Nathan Srebro

According to our database, Nathan Srebro authored at least 200 papers between 2001 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of two.

2024

Provable Tempered Overfitting of Minimal Nets and Typical Nets.

Itamar Harel, William M. Hoza, Gal Vardi, Itay Evron, Nathan Srebro, Daniel Soudry

CoRR, 2024

Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality.

Marko Medvedev, Gal Vardi, Nathan Srebro

CoRR, 2024

On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries.

Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro

CoRR, 2024

The Price of Implicit Bias in Adversarially Robust Generalization.

Nikolaos Tsilivis, Natalie Frank, Nathan Srebro, Julia Kempe

CoRR, 2024

How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers.

Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry

Proceedings of the Forty-first International Conference on Machine Learning, 2024

An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression.

Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Noisy Interpolation Learning with Shallow Univariate ReLU Networks.

Nirmit Joshi, Gal Vardi, Nathan Srebro

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Score Design for Multi-Criteria Incentivization.

Anmol Kabra, Mina Karzand, Tosca Lechner, Nati Srebro, Serena Lutong Wang

Proceedings of the 5th Symposium on Foundations of Responsible Computing, 2024

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication.

Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Depth Separation in Norm-Bounded Infinite-Width Neural Networks.

Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Metalearning with Very Few Samples Per Task.

Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Lower bounds for non-convex stochastic optimization.

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth

Math. Program., May, 2023

Applying statistical learning theory to deep learning.

Cédric Gerbelot, Avetik Karagulyan, Stefani Karp, Kavya Ravichandran, Menachem Stern, Nathan Srebro

CoRR, 2023

Efficiently Learning Neural Networks: What Assumptions May Suffice?

Amit Daniely, Nathan Srebro, Gal Vardi

CoRR, 2023

Interpolation Learning With Minimum Description Length.

Naren Sarayu Manoj, Nathan Srebro

CoRR, 2023

Uniform Convergence with Square-Root Lipschitz Loss.

Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When is Agnostic Reinforcement Learning Statistically Tractable?

Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks.

Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy.

Amit Daniely, Nati Srebro, Gal Vardi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Most Neural Networks Are Almost Learnable.

Amit Daniely, Nati Srebro, Gal Vardi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Federated Online and Bandit Convex Optimization.

Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro

Proceedings of the International Conference on Machine Learning, 2023

Continual Learning in Linear Classification on Separable Data.

Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry

Proceedings of the International Conference on Machine Learning, 2023

Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data.

Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Shortest Program Interpolation Learning.

Naren Sarayu Manoj, Nathan Srebro

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization.

Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Pessimism for Offline Linear Contextual Bandits using 𝓁_p Confidence Sets.

Gene Li, Cong Ma, Nathan Srebro

CoRR, 2022

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models.

Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Margin Maximization in Linear and ReLU Networks.

Gal Vardi, Ohad Shamir, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Sample Complexity of One-Hidden-Layer Neural Networks.

Gal Vardi, Ohad Shamir, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization.

Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization.

Omar Montasser, Steve Hanneke, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets.

Gene Li, Cong Ma, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exponential Family Model-Based Reinforcement Learning via Score Matching.

Gene Li, Junbo Li, Anmol Kabra, Nati Srebro, Zhaoran Wang, Zhuoran Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding the Eluder Dimension.

Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization.

Idan Amir, Roi Livni, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract).

Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Implicit Bias of the Step Size in Linear Diagonal Neural Networks.

Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry

Proceedings of the International Conference on Machine Learning, 2022

How catastrophic can catastrophic forgetting be in linear regression?

Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Transductive Robust Learning Guarantees.

Omar Montasser, Steve Hanneke, Nathan Srebro

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

An accelerated communication-efficient primal-dual optimization framework for structured machine learning.

Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác

Optim. Methods Softw., 2021

Exponential Family Model-Based Reinforcement Learning via Score Matching.

Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang

CoRR, 2021

Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression.

Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro

CoRR, 2021

Eluder Dimension and Generalized Rank.

Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro

CoRR, 2021

An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning.

Blake E. Woodworth, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting.

Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Representation Costs of Linear Neural Networks: Analysis and Design.

Zhen Dai, Mina Karzand, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Stochastic Newton Algorithm for Distributed Convex Optimization.

Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Power of Differentiable Learning versus PAC and SQ Learning.

Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels.

Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro

Proceedings of the 38th International Conference on Machine Learning, 2021

Fast margin maximization via dual acceleration.

Ziwei Ji, Nathan Srebro, Matus Telgarsky

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent.

Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry

Proceedings of the 38th International Conference on Machine Learning, 2021

Dropout: Explicit Forms and Capacity Control.

Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro

Proceedings of the 38th International Conference on Machine Learning, 2021

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication.

Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2021

Adversarially Robust Learning with Unknown Perturbation Sets.

Omar Montasser, Steve Hanneke, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2021

Does Invariant Risk Minimization Capture Invariance?

Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent.

Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Predictive Value Generalization Bounds.

Keshav Vemuri, Nathan Srebro

CoRR, 2020

Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow.

Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro

CoRR, 2020

On Uniform Convergence and Low-Norm Interpolation Learning.

Lijia Zhou, Danica J. Sutherland, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Minibatch vs Local SGD for Heterogeneous Distributed Learning.

Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy.

Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Reducing Adversarially Robust Learning to Non-Robust PAC Learning.

Omar Montasser, Steve Hanneke, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Is Local SGD Better than Minibatch SGD?

Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

Fair Learning with Private Demographic Data.

Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

Efficiently Learning Adversarially Robust Halfspaces with Noise.

Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case.

Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro

Proceedings of the 8th International Conference on Learning Representations, 2020

Kernel and Rich Regimes in Overparametrized Models.

Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2020

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity.

Pritish Kamath, Omar Montasser, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2020

A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates.

Yossi Arjevani, Ohad Shamir, Nathan Srebro

Proceedings of the Algorithmic Learning Theory, 2020

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis.

Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Stochastic Canonical Correlation Analysis.

Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang

J. Mach. Learn. Res., 2019

Simple Surveys: Response Retrieval Inspired by Recommendation Systems.

Nandana Sengupta, Nati Srebro, James Evans

CoRR, 2019

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models.

Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry

Proceedings of the 36th International Conference on Machine Learning, 2019

Semi-Cyclic Stochastic Gradient Descent.

Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar

Proceedings of the 36th International Conference on Machine Learning, 2019

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints.

Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You

Proceedings of the 36th International Conference on Machine Learning, 2019

The role of over-parametrization in generalization of neural networks.

Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

Proceedings of the 7th International Conference on Learning Representations, 2019

From Fair Decision Making To Social Equality.

Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory.

Blake E. Woodworth, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2019

How do infinite width bounded norm networks look in function space?

Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2019

VC Classes are Adversarially Robustly Learnable, but Only Improperly.

Omar Montasser, Steve Hanneke, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2019

The Complexity of Making the Gradient Small in Stochastic Convex Optimization.

Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth

Proceedings of the Conference on Learning Theory, 2019

Stochastic Nonconvex Optimization with Large Minibatches.

Weiran Wang, Nathan Srebro

Proceedings of the Algorithmic Learning Theory, 2019

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate.

Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Convergence of Gradient Descent on Separable Data.

Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro

J. Mach. Learn. Res., 2018

Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks.

Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

CoRR, 2018

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization.

Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro

CoRR, 2018

Convergence of Gradient Descent on Separable Data.

Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Nathan Srebro, Daniel Soudry

CoRR, 2018

Distributed Stochastic Multi-Task Learning with Graph Regularization.

Weiran Wang, Jialei Wang, Mladen Kolar, Nathan Srebro

CoRR, 2018

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization.

Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Everlasting Database: Statistical Validity at a Fair Price.

Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Implicit Bias of Gradient Descent on Linear Convolutional Networks.

Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On preserving non-discrimination when combining expert advice.

Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Characterizing Implicit Bias in Terms of Optimization Geometry.

Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro

Proceedings of the 35th International Conference on Machine Learning, 2018

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro

Proceedings of the 6th International Conference on Learning Representations, 2018

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro

Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient coordinate-wise leading eigenvector computation.

Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro

Proceedings of the Algorithmic Learning Theory, 2018

2017

Data-Dependent Convergence for Consensus Stochastic Optimization.

Avleen Singh Bijral, Anand D. Sarwate, Nathan Srebro

IEEE Trans. Autom. Control., 2017

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Nathan Srebro

CoRR, 2017

Geometry of Optimization and Implicit Regularization in Deep Learning.

Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro

CoRR, 2017

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro

CoRR, 2017

The Marginal Value of Adaptive Gradient Methods in Machine Learning.

Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Exploring Generalization in Deep Learning.

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Implicit Regularization in Matrix Factorization.

Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stochastic Approximation for Canonical Correlation Analysis.

Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Efficient Distributed Learning with Sparsity.

Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis.

Dan Garber, Ohad Shamir, Nathan Srebro

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning Non-Discriminatory Predictors.

Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro

Proceedings of the 30th Conference on Learning Theory, 2017

Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox.

Jialei Wang, Weiran Wang, Nathan Srebro

Proceedings of the 30th Conference on Learning Theory, 2017

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data.

Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm.

Deanna Needell, Nathan Srebro, Rachel A. Ward

Math. Program., 2016

Globally Convergent Stochastic Optimization for Canonical Correlation Analysis.

Weiran Wang, Jialei Wang, Nathan Srebro

CoRR, 2016

Reducing Runtime by Recycling Samples.

Jialei Wang, Hai Wang, Nathan Srebro

CoRR, 2016

Distributed Multi-Task Learning with Shared Representation.

Jialei Wang, Mladen Kolar, Nathan Srebro

CoRR, 2016

Data-Dependent Path Normalization in Neural Networks.

Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro

Proceedings of the 4th International Conference on Learning Representations, 2016

Tight Complexity Bounds for Optimizing Composite Objectives.

Blake E. Woodworth, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis.

Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Normalized Spectral Map Synchronization.

Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations.

Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Equality of Opportunity in Supervised Learning.

Moritz Hardt, Eric Price, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Global Optimality of Local Search for Low Rank Matrix Recovery.

Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Data-dependent bounds on network gradient descent.

Avleen Singh Bijral, Anand D. Sarwate, Nathan Srebro

Proceedings of the 54th Annual Allerton Conference on Communication, 2016

Distributed Multi-Task Learning.

Jialei Wang, Mladen Kolar, Nathan Srebro

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Fast and Scalable Structural SVM with Slack Rescaling.

Heejin Choi, Ofer Meshi, Nathan Srebro

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Learning sparse low-threshold linear classifiers.

Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang

J. Mach. Learn. Res., 2015

Distributed Multitask Learning.

Jialei Wang, Mladen Kolar, Nathan Srebro

CoRR, 2015

Distributed Mini-Batch SDCA.

Martin Takác, Peter Richtárik, Nathan Srebro

CoRR, 2015

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning.

Behnam Neyshabur, Ryota Tomioka, Nathan Srebro

Proceedings of the 3rd International Conference on Learning Representations, 2015

Normalized Hierarchical SVM.

Heejin Choi, Yutaka Sasaki, Nathan Srebro

CoRR, 2015

Path-SGD: Path-Normalized Optimization in Deep Neural Networks.

Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

On Symmetric and Asymmetric LSHs for Inner Product Search.

Behnam Neyshabur, Nathan Srebro

Proceedings of the 32nd International Conference on Machine Learning, 2015

Norm-Based Capacity Control in Neural Networks.

Behnam Neyshabur, Ryota Tomioka, Nathan Srebro

Proceedings of The 28th Conference on Learning Theory, 2015

Stochastic optimization for deep CCA via nonlinear orthogonal iterations.

Weiran Wang, Raman Arora, Karen Livescu, Nathan Srebro

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

Efficient Training of Structured SVMs via Soft Constraints.

Ofer Meshi, Nathan Srebro, Tamir Hazan

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Active collaborative permutation learning.

Jialei Wang, Nathan Srebro, James Evans

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Communication-Efficient Distributed Optimization using an Approximate Newton-type Method.

Ohad Shamir, Nathan Srebro, Tong Zhang

Proceedings of the 31th International Conference on Machine Learning, 2014

Clustering, Hamming Embedding, Generalized LSH and the Max Norm.

Behnam Neyshabur, Yury Makarychev, Nathan Srebro

Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

Distributed stochastic optimization and learning.

Ohad Shamir, Nathan Srebro

Proceedings of the 52nd Annual Allerton Conference on Communication, 2014

2013

Distribution-dependent sample complexity of large margin learning.

Sivan Sabato, Nathan Srebro, Naftali Tishby

J. Mach. Learn. Res., 2013

Stochastic gradient descent and the randomized Kaczmarz algorithm.

Deanna Needell, Nathan Srebro, Rachel A. Ward

CoRR, 2013

Auditing: Active Learning with Outcome-Dependent Query Costs.

Sivan Sabato, Anand D. Sarwate, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

The Power of Asymmetry in Binary Hashing.

Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Stochastic Optimization of PCA with Capped MSG.

Raman Arora, Andrew Cotter, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Mini-Batch Primal and Dual Methods for SVMs.

Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro

Proceedings of the 30th International Conference on Machine Learning, 2013

Learning Optimally Sparse Support Vector Machines.

Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Approximate Inference by Intersecting Semidefinite Bound and Local Polytope.

Jian Peng, Tamir Hazan, Nathan Srebro, Jinbo Xu

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Preface.

Shie Mannor, Nathan Srebro

Proceedings of the COLT 2012, 2012

PRISMA: PRoximal Iterative SMoothing Algorithm

Francesco Orabona, Andreas Argyriou, Nathan Srebro

CoRR, 2012

Sparse Prediction with the k-Overlap Norm

Andreas Argyriou, Rina Foygel, Nathan Srebro

CoRR, 2012

Characterizing the Sample Complexity of Large-Margin Learning With Second-Order Statistics

Sivan Sabato, Nati Srebro, Naftali Tishby

CoRR, 2012

Matrix reconstruction with the local max norm.

Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Sparse Prediction with the $k$-Support Norm.

Andreas Argyriou, Rina Foygel, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Clustering using Max-norm Constrained Optimization.

Ali Jalali, Nathan Srebro

Proceedings of the 29th International Conference on Machine Learning, 2012

The Kernelized Stochastic Batch Perceptron.

Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro

Proceedings of the 29th International Conference on Machine Learning, 2012

Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss.

Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan

Proceedings of the 29th International Conference on Machine Learning, 2012

Stochastic optimization for PCA and PLS.

Raman Arora, Andrew Cotter, Karen Livescu, Nathan Srebro

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011

Pegasos: primal estimated sub-gradient solver for SVM.

Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter

Math. Program., 2011

Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning.

Xueyuan Zhou, Nathan Srebro

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Concentration-Based Guarantees for Low-Rank Matrix Reconstruction.

Rina Foygel, Nathan Srebro

Proceedings of the COLT 2011, 2011

Explicit Approximations of the Gaussian Kernel

Andrew Cotter, Joseph Keshet, Nathan Srebro

CoRR, 2011

Semi-supervised Learning with Density Based Distances.

Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro

Proceedings of the UAI 2011, 2011

On the Universality of Online Mirror Descent.

Nati Srebro, Karthik Sridharan, Ambuj Tewari

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Beating SGD: Learning SVMs in Sublinear Time.

Elad Hazan, Tomer Koren, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Learning with the weighted trace-norm under arbitrary sampling distributions.

Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Better Mini-Batch Algorithms via Accelerated Gradient Methods.

Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

An iterated graph laplacian approach for ranking on manifolds.

Xueyuan Zhou, Mikhail Belkin, Nathan Srebro

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

A GPU-tailored approach for training kernelized SVMs.

Andrew Cotter, Nathan Srebro, Joseph Keshet

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2010

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints.

Shai Shalev-Shwartz, Nathan Srebro, Tong Zhang

SIAM J. Optim., 2010

Learnability, Stability and Uniform Convergence.

Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

J. Mach. Learn. Res., 2010

Reducing Label Complexity by Learning From Bags.

Sivan Sabato, Nathan Srebro, Naftali Tishby

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Smoothness, Low Noise and Fast Rates.

Nathan Srebro, Karthik Sridharan, Ambuj Tewari

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm.

Ruslan Salakhutdinov, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Tight Sample Complexity of Large-Margin Learning.

Sivan Sabato, Nathan Srebro, Naftali Tishby

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Practical Large-Scale Optimization for Max-norm Regularization.

Jason D. Lee, Ben Recht, Ruslan Salakhutdinov, Nathan Srebro, Joel A. Tropp

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning.

Percy Liang, Nati Srebro

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data.

Boaz Nadler, Nathan Srebro, Xueyuan Zhou

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Learnability and Stability in the General Learning Setting.

Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

Proceedings of the COLT 2009, 2009

Stochastic Convex Optimization.

Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

Proceedings of the COLT 2009, 2009

2008

A theory of learning with similarity functions.

Maria-Florina Balcan, Avrim Blum, Nathan Srebro

Mach. Learn., 2008

Complexity of Inference in Graphical Models.

Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha

Proceedings of the UAI 2008, 2008

Fast Rates for Regularized Objectives.

Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

SVM optimization: inverse dependence on training set size.

Shai Shalev-Shwartz, Nathan Srebro

Proceedings of the Machine Learning, 2008

Improved Guarantees for Learning via Similarity Functions.

Maria-Florina Balcan, Avrim Blum, Nathan Srebro

Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM.

Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro

Proceedings of the Machine Learning, 2007

Uncovering shared structures in multiclass classification.

Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman

Proceedings of the Machine Learning, 2007

Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?

Nathan Srebro

Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

How Good Is a Kernel When Used as a Similarity Measure?

Nathan Srebro

Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

<i>l</i>₁ Regularization in Infinite Dimensional Feature Spaces.

Saharon Rosset, Grzegorz Swirszcz, Nathan Srebro, Ji Zhu

Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

2006

An investigation of computational and informational limits in Gaussian mixture clustering.

Nathan Srebro, Gregory Shakhnarovich, Sam T. Roweis

Proceedings of the Machine Learning, 2006

Learning Bounds for Support Vector Machines with Learned Kernels.

Nathan Srebro, Shai Ben-David

Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005

Fast maximum margin matrix factorization for collaborative prediction.

Jason D. M. Rennie, Nathan Srebro

Proceedings of the Machine Learning, 2005

Rank, Trace-Norm and Max-Norm.

Nathan Srebro, Adi Shraibman

Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2004

Learning with matrix factorizations.

Nathan Srebro

PhD thesis, 2004

Maximum-Margin Matrix Factorization.

Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices.

Nathan Srebro, Noga Alon, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data.

Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi S. Jaakkola

Bioinform., 2003

Maximum likelihood bounded tree-width Markov networks.

Nathan Srebro

Artif. Intell., 2003

Linear Dependent Dimensionality Reduction.

Nathan Srebro, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Weighted Low-Rank Approximations.

Nathan Srebro, Tommi S. Jaakkola

Proceedings of the Machine Learning, 2003

2001

Learning Markov networks: maximum bounded tree-width graphs.

David R. Karger, Nathan Srebro

Proceedings of the Twelfth Annual Symposium on Discrete Algorithms, 2001