Learning Markov networks: maximum bounded tree-width graphs. - David R. Karger; Nathan Srebro (SODA, 2001)

Weighted Low-Rank Approximations. - Nathan Srebro; Tommi S. Jaakkola (ICML, 2003)

Linear Dependent Dimensionality Reduction. - Nathan Srebro; Tommi S. Jaakkola (NIPS, 2003)

Maximum likelihood bounded tree-width Markov networks. - Nathan Srebro (Artif. Intell., 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)

Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. - Nathan Srebro; Noga Alon; Tommi S. Jaakkola (NIPS, 2004)

Maximum-Margin Matrix Factorization. - Nathan Srebro; Jason D. M. Rennie; Tommi S. Jaakkola (NIPS, 2004)

Learning with matrix factorizations. - Nathan Srebro (2004)

Rank, Trace-Norm and Max-Norm. - Nathan Srebro; Adi Shraibman (COLT, 2005)

Fast maximum margin matrix factorization for collaborative prediction. - Jason D. M. Rennie; Nathan Srebro (ICML, 2005)

Learning Bounds for Support Vector Machines with Learned Kernels. - Nathan Srebro; Shai Ben-David (COLT, 2006)

An investigation of computational and informational limits in Gaussian mixture clustering. - Nathan Srebro; Gregory Shakhnarovich; Sam T. Roweis (ICML, 2006)

l1 Regularization in Infinite Dimensional Feature Spaces. - Saharon Rosset; Grzegorz Swirszcz; Nathan Srebro; Ji Zhu (COLT, 2007)

How Good Is a Kernel When Used as a Similarity Measure? - Nathan Srebro (COLT, 2007)

Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? - Nathan Srebro (COLT, 2007)

Uncovering shared structures in multiclass classification. - Yonatan Amit; Michael Fink 002; Nathan Srebro; Shimon Ullman (ICML, 2007)

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. - Shai Shalev-Shwartz; Yoram Singer; Nathan Srebro (ICML, 2007)

Improved Guarantees for Learning via Similarity Functions. - Maria-Florina Balcan; Avrim Blum; Nathan Srebro (COLT, 2008)

SVM optimization: inverse dependence on training set size. - Shai Shalev-Shwartz; Nathan Srebro (ICML, 2008)

Fast Rates for Regularized Objectives. - Karthik Sridharan; Shai Shalev-Shwartz; Nathan Srebro (NIPS, 2008)

Complexity of Inference in Graphical Models. - Venkat Chandrasekaran; Nathan Srebro; Prahladh Harsha (UAI, 2008)

A theory of learning with similarity functions. - Maria-Florina Balcan; Avrim Blum; Nathan Srebro (Mach. Learn., 2008)

Stochastic Convex Optimization. - Shai Shalev-Shwartz; Ohad Shamir; Nathan Srebro; Karthik Sridharan (COLT, 2009)

Learnability and Stability in the General Learning Setting. - Shai Shalev-Shwartz; Ohad Shamir; Nathan Srebro; Karthik Sridharan (COLT, 2009)

Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data. - Boaz Nadler; Nathan Srebro; Xueyuan Zhou (NIPS, 2009)

On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning. - Percy Liang; Nati Srebro (ICML, 2010)

Practical Large-Scale Optimization for Max-norm Regularization. - Jason D. Lee; Ben Recht; Ruslan Salakhutdinov; Nathan Srebro; Joel A. Tropp (NIPS, 2010)

Tight Sample Complexity of Large-Margin Learning. - Sivan Sabato; Nathan Srebro; Naftali Tishby (NIPS, 2010)

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm. - Ruslan Salakhutdinov; Nathan Srebro (NIPS, 2010)

Smoothness, Low Noise and Fast Rates. - Nathan Srebro; Karthik Sridharan; Ambuj Tewari (NIPS, 2010)

Reducing Label Complexity by Learning From Bags. - Sivan Sabato; Nathan Srebro; Naftali Tishby (AISTATS, 2010)

Learnability, Stability and Uniform Convergence. - Shai Shalev-Shwartz; Ohad Shamir; Nathan Srebro; Karthik Sridharan (J. Mach. Learn. Res., 2010)

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. - Shai Shalev-Shwartz; Nathan Srebro; Tong Zhang 001 (SIAM J. Optim., 2010)

A GPU-tailored approach for training kernelized SVMs. - Andrew Cotter; Nathan Srebro; Joseph Keshet (KDD, 2011)

An iterated graph laplacian approach for ranking on manifolds. - Xueyuan Zhou; Mikhail Belkin; Nathan Srebro (KDD, 2011)

Better Mini-Batch Algorithms via Accelerated Gradient Methods. - Andrew Cotter; Ohad Shamir; Nati Srebro; Karthik Sridharan (NIPS, 2011)

Learning with the weighted trace-norm under arbitrary sampling distributions. - Rina Foygel; Ruslan Salakhutdinov; Ohad Shamir; Nati Srebro (NIPS, 2011)

Beating SGD: Learning SVMs in Sublinear Time. - Elad Hazan; Tomer Koren; Nati Srebro (NIPS, 2011)

On the Universality of Online Mirror Descent. - Nati Srebro; Karthik Sridharan; Ambuj Tewari (NIPS, 2011)

Semi-supervised Learning with Density Based Distances. - Avleen Singh Bijral; Nathan D. Ratliff; Nathan Srebro (UAI, 2011)

Explicit Approximations of the Gaussian Kernel - Andrew Cotter; Joseph Keshet; Nathan Srebro (CoRR, 2011)

Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. - Rina Foygel; Nathan Srebro (COLT, 2011)

Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. - Xueyuan Zhou; Nathan Srebro (AISTATS, 2011)

Pegasos: primal estimated sub-gradient solver for SVM. - Shai Shalev-Shwartz; Yoram Singer; Nathan Srebro; Andrew Cotter (Math. Program., 2011)

Stochastic optimization for PCA and PLS. - Raman Arora; Andrew Cotter; Karen Livescu; Nathan Srebro (Allerton Conference, 2012)

Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. - Shai Ben-David; David Loker; Nathan Srebro; Karthik Sridharan (ICML, 2012)

The Kernelized Stochastic Batch Perceptron. - Andrew Cotter; Shai Shalev-Shwartz; Nathan Srebro (ICML, 2012)

Clustering using Max-norm Constrained Optimization. - Ali Jalali; Nathan Srebro (ICML, 2012)

Sparse Prediction with the $k$-Support Norm. - Andreas Argyriou; Rina Foygel; Nathan Srebro (NIPS, 2012)

Matrix reconstruction with the local max norm. - Rina Foygel; Nathan Srebro; Ruslan Salakhutdinov (NIPS, 2012)

Characterizing the Sample Complexity of Large-Margin Learning With Second-Order Statistics - Sivan Sabato; Nati Srebro; Naftali Tishby (CoRR, 2012)

Sparse Prediction with the k-Overlap Norm - Andreas Argyriou; Rina Foygel; Nathan Srebro (CoRR, 2012)

PRISMA: PRoximal Iterative SMoothing Algorithm - Francesco Orabona; Andreas Argyriou; Nathan Srebro (CoRR, 2012)

Preface. - Shie Mannor; Nathan Srebro (COLT, 2012)

Approximate Inference by Intersecting Semidefinite Bound and Local Polytope. - Jian Peng 001; Tamir Hazan; Nathan Srebro; Jinbo Xu (AISTATS, 2012)

Learning Optimally Sparse Support Vector Machines. - Andrew Cotter; Shai Shalev-Shwartz; Nati Srebro (ICML (1), 2013)

Mini-Batch Primal and Dual Methods for SVMs. - Martin Takác 001; Avleen Singh Bijral; Peter Richtárik; Nati Srebro (ICML (3), 2013)

Stochastic Optimization of PCA with Capped MSG. - Raman Arora; Andrew Cotter; Nati Srebro (NIPS, 2013)

The Power of Asymmetry in Binary Hashing. - Behnam Neyshabur; Nati Srebro; Ruslan Salakhutdinov; Yury Makarychev; Payman Yadollahpour (NIPS, 2013)

Auditing: Active Learning with Outcome-Dependent Query Costs. - Sivan Sabato; Anand D. Sarwate; Nati Srebro (NIPS, 2013)

Stochastic gradient descent and the randomized Kaczmarz algorithm. - Deanna Needell; Nathan Srebro; Rachel A. Ward (CoRR, 2013)

Distribution-dependent sample complexity of large margin learning. - Sivan Sabato; Nathan Srebro; Naftali Tishby (J. Mach. Learn. Res., 2013)

Distributed stochastic optimization and learning. - Ohad Shamir; Nathan Srebro (Allerton, 2014)

Clustering, Hamming Embedding, Generalized LSH and the Max Norm. - Behnam Neyshabur; Yury Makarychev; Nathan Srebro (ALT, 2014)

Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. - Ohad Shamir; Nathan Srebro; Tong Zhang 001 (ICML, 2014)

Active collaborative permutation learning. - Jialei Wang; Nathan Srebro; James Evans (KDD, 2014)

Efficient Training of Structured SVMs via Soft Constraints. - Ofer Meshi; Nathan Srebro; Tamir Hazan (AISTATS, 2015)

Stochastic optimization for deep CCA via nonlinear orthogonal iterations. - Weiran Wang; Raman Arora; Karen Livescu; Nathan Srebro (Allerton, 2015)

Norm-Based Capacity Control in Neural Networks. - Behnam Neyshabur; Ryota Tomioka; Nathan Srebro (COLT, 2015)

On Symmetric and Asymmetric LSHs for Inner Product Search. - Behnam Neyshabur; Nathan Srebro (ICML, 2015)

Path-SGD: Path-Normalized Optimization in Deep Neural Networks. - Behnam Neyshabur; Ruslan Salakhutdinov; Nathan Srebro (NIPS, 2015)

Normalized Hierarchical SVM. - Heejin Choi; Yutaka Sasaki; 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 (ICLR (Workshop), 2015)

Distributed Mini-Batch SDCA. - Martin Takác 001; Peter Richtárik; Nathan Srebro (CoRR, 2015)

Distributed Multitask Learning. - Jialei Wang; Mladen Kolar; Nathan Srebro (CoRR, 2015)

Learning sparse low-threshold linear classifiers. - Sivan Sabato; Shai Shalev-Shwartz; Nathan Srebro; Daniel J. Hsu; Tong Zhang 001 (J. Mach. Learn. Res., 2015)

Fast and Scalable Structural SVM with Slack Rescaling. - Heejin Choi; Ofer Meshi; Nathan Srebro (AISTATS, 2016)

Distributed Multi-Task Learning. - Jialei Wang; Mladen Kolar; Nathan Srebro (AISTATS, 2016)

Data-dependent bounds on network gradient descent. - Avleen Singh Bijral; Anand D. Sarwate; Nathan Srebro (Allerton, 2016)

Global Optimality of Local Search for Low Rank Matrix Recovery. - Srinadh Bhojanapalli; Behnam Neyshabur; Nati Srebro (NIPS, 2016)

Equality of Opportunity in Supervised Learning. - Moritz Hardt; Eric Price 001; Nati Srebro (NIPS, 2016)

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. - Behnam Neyshabur; Yuhuai Wu; Ruslan Salakhutdinov; Nati Srebro (NIPS, 2016)

Normalized Spectral Map Synchronization. - Yanyao Shen; Qixing Huang; Nati Srebro; Sujay Sanghavi (NIPS, 2016)

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. - Weiran Wang; Jialei Wang; Dan Garber; Nati Srebro (NIPS, 2016)

Tight Complexity Bounds for Optimizing Composite Objectives. - Blake E. Woodworth; Nati Srebro (NIPS, 2016)

Data-Dependent Path Normalization in Neural Networks. - Behnam Neyshabur; Ryota Tomioka; Ruslan Salakhutdinov; Nathan Srebro (ICLR (Poster), 2016)

Distributed Multi-Task Learning with Shared Representation. - Jialei Wang; Mladen Kolar; Nathan Srebro (CoRR, 2016)

Reducing Runtime by Recycling Samples. - Jialei Wang; Hai Wang; Nathan Srebro (CoRR, 2016)

Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. - Weiran Wang; Jialei Wang; Nathan Srebro (CoRR, 2016)

Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm. - Deanna Needell; Nathan Srebro; Rachel A. Ward (Math. Program., 2016)

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 (AISTATS, 2017)

Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. - Jialei Wang; Weiran Wang; Nathan Srebro (COLT, 2017)

Learning Non-Discriminatory Predictors. - Blake E. Woodworth; Suriya Gunasekar; Mesrob I. Ohannessian; Nathan Srebro (COLT, 2017)

Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. - Dan Garber; Ohad Shamir; Nathan Srebro (ICML, 2017)

Efficient Distributed Learning with Sparsity. - Jialei Wang; Mladen Kolar; Nathan Srebro; Tong Zhang 001 (ICML, 2017)

Stochastic Approximation for Canonical Correlation Analysis. - Raman Arora; Teodor Vanislavov Marinov; Poorya Mianjy; Nati Srebro (NIPS, 2017)

Implicit Regularization in Matrix Factorization. - Suriya Gunasekar; Blake E. Woodworth; Srinadh Bhojanapalli; Behnam Neyshabur; Nati Srebro (NIPS, 2017)

Exploring Generalization in Deep Learning. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nati Srebro (NIPS, 2017)

The Marginal Value of Adaptive Gradient Methods in Machine Learning. - Ashia C. Wilson; Rebecca Roelofs; Mitchell Stern; Nati Srebro; Benjamin Recht (NIPS, 2017)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nathan Srebro (CoRR, 2017)

Geometry of Optimization and Implicit Regularization in Deep Learning. - Behnam Neyshabur; Ryota Tomioka; Ruslan Salakhutdinov; Nathan Srebro (CoRR, 2017)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Nathan Srebro (CoRR, 2017)

Data-Dependent Convergence for Consensus Stochastic Optimization. - Avleen Singh Bijral; Anand D. Sarwate; Nathan Srebro (IEEE Trans. Autom. Control., 2017)

Efficient coordinate-wise leading eigenvector computation. - Jialei Wang; Weiran Wang; Dan Garber; Nathan Srebro (ALT, 2018)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; Nathan Srebro (ICLR (Poster), 2018)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Mor Shpigel Nacson; Nathan Srebro (ICLR (Poster), 2018)

Characterizing Implicit Bias in Terms of Optimization Geometry. - Suriya Gunasekar; Jason D. Lee; Daniel Soudry; Nathan Srebro (ICML, 2018)

On preserving non-discrimination when combining expert advice. - Avrim Blum; Suriya Gunasekar; Thodoris Lykouris; Nati Srebro (NeurIPS, 2018)

Implicit Bias of Gradient Descent on Linear Convolutional Networks. - Suriya Gunasekar; Jason D. Lee; Daniel Soudry; Nati Srebro (NeurIPS, 2018)

The Everlasting Database: Statistical Validity at a Fair Price. - Blake E. Woodworth; Vitaly Feldman; Saharon Rosset; Nati Srebro (NeurIPS, 2018)

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. - Blake E. Woodworth; Jialei Wang; Adam D. Smith 001; Brendan McMahan; Nati Srebro (NeurIPS, 2018)

Distributed Stochastic Multi-Task Learning with Graph Regularization. - Weiran Wang; Jialei Wang; Mladen Kolar; 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)

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. - Blake E. Woodworth; Jialei Wang; Brendan McMahan; Nathan Srebro (CoRR, 2018)

Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. - Behnam Neyshabur; Zhiyuan Li 005; Srinadh Bhojanapalli; Yann LeCun; Nathan Srebro (CoRR, 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)

Convergence of Gradient Descent on Separable Data. - Mor Shpigel Nacson; Jason D. Lee; Suriya Gunasekar; Pedro Henrique Pamplona Savarese; Nathan Srebro; Daniel Soudry (AISTATS, 2019)

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. - Mor Shpigel Nacson; Nathan Srebro; Daniel Soudry (AISTATS, 2019)

Stochastic Nonconvex Optimization with Large Minibatches. - Weiran Wang; Nathan Srebro (ALT, 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 (COLT, 2019)

VC Classes are Adversarially Robustly Learnable, but Only Improperly. - Omar Montasser; Steve Hanneke; Nathan Srebro (COLT, 2019)

How do infinite width bounded norm networks look in function space? - Pedro Savarese; Itay Evron; Daniel Soudry; Nathan Srebro (COLT, 2019)

Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. - Blake E. Woodworth; Nathan Srebro (COLT, 2019)

From Fair Decision Making To Social Equality. - Hussein Mouzannar; Mesrob I. Ohannessian; Nathan Srebro (FAT, 2019)

The role of over-parametrization in generalization of neural networks. - Behnam Neyshabur; Zhiyuan Li 005; Srinadh Bhojanapalli; Yann LeCun; Nathan Srebro (ICLR (Poster), 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 (ICML, 2019)

Semi-Cyclic Stochastic Gradient Descent. - Hubert Eichner; Tomer Koren; Brendan McMahan; Nathan Srebro; Kunal Talwar (ICML, 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 (ICML, 2019)

Simple Surveys: Response Retrieval Inspired by Recommendation Systems. - Nandana Sengupta; Nati Srebro; James Evans (CoRR, 2019)

Stochastic Canonical Correlation Analysis. - Chao Gao; Dan Garber; Nathan Srebro; Jialei Wang; Weiran Wang (J. Mach. Learn. Res., 2019)

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. - Ryan Rogers 002; Aaron Roth 001; Adam D. Smith 001; Nathan Srebro; Om Thakkar 001; Blake E. Woodworth (AISTATS, 2020)

A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. - Yossi Arjevani; Ohad Shamir; Nathan Srebro (ALT, 2020)

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. - Pritish Kamath; Omar Montasser; Nathan Srebro (COLT, 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 (COLT, 2020)

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. - Greg Ongie; Rebecca Willett; Daniel Soudry; Nathan Srebro (ICLR, 2020)

Efficiently Learning Adversarially Robust Halfspaces with Noise. - Omar Montasser; Surbhi Goel; Ilias Diakonikolas; Nathan Srebro (ICML, 2020)

Fair Learning with Private Demographic Data. - Hussein Mozannar; Mesrob I. Ohannessian; Nathan Srebro (ICML, 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 (ICML, 2020)

Reducing Adversarially Robust Learning to Non-Robust PAC Learning. - Omar Montasser; Steve Hanneke; Nati Srebro (NeurIPS, 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 (NeurIPS, 2020)

Minibatch vs Local SGD for Heterogeneous Distributed Learning. - Blake E. Woodworth; Kumar Kshitij Patel; Nati Srebro (NeurIPS, 2020)

On Uniform Convergence and Low-Norm Interpolation Learning. - Lijia Zhou; Danica J. Sutherland; Nati Srebro (NeurIPS, 2020)

Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow. - Suriya Gunasekar; Blake E. Woodworth; Nathan Srebro (CoRR, 2020)

Predictive Value Generalization Bounds. - Keshav Vemuri; Nathan Srebro (CoRR, 2020)

Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. - Suriya Gunasekar; Blake E. Woodworth; Nathan Srebro (AISTATS, 2021)

Does Invariant Risk Minimization Capture Invariance? - Pritish Kamath; Akilesh Tangella; Danica J. Sutherland; Nathan Srebro (AISTATS, 2021)

Adversarially Robust Learning with Unknown Perturbation Sets. - Omar Montasser; Steve Hanneke; Nathan Srebro (COLT, 2021)

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. - Blake E. Woodworth; Brian Bullins; Ohad Shamir; Nathan Srebro (COLT, 2021)

Dropout: Explicit Forms and Capacity Control. - Raman Arora; Peter L. Bartlett; Poorya Mianjy; Nathan Srebro (ICML, 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 (ICML, 2021)

Fast margin maximization via dual acceleration. - Ziwei Ji; Nathan Srebro; Matus Telgarsky (ICML, 2021)

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. - Eran Malach; Pritish Kamath; Emmanuel Abbe; Nathan Srebro (ICML, 2021)

On the Power of Differentiable Learning versus PAC and SQ Learning. - Emmanuel Abbe; Pritish Kamath; Eran Malach; Colin Sandon; Nathan Srebro (NeurIPS, 2021)

A Stochastic Newton Algorithm for Distributed Convex Optimization. - Brian Bullins; Kumar Kshitij Patel; Ohad Shamir; Nathan Srebro; Blake E. Woodworth (NeurIPS, 2021)

Representation Costs of Linear Neural Networks: Analysis and Design. - Zhen Dai; Mina Karzand; Nathan Srebro (NeurIPS, 2021)

Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. - Frederic Koehler; Lijia Zhou; Danica J. Sutherland; Nathan Srebro (NeurIPS, 2021)

An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. - Blake E. Woodworth; Nathan Srebro (NeurIPS, 2021)

Eluder Dimension and Generalized Rank. - Gene Li; Pritish Kamath; Dylan J. Foster; Nathan Srebro (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)

Exponential Family Model-Based Reinforcement Learning via Score Matching. - Gene Li; Junbo Li 003; Nathan Srebro; Zhaoran Wang 001; Zhuoran Yang (CoRR, 2021)

An accelerated communication-efficient primal-dual optimization framework for structured machine learning. - Chenxin Ma; Martin Jaggi; Frank E. Curtis; Nathan Srebro; Martin Takác 001 (Optim. Methods Softw., 2021)

Transductive Robust Learning Guarantees. - Omar Montasser; Steve Hanneke; Nathan Srebro (AISTATS, 2022)

How catastrophic can catastrophic forgetting be in linear regression? - Itay Evron; Edward Moroshko; Rachel A. Ward; Nathan Srebro; Daniel Soudry (COLT, 2022)

Implicit Bias of the Step Size in Linear Diagonal Neural Networks. - Mor Shpigel Nacson; Kavya Ravichandran; Nathan Srebro; Daniel Soudry (ICML, 2022)

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). - Blake E. Woodworth; Brian Bullins; Ohad Shamir; Nathan Srebro (IJCAI, 2022)

Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. - Idan Amir; Roi Livni; Nati Srebro (NeurIPS, 2022)

Understanding the Eluder Dimension. - Gene Li; Pritish Kamath; Dylan J. Foster; Nati Srebro (NeurIPS, 2022)

Exponential Family Model-Based Reinforcement Learning via Score Matching. - Gene Li; Junbo Li 003; Anmol Kabra; Nati Srebro; Zhaoran Wang 001; Zhuoran Yang (NeurIPS, 2022)

Pessimism for Offline Linear Contextual Bandits using $ ell_p$ Confidence Sets. - Gene Li; Cong Ma; Nati Srebro (NeurIPS, 2022)

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. - Omar Montasser; Steve Hanneke; Nati Srebro (NeurIPS, 2022)

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. - Kumar Kshitij Patel; Lingxiao Wang; Blake E. Woodworth; Brian Bullins; Nati Srebro (NeurIPS, 2022)

The Sample Complexity of One-Hidden-Layer Neural Networks. - Gal Vardi; Ohad Shamir; Nati Srebro (NeurIPS, 2022)

On Margin Maximization in Linear and ReLU Networks. - Gal Vardi; Ohad Shamir; Nati Srebro (NeurIPS, 2022)

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. - Lijia Zhou; Frederic Koehler; Pragya Sur; Danica J. Sutherland; Nati Srebro (NeurIPS, 2022)

Pessimism for Offline Linear Contextual Bandits using 𝓁p Confidence Sets. - Gene Li; Cong Ma; Nathan Srebro (CoRR, 2022)

Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. - Spencer Frei; Gal Vardi; Peter L. Bartlett; Nathan Srebro (COLT, 2023)

Shortest Program Interpolation Learning. - Naren Sarayu Manoj; Nathan Srebro (COLT, 2023)

Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. - Spencer Frei; Gal Vardi; Peter L. Bartlett; Nathan Srebro; Wei Hu (ICLR, 2023)

Continual Learning in Linear Classification on Separable Data. - Itay Evron; Edward Moroshko; Gon Buzaglo; Maroun Khriesh; Badea Marjieh; Nathan Srebro; Daniel Soudry (ICML, 2023)

Federated Online and Bandit Convex Optimization. - Kumar Kshitij Patel; Lingxiao Wang; Aadirupa Saha; Nathan Srebro (ICML, 2023)

Most Neural Networks Are Almost Learnable. - Amit Daniely; Nati Srebro; Gal Vardi (NeurIPS, 2023)

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. - Amit Daniely; Nati Srebro; Gal Vardi (NeurIPS, 2023)

The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. - Spencer Frei; Gal Vardi; Peter L. Bartlett; Nati Srebro (NeurIPS, 2023)

When is Agnostic Reinforcement Learning Statistically Tractable? - Zeyu Jia; Gene Li; Alexander Rakhlin; Ayush Sekhari; Nati Srebro (NeurIPS, 2023)

Uniform Convergence with Square-Root Lipschitz Loss. - Lijia Zhou; Zhen Dai; Frederic Koehler; Nati Srebro (NeurIPS, 2023)

Interpolation Learning With Minimum Description Length. - Naren Sarayu Manoj; Nathan Srebro (CoRR, 2023)

Efficiently Learning Neural Networks: What Assumptions May Suffice? - Amit Daniely; Nathan Srebro; Gal Vardi (CoRR, 2023)

Applying statistical learning theory to deep learning. - Cédric Gerbelot; Avetik Karagulyan; Stefani Karp; Kavya Ravichandran; Menachem Stern; Nathan Srebro (CoRR, 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., 2023)

Metalearning with Very Few Samples Per Task. - Maryam Aliakbarpour; Konstantina Bairaktari; Gavin Brown 003; Adam Smith 006; Nathan Srebro; Jonathan R. Ullman (COLT, 2024)

Depth Separation in Norm-Bounded Infinite-Width Neural Networks. - Suzanna Parkinson; Greg Ongie; Rebecca Willett; Ohad Shamir; Nathan Srebro (COLT, 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 (COLT, 2024)

Score Design for Multi-Criteria Incentivization. - Anmol Kabra; Mina Karzand; Tosca Lechner; Nati Srebro; Serena Wang 001 (FORC, 2024)

Noisy Interpolation Learning with Shallow Univariate ReLU Networks. - Nirmit Joshi; Gal Vardi; Nathan Srebro (ICLR, 2024)

An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. - Lijia Zhou; James B. Simon; Gal Vardi; Nathan Srebro (ICLR, 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 (ICML, 2024)

The Price of Implicit Bias in Adversarially Robust Generalization. - Nikolaos Tsilivis 002; Natalie Frank; Nati Srebro; Julia Kempe (NeurIPS, 2024)

Provable Tempered Overfitting of Minimal Nets and Typical Nets. - Itamar Harel; William Hoza; Gal Vardi; Itay Evron; Nati Srebro; Daniel Soudry (NeurIPS, 2024)

On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. - Nirmit Joshi; Theodor Misiakiewicz; Nati Srebro (NeurIPS, 2024)

Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality. - Marko Medvedev; Gal Vardi; Nati Srebro (NeurIPS, 2024)

A Theory of Learning with Autoregressive Chain of Thought. - Nirmit Joshi; Gal Vardi; Adam Block; Surbhi Goel; Zhiyuan Li 005; Theodor Misiakiewicz; Nathan Srebro (COLT, 2025)

Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification. - Xiaohan Zhu; Nathan Srebro (COLT, 2025)

Tight Bounds on the Binomial CDF, and the Minimum of i.i.d Binomials, in terms of KL-Divergence. - Xiaohan Zhu; Mesrob I. Ohannessian; Nathan Srebro (CoRR, 2025)

Weak-to-Strong Generalization Even in Random Feature Networks, Provably. - Marko Medvedev; Kaifeng Lyu; Dingli Yu; Sanjeev Arora; Zhiyuan Li 005; Nathan Srebro (CoRR, 2025)

PENCIL: Long Thoughts with Short Memory. - Chenxiao Yang; Nathan Srebro; David McAllester; Zhiyuan Li 005 (CoRR, 2025)

Better Rates for Random Task Orderings in Continual Linear Models. - Itay Evron; Ran Levinstein; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Nathan Srebro (CoRR, 2025)

Temperature is All You Need for Generalization in Langevin Dynamics and other Markov Processes. - Itamar Harel; Yonathan Wolanowsky; Gal Vardi; Nathan Srebro; Daniel Soudry (CoRR, 2025)

Learning single-index models via harmonic decomposition. - Nirmit Joshi; Hugo Koubbi; Theodor Misiakiewicz; Nathan Srebro (CoRR, 2025)