| 2026 | COLT | Invited Open Problem: Is the Power of Deep Learning over Linear Models Inherently Distribution Dependent? | Vitaly Feldman, Pritish Kamath, Nathan Srebro |
| 2026 | COLT | Tight Sample Complexity of Transformers. | Chenxiao Yang, Nathan Srebro, Zhiyuan Li |
| 2025 | COLT | A Theory of Learning with Autoregressive Chain of Thought. | Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro |
| 2025 | COLT | Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification. | Xiaohan Zhu, Nathan Srebro |
| 2025 | ICML | Weak-to-Strong Generalization Even in Random Feature Networks, Provably. | Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro |
| 2025 | ICML | PENCIL: Long Thoughts with Short Memory. | Chenxiao Yang, Nathan Srebro, David McAllester, Zhiyuan Li |
| 2024 | COLT | Metalearning with Very Few Samples Per Task. | Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman |
| 2024 | COLT | Depth Separation in Norm-Bounded Infinite-Width Neural Networks. | Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro |
| 2024 | COLT | 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 |
| 2024 | ICLR | Noisy Interpolation Learning with Shallow Univariate ReLU Networks. | Nirmit Joshi, Gal Vardi, Nathan Srebro |
| 2024 | ICLR | An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. | Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro |
| 2024 | ICML | 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 |
| 2023 | COLT | Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. | Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro |
| 2023 | COLT | Shortest Program Interpolation Learning. | Naren Sarayu Manoj, Nathan Srebro |
| 2023 | ICLR | Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. | Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu |
| 2023 | ICML | Continual Learning in Linear Classification on Separable Data. | Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry |
| 2023 | ICML | Federated Online and Bandit Convex Optimization. | Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro |
| 2022 | AISTATS | Transductive Robust Learning Guarantees. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2022 | COLT | How catastrophic can catastrophic forgetting be in linear regression? | Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry |
| 2022 | ICML | Implicit Bias of the Step Size in Linear Diagonal Neural Networks. | Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry |
| 2022 | IJCAI | The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). | Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro |
| 2021 | AISTATS | Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. | Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro |
| 2021 | AISTATS | Does Invariant Risk Minimization Capture Invariance? | Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro |
| 2021 | COLT | Adversarially Robust Learning with Unknown Perturbation Sets. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2021 | COLT | The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. | Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro |
| 2021 | ICML | Dropout: Explicit Forms and Capacity Control. | Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro |
| 2021 | ICML | 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 |
| 2021 | ICML | Fast margin maximization via dual acceleration. | Ziwei Ji, Nathan Srebro, Matus Telgarsky |
| 2021 | ICML | Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. | Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro |
| 2020 | AISTATS | Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. | Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth |
| 2020 | ALT | A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. | Yossi Arjevani, Ohad Shamir, Nathan Srebro |
| 2020 | COLT | Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. | Pritish Kamath, Omar Montasser, Nathan Srebro |
| 2020 | COLT | 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 |
| 2020 | ICLR | A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. | Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro |
| 2020 | ICML | Efficiently Learning Adversarially Robust Halfspaces with Noise. | Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro |
| 2020 | ICML | Fair Learning with Private Demographic Data. | Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro |
| 2020 | ICML | 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 |
| 2019 | AISTATS | Convergence of Gradient Descent on Separable Data. | Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry |
| 2019 | AISTATS | Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. | Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry |
| 2019 | ALT | Stochastic Nonconvex Optimization with Large Minibatches. | Weiran Wang, Nathan Srebro |
| 2019 | COLT | 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 |
| 2019 | COLT | VC Classes are Adversarially Robustly Learnable, but Only Improperly. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2019 | COLT | How do infinite width bounded norm networks look in function space? | Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro |
| 2019 | COLT | Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. | Blake E. Woodworth, Nathan Srebro |
| 2019 | ICLR | The role of over-parametrization in generalization of neural networks. | Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro |
| 2019 | ICML | 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 |
| 2019 | ICML | Semi-Cyclic Stochastic Gradient Descent. | Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar |
| 2019 | ICML | Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. | Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry |
| 2018 | ALT | Efficient coordinate-wise leading eigenvector computation. | Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro |
| 2018 | ICLR | A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. | Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro |
| 2018 | ICLR | The Implicit Bias of Gradient Descent on Separable Data. | Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro |
| 2018 | ICML | Characterizing Implicit Bias in Terms of Optimization Geometry. | Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro |
| 2018 | ITA | Implicit Regularization in Matrix Factorization. | Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro |
| 2017 | COLT | Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. | Jialei Wang, Weiran Wang, Nathan Srebro |
| 2017 | COLT | Learning Non-Discriminatory Predictors. | Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro |
| 2017 | ICML | Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. | Dan Garber, Ohad Shamir, Nathan Srebro |
| 2017 | ICML | Efficient Distributed Learning with Sparsity. | Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang |
| 2016 | AISTATS | Fast and Scalable Structural SVM with Slack Rescaling. | Heejin Choi, Ofer Meshi, Nathan Srebro |
| 2016 | AISTATS | Distributed Multi-Task Learning. | Jialei Wang, Mladen Kolar, Nathan Srebro |
| 2015 | AISTATS | Efficient Training of Structured SVMs via Soft Constraints. | Ofer Meshi, Nathan Srebro, Tamir Hazan |
| 2015 | COLT | Norm-Based Capacity Control in Neural Networks. | Behnam Neyshabur, Ryota Tomioka, Nathan Srebro |
| 2015 | ICML | On Symmetric and Asymmetric LSHs for Inner Product Search. | Behnam Neyshabur, Nathan Srebro |
| 2014 | ALT | Clustering, Hamming Embedding, Generalized LSH and the Max Norm. | Behnam Neyshabur, Yury Makarychev, Nathan Srebro |
| 2014 | ICML | Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. | Ohad Shamir, Nathan Srebro, Tong Zhang |
| 2014 | KDD | Active collaborative permutation learning. | Jialei Wang, Nathan Srebro, James A. Evans |
| 2012 | ICML | Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. | Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan |
| 2012 | ICML | The Kernelized Stochastic Batch Perceptron. | Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro |
| 2012 | ICML | Clustering using Max-norm Constrained Optimization. | Ali Jalali, Nathan Srebro |
| 2011 | KDD | A GPU-tailored approach for training kernelized SVMs. | Andrew Cotter, Nathan Srebro, Joseph Keshet |
| 2011 | KDD | An iterated graph laplacian approach for ranking on manifolds. | Xueyuan Zhou, Mikhail Belkin, Nathan Srebro |
| 2011 | UAI | Semi-supervised Learning with Density Based Distances. | Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro |
| 2009 | COLT | Stochastic Convex Optimization. | Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan |
| 2009 | COLT | Learnability and Stability in the General Learning Setting. | Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan |
| 2008 | COLT | Improved Guarantees for Learning via Similarity Functions. | Maria-Florina Balcan, Avrim Blum, Nathan Srebro |
| 2008 | ICML | SVM optimization: inverse dependence on training set size. | Shai Shalev-Shwartz, Nathan Srebro |
| 2008 | UAI | Complexity of Inference in Graphical Models. | Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha |
| 2007 | COLT | Untitled record | Saharon Rosset, Grzegorz Swirszcz, Nathan Srebro, Ji Zhu |
| 2007 | COLT | How Good Is a Kernel When Used as a Similarity Measure? | Nathan Srebro |
| 2007 | COLT | Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? | Nathan Srebro |
| 2007 | ICML | Uncovering shared structures in multiclass classification. | Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman |
| 2007 | ICML | Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. | Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro |
| 2006 | COLT | Learning Bounds for Support Vector Machines with Learned Kernels. | Nathan Srebro, Shai Ben-David |
| 2006 | ICML | An investigation of computational and informational limits in Gaussian mixture clustering. | Nathan Srebro, Gregory Shakhnarovich, Sam T. Roweis |
| 2005 | COLT | Rank, Trace-Norm and Max-Norm. | Nathan Srebro, Adi Shraibman |
| 2005 | ICML | Fast maximum margin matrix factorization for collaborative prediction. | Jason D. M. Rennie, Nathan Srebro |
| 2003 | ICML | Weighted Low-Rank Approximations. | Nathan Srebro, Tommi S. Jaakkola |
| 2002 | WABI | K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. | Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angle M. Hamel, Tommi S. Jaakkola, Nathan Srebro |
| 2001 | SODA | Learning Markov networks: maximum bounded tree-width graphs. | David R. Karger, Nathan Srebro |
| 2001 | UAI | Maximum Likelihood Bounded Tree-Width Markov Networks. | Nathan Srebro |