Nati Srebro

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 36

Top Topics

Tight Complexity Bounds

Generalized Linear

Convex Optimization

Top Venues

Publications

Anmol Kabra, Mina Karzand, Tosca Lechner, Nati Srebro, Serena Lutong Wang
Score Design for Multi-Criteria Incentivization. FORC (2024)
Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro
Uniform Convergence with Square-Root Lipschitz Loss. NeurIPS (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS (2023)
Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro
When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS (2023)
Amit Daniely, Nati Srebro, Gal Vardi
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS (2023)
Amit Daniely, Nati Srebro, Gal Vardi
Most Neural Networks Are Almost Learnable. NeurIPS (2023)
Omar Montasser, Steve Hanneke, Nati Srebro
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. NeurIPS (2022)
Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS (2022)
Gal Vardi, Ohad Shamir, Nati Srebro
On Margin Maximization in Linear and ReLU Networks. NeurIPS (2022)
Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS (2022)
Gene Li, Cong Ma, Nati Srebro
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets. NeurIPS (2022)
Gene Li, Junbo Li, Anmol Kabra, Nati Srebro, Zhaoran Wang, Zhuoran Yang
Exponential Family Model-Based Reinforcement Learning via Score Matching. NeurIPS (2022)
Gal Vardi, Ohad Shamir, Nati Srebro
The Sample Complexity of One-Hidden-Layer Neural Networks. NeurIPS (2022)
Itay Evron, Edward Moroshko, Rachel Ward, Nati Srebro, Daniel Soudry
How catastrophic can catastrophic forgetting be in linear regression? CoRR (2022)
Idan Amir, Roi Livni, Nati Srebro
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. NeurIPS (2022)
Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro
Understanding the Eluder Dimension. NeurIPS (2022)
Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro
Minibatch vs Local SGD for Heterogeneous Distributed Learning. NeurIPS (2020)
Lijia Zhou, Danica J. Sutherland, Nati Srebro
On Uniform Convergence and Low-Norm Interpolation Learning. NeurIPS (2020)
Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. NeurIPS (2020)
Omar Montasser, Steve Hanneke, Nati Srebro
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. NeurIPS (2020)
Nandana Sengupta, Nati Srebro, James Evans
Simple Surveys: Response Retrieval Inspired by Recommendation Systems. CoRR (2019)
Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro
The Everlasting Database: Statistical Validity at a Fair Price. NeurIPS (2018)
Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. NeurIPS (2018)
Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro
On preserving non-discrimination when combining expert advice. NeurIPS (2018)
Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro
Implicit Bias of Gradient Descent on Linear Convolutional Networks. NeurIPS (2018)
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro
Exploring Generalization in Deep Learning. NIPS (2017)
Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro
Stochastic Approximation for Canonical Correlation Analysis. NIPS (2017)
Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. AISTATS (2017)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Implicit Regularization in Matrix Factorization. NIPS (2017)
Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht
The Marginal Value of Adaptive Gradient Methods in Machine Learning. NIPS (2017)
Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Global Optimality of Local Search for Low Rank Matrix Recovery. NIPS (2016)
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. NIPS (2016)
Moritz Hardt, Eric Price, Nati Srebro
Equality of Opportunity in Supervised Learning. NIPS (2016)
Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. NIPS (2016)
Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi
Normalized Spectral Map Synchronization. NIPS (2016)
Blake E. Woodworth, Nati Srebro
Tight Complexity Bounds for Optimizing Composite Objectives. NIPS (2016)
Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour
The Power of Asymmetry in Binary Hashing. NIPS (2013)
Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro
Mini-Batch Primal and Dual Methods for SVMs. ICML (3) (2013)
Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro
Learning Optimally Sparse Support Vector Machines. ICML (1) (2013)
Raman Arora, Andrew Cotter, Nati Srebro
Stochastic Optimization of PCA with Capped MSG. NIPS (2013)
Sivan Sabato, Anand D. Sarwate, Nati Srebro
Auditing: Active Learning with Outcome-Dependent Query Costs. NIPS (2013)
Sivan Sabato, Nati Srebro, Naftali Tishby
Characterizing the Sample Complexity of Large-Margin Learning With Second-Order Statistics CoRR (2012)
Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
Better Mini-Batch Algorithms via Accelerated Gradient Methods. NIPS (2011)
Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro
Learning with the weighted trace-norm under arbitrary sampling distributions. NIPS (2011)
Elad Hazan, Tomer Koren, Nati Srebro
Beating SGD: Learning SVMs in Sublinear Time. NIPS (2011)
Nati Srebro, Karthik Sridharan, Ambuj Tewari
On the Universality of Online Mirror Descent. NIPS (2011)
Percy Liang, Nati Srebro
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning. ICML (2010)