Nati Srebro - RankMe

51 papers A* 27A 1Journal 3Unranked 20

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