Nathan Srebro

ORCID

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 157

Top Venues

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. COLT (2024)
Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. CoRR (2024)
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman
Metalearning with Very Few Samples Per Task. COLT (2024)
Nikolaos Tsilivis, Natalie Frank, Nathan Srebro, Julia Kempe
The Price of Implicit Bias in Adversarially Robust Generalization. CoRR (2024)
Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. CoRR (2024)
Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. ICLR (2024)
Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. CoRR (2024)
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. COLT (2024)
Nirmit Joshi, Gal Vardi, Nathan Srebro
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. ICLR (2024)
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. CoRR (2024)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR (2023)
Naren Sarayu Manoj, Nathan Srebro
Shortest Program Interpolation Learning. COLT (2023)
Naren Sarayu Manoj, Nathan Srebro
Interpolation Learning With Minimum Description Length. CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR (2023)
Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro
Federated Online and Bandit Convex Optimization. ICML (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. COLT (2023)
Amit Daniely, Nathan Srebro, Gal Vardi
Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR (2023)
Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. CoRR (2023)
Nirmit Joshi, Gal Vardi, Nathan Srebro
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. CoRR (2023)
Cédric Gerbelot, Avetik Karagulyan, Stefani Karp, Kavya Ravichandran, Menachem Stern, Nathan Srebro
Applying statistical learning theory to deep learning. CoRR (2023)
Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
Continual Learning in Linear Classification on Separable Data. ICML (2023)
Amit Daniely, Nathan Srebro, Gal Vardi
Most Neural Networks Are Almost Learnable. CoRR (2023)
Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
Continual Learning in Linear Classification on Separable Data. CoRR (2023)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower bounds for non-convex stochastic optimization. Math. Program. 199 (1) (2023)
Lijia Zhou, Zhen Dai, Frederic Koehler, Nathan Srebro
Uniform Convergence with Square-Root Lipschitz Loss. CoRR (2023)
Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
When is Agnostic Reinforcement Learning Statistically Tractable? CoRR (2023)
Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry
Implicit Bias of the Step Size in Linear Diagonal Neural Networks. ICML (2022)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI (2022)
Omar Montasser, Steve Hanneke, Nathan Srebro
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR (2022)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR (2022)
Gene Li, Cong Ma, Nathan Srebro
Confidence Sets. CoRR (2022)
Idan Amir, Roi Livni, Nathan Srebro
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. CoRR (2022)
Itay Evron, Edward Moroshko, Rachel Ward, Nathan Srebro, Daniel Soudry
How catastrophic can catastrophic forgetting be in linear regression? COLT (2022)
Gal Vardi, Ohad Shamir, Nathan Srebro
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR (2022)
Omar Montasser, Steve Hanneke, Nathan Srebro
Transductive Robust Learning Guarantees. AISTATS (2022)
Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR (2022)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR (2021)
Blake E. Woodworth, Nathan Srebro
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. NeurIPS (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS (2021)
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. CoRR (2021)
Omar Montasser, Steve Hanneke, Nathan Srebro
Adversarially Robust Learning with Unknown Perturbation Sets. CoRR (2021)
Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác
An accelerated communication-efficient primal-dual optimization framework for structured machine learning. Optim. Methods Softw. 36 (1) (2021)
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
Does Invariant Risk Minimization Capture Invariance? AISTATS (2021)
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR (2021)
Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
On the Power of Differentiable Learning versus PAC and SQ Learning. CoRR (2021)
Omar Montasser, Steve Hanneke, Nathan Srebro
Adversarially Robust Learning with Unknown Perturbation Sets. COLT (2021)
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. CoRR (2021)
Ziwei Ji, Nathan Srebro, Matus Telgarsky
Fast Margin Maximization via Dual Acceleration. CoRR (2021)
Omar Montasser, Steve Hanneke, Nathan Srebro
Transductive Robust Learning Guarantees. CoRR (2021)
Blake E. Woodworth, Nathan Srebro
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. CoRR (2021)
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
Does Invariant Risk Minimization Capture Invariance? CoRR (2021)
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS (2021)
Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
On the Power of Differentiable Learning versus PAC and SQ Learning. NeurIPS (2021)
Zhen Dai, Mina Karzand, Nathan Srebro
Representation Costs of Linear Neural Networks: Analysis and Design. NeurIPS (2021)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT (2021)
Ziwei Ji, Nathan Srebro, Matus Telgarsky
Fast margin maximization via dual acceleration. ICML (2021)
Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro
Dropout: Explicit Forms and Capacity Control. ICML (2021)
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. ICML (2021)
Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. AISTATS (2021)
Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR (2021)
Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang
Exponential Family Model-Based Reinforcement Learning via Score Matching. CoRR (2021)
Gal Vardi, Ohad Shamir, Nathan Srebro
On Margin Maximization in Linear and ReLU Networks. CoRR (2021)
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. ICML (2021)
Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro
Eluder Dimension and Generalized Rank. CoRR (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR (2021)
Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro
Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow. CoRR (2020)
Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
Kernel and Rich Regimes in Overparametrized Models. CoRR (2020)
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
Efficiently Learning Adversarially Robust Halfspaces with Noise. CoRR (2020)
Lijia Zhou, Danica J. Sutherland, Nathan Srebro
On Uniform Convergence and Low-Norm Interpolation Learning. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? ICML (2020)
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
Efficiently Learning Adversarially Robust Halfspaces with Noise. ICML (2020)
Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro
Dropout: Explicit Forms and Capacity Control. CoRR (2020)
Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. ICLR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Nathan Srebro
Minibatch vs Local SGD for Heterogeneous Distributed Learning. CoRR (2020)
Yossi Arjevani, Ohad Shamir, Nathan Srebro
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. ALT (2020)
Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
Fair Learning with Private Demographic Data. CoRR (2020)
Pritish Kamath, Omar Montasser, Nathan Srebro
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR (2020)
Omar Montasser, Steve Hanneke, Nathan Srebro
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR (2020)
Edward Moroshko, Suriya Gunasekar, Blake E. Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? CoRR (2020)
Keshav Vemuri, Nathan Srebro
Predictive Value Generalization Bounds. CoRR (2020)
Pritish Kamath, Omar Montasser, Nathan Srebro
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT (2020)
Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
Fair Learning with Private Demographic Data. ICML (2020)
Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
Kernel and Rich Regimes in Overparametrized Models. COLT (2020)
Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS (2020)
Omar Montasser, Steve Hanneke, Nathan Srebro
VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR (2019)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower Bounds for Non-Convex Stochastic Optimization. CoRR (2019)
Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
Semi-Cyclic Stochastic Gradient Descent. CoRR (2019)
Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro
How do infinite width bounded norm networks look in function space? CoRR (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT (2019)
Blake E. Woodworth, Nathan Srebro
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. CoRR (2019)
Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. CoRR (2019)
Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro
From Fair Decision Making To Social Equality. FAT (2019)
Omar Montasser, Steve Hanneke, Nathan Srebro
VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR (2019)
Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
Stochastic Canonical Correlation Analysis. J. Mach. Learn. Res. 20 (2019)
Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. CoRR (2019)
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar
Semi-Cyclic Stochastic Gradient Descent. ICML (2019)
Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry
Convergence of Gradient Descent on Separable Data. AISTATS (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Nathan Srebro