes and statistical mechanics
13. Neural tangent kernels, transportation mappings, and universal approximation
    2019/10/15 by Ziwei Ji, Matus Telgarsky, Ji, Ziwei +3 · 1 citation
    Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
14. Moment-based Uniform Deviation Bounds for k-means and Friends
    2013/11/08 by Matus Telgarsky, Telgarsky, Matus, Sanjoy Dasgupta +1 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Methods and Inference #Statistical Methods and Bayesian Inference
15. Greedy bi-criteria approximations for k-medians and k-means
    2016/07/21 by Daniel Hsu, Matus Telgarsky, Hsu, Daniel +1 · 1 citation
    Computer Science · Engineering · #Complexity and Algorithms in Graphs #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques
16. Understanding Reasoning from Pretraining to Post-Training
    2026/07/17 by Jingyan Shen, Ang Li, Salman Rahman +4 · 1 voice
    #cs.LG #cs.AI #cs.CL