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ORCIDNathan Srebro
Nati Srebro
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- affiliation: Toyota Technological Institute at Chicago, USA
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2020 – today
- 2024
[c157]Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. COLT 2024: 46-93- [c156]Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro:
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. COLT 2024: 4082-4114 - [c155]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: 4115-4157 - [c154]Anmol Kabra, Mina Karzand, Tosca Lechner, Nati Srebro, Serena Lutong Wang:
Score Design for Multi-Criteria Incentivization. FORC 2024: 8:1-8:22 - [c153]Nirmit Joshi, Gal Vardi, Nathan Srebro:
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. ICLR 2024 - [c152]Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro:
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. ICLR 2024 - [c151]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. ICML 2024 - [i127]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 abs/2402.06323 (2024) - [i126]Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro:
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. CoRR abs/2402.08808 (2024) - [i125]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 abs/2405.11667 (2024) - [i124]Nikolaos Tsilivis, Natalie Frank, Nathan Srebro, Julia Kempe:
The Price of Implicit Bias in Adversarially Robust Generalization. CoRR abs/2406.04981 (2024) - [i123]Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro:
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. CoRR abs/2407.05622 (2024) - [i122]Marko Medvedev, Gal Vardi, Nathan Srebro:
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality. CoRR abs/2409.03891 (2024) - 2023
- [j14]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): 165-214 (2023) - [c150]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: 3173-3228 - [c149]Naren Sarayu Manoj, Nathan Srebro:
Shortest Program Interpolation Learning. COLT 2023: 4881-4901 - [c148]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR 2023 - [c147]Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry:
Continual Learning in Linear Classification on Separable Data. ICML 2023: 9440-9484 - [c146]Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro:
Federated Online and Bandit Convex Optimization. ICML 2023: 27439-27460 - [c145]Amit Daniely, Nati Srebro, Gal Vardi:
Most Neural Networks Are Almost Learnable. NeurIPS 2023 - [c144]Amit Daniely, Nati Srebro, Gal Vardi:
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS 2023 - [c143]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS 2023 - [c142]Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro:
When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS 2023 - [c141]Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro:
Uniform Convergence with Square-Root Lipschitz Loss. NeurIPS 2023 - [i121]Naren Sarayu Manoj, Nathan Srebro:
Interpolation Learning With Minimum Description Length. CoRR abs/2302.07263 (2023) - [i120]Amit Daniely, Nathan Srebro, Gal Vardi:
Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR abs/2302.07426 (2023) - [i119]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR abs/2303.01456 (2023) - [i118]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 abs/2303.01462 (2023) - [i117]Amit Daniely, Nathan Srebro, Gal Vardi:
Most Neural Networks Are Almost Learnable. CoRR abs/2305.16508 (2023) - [i116]Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry:
Continual Learning in Linear Classification on Separable Data. CoRR abs/2306.03534 (2023) - [i115]Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro:
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. CoRR abs/2306.13185 (2023) - [i114]Lijia Zhou, Zhen Dai, Frederic Koehler, Nathan Srebro:
Uniform Convergence with Square-Root Lipschitz Loss. CoRR abs/2306.13188 (2023) - [i113]Nirmit Joshi, Gal Vardi, Nathan Srebro:
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. CoRR abs/2307.15396 (2023) - [i112]Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro:
When is Agnostic Reinforcement Learning Statistically Tractable? CoRR abs/2310.06113 (2023) - [i111]Cédric Gerbelot, Avetik Karagulyan, Stefani Karp, Kavya Ravichandran, Menachem Stern, Nathan Srebro:
Applying statistical learning theory to deep learning. CoRR abs/2311.15404 (2023) - 2022
- [c140]Omar Montasser, Steve Hanneke, Nathan Srebro:
Transductive Robust Learning Guarantees. AISTATS 2022: 11461-11471 - [c139]Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry:
How catastrophic can catastrophic forgetting be in linear regression? COLT 2022: 4028-4079 - [c138]Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry:
Implicit Bias of the Step Size in Linear Diagonal Neural Networks. ICML 2022: 16270-16295 - [c137]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: 5359-5363 - [c136]Idan Amir, Roi Livni, Nati Srebro:
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. NeurIPS 2022 - [c135]Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro:
Understanding the Eluder Dimension. NeurIPS 2022 - [c134]Gene Li, Junbo Li, Anmol Kabra, Nati Srebro, Zhaoran Wang, Zhuoran Yang:
Exponential Family Model-Based Reinforcement Learning via Score Matching. NeurIPS 2022 - [c133]Gene Li, Cong Ma, Nati Srebro:
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets. NeurIPS 2022 - [c132]Omar Montasser, Steve Hanneke, Nati Srebro:
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. NeurIPS 2022 - [c131]Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro:
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS 2022 - [c130]Gal Vardi, Ohad Shamir, Nati Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. NeurIPS 2022 - [c129]Gal Vardi, Ohad Shamir, Nati Srebro:
On Margin Maximization in Linear and ReLU Networks. NeurIPS 2022 - [c128]Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS 2022 - [i110]Gal Vardi, Ohad Shamir, Nathan Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR abs/2202.06233 (2022) - [i109]Idan Amir, Roi Livni, Nathan Srebro:
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. CoRR abs/2202.13328 (2022) - [i108]Itay Evron, Edward Moroshko, Rachel A. Ward, Nati Srebro, Daniel Soudry:
How catastrophic can catastrophic forgetting be in linear regression? CoRR abs/2205.09588 (2022) - [i107]Gene Li, Cong Ma, Nathan Srebro:
Pessimism for Offline Linear Contextual Bandits using 𝓁p Confidence Sets. CoRR abs/2205.10671 (2022) - [i106]Omar Montasser, Steve Hanneke, Nathan Srebro:
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR abs/2209.07369 (2022) - [i105]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR abs/2210.07082 (2022) - [i104]Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR abs/2210.12082 (2022) - 2021
- [j13]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): 20-44 (2021) - [c127]Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro:
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. AISTATS 2021: 2305-2313 - [c126]Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro:
Does Invariant Risk Minimization Capture Invariance? AISTATS 2021: 4069-4077 - [c125]Omar Montasser, Steve Hanneke, Nathan Srebro:
Adversarially Robust Learning with Unknown Perturbation Sets. COLT 2021: 3452-3482 - [c124]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT 2021: 4386-4437 - [c123]Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro:
Dropout: Explicit Forms and Capacity Control. ICML 2021: 351-361 - [c122]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: 468-477 - [c121]Ziwei Ji, Nathan Srebro, Matus Telgarsky:
Fast margin maximization via dual acceleration. ICML 2021: 4860-4869 - [c120]Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro:
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. ICML 2021: 7379-7389 - [c119]Blake E. Woodworth, Nathan Srebro:
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. NeurIPS 2021: 7333-7345 - [c118]Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro:
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS 2021: 20657-20668 - [c117]Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro:
On the Power of Differentiable Learning versus PAC and SQ Learning. NeurIPS 2021: 24340-24351 - [c116]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS 2021: 26818-26830 - [c115]Zhen Dai, Mina Karzand, Nathan Srebro:
Representation Costs of Linear Neural Networks: Analysis and Design. NeurIPS 2021: 26884-26896 - [i103]Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro:
Does Invariant Risk Minimization Capture Invariance? CoRR abs/2101.01134 (2021) - [i102]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR abs/2102.01583 (2021) - [i101]Omar Montasser, Steve Hanneke, Nathan Srebro:
Adversarially Robust Learning with Unknown Perturbation Sets. CoRR abs/2102.02145 (2021) - [i100]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 abs/2102.09769 (2021) - [i99]Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro:
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. CoRR abs/2103.01210 (2021) - [i98]Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro:
Eluder Dimension and Generalized Rank. CoRR abs/2104.06970 (2021) - [i97]Blake E. Woodworth, Nathan Srebro:
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. CoRR abs/2106.02720 (2021) - [i96]Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro:
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR abs/2106.09276 (2021) - [i95]Ziwei Ji, Nathan Srebro, Matus Telgarsky:
Fast Margin Maximization via Dual Acceleration. CoRR abs/2107.00595 (2021) - [i94]Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro:
On the Power of Differentiable Learning versus PAC and SQ Learning. CoRR abs/2108.04190 (2021) - [i93]Gal Vardi, Ohad Shamir, Nathan Srebro:
On Margin Maximization in Linear and ReLU Networks. CoRR abs/2110.02732 (2021) - [i92]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR abs/2110.02954 (2021) - [i91]Omar Montasser, Steve Hanneke, Nathan Srebro:
Transductive Robust Learning Guarantees. CoRR abs/2110.10602 (2021) - [i90]Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro:
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR abs/2112.04470 (2021) - [i89]Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang:
Exponential Family Model-Based Reinforcement Learning via Score Matching. CoRR abs/2112.14195 (2021) - 2020
- [c114]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: 2830-2840 - [c113]Yossi Arjevani, Ohad Shamir, Nathan Srebro:
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. ALT 2020: 111-132 - [c112]Pritish Kamath, Omar Montasser, Nathan Srebro:
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT 2020: 2236-2262 - [c111]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: 3635-3673 - [c110]Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro:
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. ICLR 2020 - [c109]Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. ICML 2020: 7010-7021 - [c108]Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro:
Fair Learning with Private Demographic Data. ICML 2020: 7066-7075 - [c107]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: 10334-10343 - [c106]Omar Montasser, Steve Hanneke, Nati Srebro:
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. NeurIPS 2020 - [c105]Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry:
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. NeurIPS 2020 - [c104]Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro:
Minibatch vs Local SGD for Heterogeneous Distributed Learning. NeurIPS 2020 - [c103]Lijia Zhou, Danica J. Sutherland, Nati Srebro:
On Uniform Convergence and Low-Norm Interpolation Learning. NeurIPS 2020 - [i88]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 abs/2002.07839 (2020) - [i87]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 abs/2002.09277 (2020) - [i86]Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro:
Fair Learning with Private Demographic Data. CoRR abs/2002.11651 (2020) - [i85]Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro:
Dropout: Explicit Forms and Capacity Control. CoRR abs/2003.03397 (2020) - [i84]Pritish Kamath, Omar Montasser, Nathan Srebro:
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR abs/2003.04180 (2020) - [i83]Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro:
Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow. CoRR abs/2004.01025 (2020) - [i82]Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. CoRR abs/2005.07652 (2020) - [i81]Blake E. Woodworth, Kumar Kshitij Patel, Nathan Srebro:
Minibatch vs Local SGD for Heterogeneous Distributed Learning. CoRR abs/2006.04735 (2020) - [i80]Lijia Zhou, Danica J. Sutherland, Nathan Srebro:
On Uniform Convergence and Low-Norm Interpolation Learning. CoRR abs/2006.05942 (2020) - [i79]Keshav Vemuri, Nathan Srebro:
Predictive Value Generalization Bounds. CoRR abs/2007.05073 (2020) - [i78]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 abs/2007.06738 (2020) - [i77]Omar Montasser, Steve Hanneke, Nathan Srebro:
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR abs/2010.12039 (2020)
2010 – 2019
- 2019
- [j12]Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang:
Stochastic Canonical Correlation Analysis. J. Mach. Learn. Res. 20: 167:1-167:46 (2019) - [c102]Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry:
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. AISTATS 2019: 3051-3059 - [c101]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: 3420-3428 - [c100]Weiran Wang, Nathan Srebro:
Stochastic Nonconvex Optimization with Large Minibatches. ALT 2019: 856-881 - [c99]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: 1319-1345 - [c98]Omar Montasser, Steve Hanneke, Nathan Srebro:
VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT 2019: 2512-2530 - [c97]Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro:
How do infinite width bounded norm networks look in function space? COLT 2019: 2667-2690 - [c96]Blake E. Woodworth, Nathan Srebro:
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. COLT 2019: 3202-3210 - [c95]Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro:
From Fair Decision Making To Social Equality. FAT 2019: 359-368 - [c94]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
The role of over-parametrization in generalization of neural networks. ICLR (Poster) 2019 - [c93]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML 2019: 1397-1405 - [c92]Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. ICML 2019: 1764-1773 - [c91]Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. ICML 2019: 4683-4692 - [i76]Nandana Sengupta, Nati Srebro, James Evans:
Simple Surveys: Response Retrieval Inspired by Recommendation Systems. CoRR abs/1901.09659 (2019) - [i75]Omar Montasser, Steve Hanneke, Nathan Srebro:
VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR abs/1902.04217 (2019) - [i74]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 abs/1902.04686 (2019) - [i73]Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro:
How do infinite width bounded norm networks look in function space? CoRR abs/1902.05040 (2019) - [i72]Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. CoRR abs/1904.10120 (2019) - [i71]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 abs/1905.07325 (2019) - [i70]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR abs/1906.09231 (2019) - [i69]Blake E. Woodworth, Nathan Srebro:
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. CoRR abs/1907.00762 (2019) - [i68]Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro:
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. CoRR abs/1910.01635 (2019) - [i67]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth:
Lower Bounds for Non-Convex Stochastic Optimization. CoRR abs/1912.02365 (2019) - 2018
- [j11]Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. J. Mach. Learn. Res. 19: 70:1-70:57 (2018) - [c90]Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro:
Efficient coordinate-wise leading eigenvector computation. ALT 2018: 806-820 - [c89]Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro:
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. ICLR (Poster) 2018 - [c88]Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. ICLR (Poster) 2018 - [c87]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Characterizing Implicit Bias in Terms of Optimization Geometry. ICML 2018: 1827-1836 - [c86]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. ITA 2018: 1-10 - [c85]Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. NeurIPS 2018: 6532-6541 - [c84]Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro:
On preserving non-discrimination when combining expert advice. NeurIPS 2018: 8386-8397 - [c83]Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro:
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. NeurIPS 2018: 8505-8515 - [c82]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro:
Implicit Bias of Gradient Descent on Linear Convolutional Networks. NeurIPS 2018: 9482-9491 - [i66]Weiran Wang, Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Stochastic Multi-Task Learning with Graph Regularization. CoRR abs/1802.03830 (2018) - [i65]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Characterizing Implicit Bias in Terms of Optimization Geometry. CoRR abs/1802.08246 (2018) - [i64]Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Nathan Srebro, Daniel Soudry:
Convergence of Gradient Descent on Separable Data. CoRR abs/1803.01905 (2018) - [i63]Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. CoRR abs/1803.04307 (2018) - [i62]Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro:
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. CoRR abs/1805.10222 (2018) - [i61]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. CoRR abs/1805.12076 (2018) - [i60]Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro:
Implicit Bias of Gradient Descent on Linear Convolutional Networks. CoRR abs/1806.00468 (2018) - [i59]Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry:
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. CoRR abs/1806.01796 (2018) - [i58]Yossi Arjevani, Ohad Shamir, Nathan Srebro:
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. CoRR abs/1806.10188 (2018) - [i57]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR abs/1807.00028 (2018) - [i56]Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nathan Srebro:
On preserving non-discrimination when combining expert advice. CoRR abs/1810.11829 (2018) - [i55]Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro:
From Fair Decision Making to Social Equality. CoRR abs/1812.02952 (2018) - 2017
- [j10]Avleen Singh Bijral, Anand D. Sarwate, Nathan Srebro:
Data-Dependent Convergence for Consensus Stochastic Optimization. IEEE Trans. Autom. Control. 62(9): 4483-4498 (2017) - [c81]Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro:
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. AISTATS 2017: 1150-1158 - [c80]Jialei Wang, Weiran Wang, Nathan Srebro:
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. COLT 2017: 1882-1919 - [c79]Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro:
Learning Non-Discriminatory Predictors. COLT 2017: 1920-1953 - [c78]Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. ICML 2017: 1203-1212 - [c77]Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. ICML 2017: 3636-3645 - [c76]Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht:
The Marginal Value of Adaptive Gradient Methods in Machine Learning. NIPS 2017: 4148-4158 - [c75]Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro:
Stochastic Approximation for Canonical Correlation Analysis. NIPS 2017: 4775-4784 - [c74]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro:
Exploring Generalization in Deep Learning. NIPS 2017: 5947-5956 - [c73]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Implicit Regularization in Matrix Factorization. NIPS 2017: 6151-6159 - [i54]Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro:
Learning Non-Discriminatory Predictors. CoRR abs/1702.06081 (2017) - [i53]Jialei Wang, Weiran Wang, Nathan Srebro:
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. CoRR abs/1702.06269 (2017) - [i52]Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang:
Stochastic Canonical Correlation Analysis. CoRR abs/1702.06533 (2017) - [i51]Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro:
Efficient coordinate-wise leading eigenvector computation. CoRR abs/1702.07834 (2017) - [i50]Dan Garber, Ohad Shamir, Nathan Srebro:
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. CoRR abs/1702.08169 (2017) - [i49]Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Geometry of Optimization and Implicit Regularization in Deep Learning. CoRR abs/1705.03071 (2017) - [i48]Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht:
The Marginal Value of Adaptive Gradient Methods in Machine Learning. CoRR abs/1705.08292 (2017) - [i47]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. CoRR abs/1705.09280 (2017) - [i46]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro:
Exploring Generalization in Deep Learning. CoRR abs/1706.08947 (2017) - [i45]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro:
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. CoRR abs/1707.09564 (2017) - [i44]Weiran Wang, Nathan Srebro:
Stochastic Nonconvex Optimization with Large Minibatches. CoRR abs/1709.08728 (2017) - [i43]Daniel Soudry, Elad Hoffer, Nathan Srebro:
The Implicit Bias of Gradient Descent on Separable Data. CoRR abs/1710.10345 (2017) - [i42]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR abs/1711.05305 (2017) - 2016
- [j9]Deanna Needell, Nathan Srebro, Rachel A. Ward:
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm. Math. Program. 155(1-2): 549-573 (2016) - [c72]Heejin Choi, Ofer Meshi, Nathan Srebro:
Fast and Scalable Structural SVM with Slack Rescaling. AISTATS 2016: 667-675 - [c71]Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multi-Task Learning. AISTATS 2016: 751-760 - [c70]Avleen Singh Bijral, Anand D. Sarwate, Nathan Srebro:
Data-dependent bounds on network gradient descent. Allerton 2016: 869-874 - [c69]Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro:
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. NIPS 2016: 766-774 - [c68]Moritz Hardt, Eric Price, Nati Srebro:
Equality of Opportunity in Supervised Learning. NIPS 2016: 3315-3323 - [c67]Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro:
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. NIPS 2016: 3477-3485 - [c66]Blake E. Woodworth, Nati Srebro:
Tight Complexity Bounds for Optimizing Composite Objectives. NIPS 2016: 3639-3647 - [c65]Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. NIPS 2016: 3873-3881 - [c64]Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi:
Normalized Spectral Map Synchronization. NIPS 2016: 4925-4933 - [c63]Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Data-Dependent Path Normalization in Neural Networks. ICLR (Poster) 2016 - [i41]Jialei Wang, Hai Wang, Nathan Srebro:
Reducing Runtime by Recycling Samples. CoRR abs/1602.02136 (2016) - [i40]Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multi-Task Learning with Shared Representation. CoRR abs/1603.02185 (2016) - [i39]Weiran Wang, Jialei Wang, Nathan Srebro:
Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. CoRR abs/1604.01870 (2016) - [i38]Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro:
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. CoRR abs/1605.07154 (2016) - [i37]Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. CoRR abs/1605.07221 (2016) - [i36]Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. CoRR abs/1605.07991 (2016) - [i35]Moritz Hardt, Eric Price, Nathan Srebro:
Equality of Opportunity in Supervised Learning. CoRR abs/1610.02413 (2016) - [i34]Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nathan Srebro:
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. CoRR abs/1610.03045 (2016) - 2015
- [j8]Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang:
Learning sparse low-threshold linear classifiers. J. Mach. Learn. Res. 16: 1275-1304 (2015) - [c62]Ofer Meshi, Nathan Srebro, Tamir Hazan:
Efficient Training of Structured SVMs via Soft Constraints. AISTATS 2015 - [c61]Weiran Wang, Raman Arora, Karen Livescu, Nathan Srebro:
Stochastic optimization for deep CCA via nonlinear orthogonal iterations. Allerton 2015: 688-695 - [c60]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. COLT 2015: 1376-1401 - [c59]Behnam Neyshabur, Nathan Srebro:
On Symmetric and Asymmetric LSHs for Inner Product Search. ICML 2015: 1926-1934 - [c58]Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro:
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. NIPS 2015: 2422-2430 - [c57]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. ICLR (Workshop) 2015 - [i33]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. CoRR abs/1503.00036 (2015) - [i32]Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro:
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. CoRR abs/1506.02617 (2015) - [i31]Martin Takác, Peter Richtárik, Nathan Srebro:
Distributed Mini-Batch SDCA. CoRR abs/1507.08322 (2015) - [i30]Heejin Choi, Yutaka Sasaki, Nathan Srebro:
Normalized Hierarchical SVM. CoRR abs/1508.02479 (2015) - [i29]Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multitask Learning. CoRR abs/1510.00633 (2015) - [i28]Weiran Wang, Raman Arora, Karen Livescu, Nathan Srebro:
Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations. CoRR abs/1510.02054 (2015) - [i27]Heejin Choi, Ofer Meshi, Nathan Srebro:
Fast and Scalable Structural SVM with Slack Rescaling. CoRR abs/1510.06002 (2015) - 2014
- [c56]Ohad Shamir, Nathan Srebro:
Distributed stochastic optimization and learning. Allerton 2014: 850-857 - [c55]Behnam Neyshabur, Yury Makarychev, Nathan Srebro:
Clustering, Hamming Embedding, Generalized LSH and the Max Norm. ALT 2014: 306-320 - [c54]Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. ICML 2014: 1000-1008 - [c53]Jialei Wang, Nathan Srebro, James Evans:
Active collaborative permutation learning. KDD 2014: 502-511 - [c52]Deanna Needell, Rachel A. Ward, Nathan Srebro:
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm. NIPS 2014: 1017-1025 - [i26]Behnam Neyshabur, Yury Makarychev, Nathan Srebro:
Clustering, Hamming Embedding, Generalized LSH and the Max Norm. CoRR abs/1405.3167 (2014) - [i25]Behnam Neyshabur, Nathan Srebro:
On Symmetric and Asymmetric LSHs for Inner Product Search. CoRR abs/1410.5518 (2014) - 2013
- [j7]Sivan Sabato, Nathan Srebro, Naftali Tishby:
Distribution-dependent sample complexity of large margin learning. J. Mach. Learn. Res. 14(1): 2119-2149 (2013) - [c51]Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro:
Learning Optimally Sparse Support Vector Machines. ICML (1) 2013: 266-274 - [c50]Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro:
Mini-Batch Primal and Dual Methods for SVMs. ICML (3) 2013: 1022-1030 - [c49]Sivan Sabato, Anand D. Sarwate, Nati Srebro:
Auditing: Active Learning with Outcome-Dependent Query Costs. NIPS 2013: 512-520 - [c48]Raman Arora, Andrew Cotter, Nati Srebro:
Stochastic Optimization of PCA with Capped MSG. NIPS 2013: 1815-1823 - [c47]Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour:
The Power of Asymmetry in Binary Hashing. NIPS 2013: 2823-2831 - [i24]Nathan Srebro:
Maximum Likelihood Bounded Tree-Width Markov Networks. CoRR abs/1301.2311 (2013) - [i23]Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nathan Srebro:
Mini-Batch Primal and Dual Methods for SVMs. CoRR abs/1303.2314 (2013) - [i22]Sivan Sabato, Anand D. Sarwate, Nathan Srebro:
Auditing: Active Learning with Outcome-Dependent Query Costs. CoRR abs/1306.2347 (2013) - [i21]Raman Arora, Andrew Cotter, Nathan Srebro:
Stochastic Optimization of PCA with Capped MSG. CoRR abs/1307.1674 (2013) - [i20]Deanna Needell, Nathan Srebro, Rachel A. Ward:
Stochastic gradient descent and the randomized Kaczmarz algorithm. CoRR abs/1310.5715 (2013) - [i19]Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan Salakhutdinov, Nathan Srebro:
The Power of Asymmetry in Binary Hashing. CoRR abs/1311.7662 (2013) - [i18]Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication Efficient Distributed Optimization using an Approximate Newton-type Method. CoRR abs/1312.7853 (2013) - 2012
- [c46]Raman Arora, Andrew Cotter, Karen Livescu, Nathan Srebro:
Stochastic optimization for PCA and PLS. Allerton Conference 2012: 861-868 - [c45]Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan:
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. ICML 2012 - [c44]Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro:
The Kernelized Stochastic Batch Perceptron. ICML 2012 - [c43]Ali Jalali, Nathan Srebro:
Clustering using Max-norm Constrained Optimization. ICML 2012 - [c42]Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov:
Matrix reconstruction with the local max norm. NIPS 2012: 944-952 - [c41]Andreas Argyriou, Rina Foygel, Nathan Srebro:
Sparse Prediction with the $k$-Support Norm. NIPS 2012: 1466-1474 - [c40]Shie Mannor, Nathan Srebro:
Preface. COLT 2012: 1.1-1.2 - [c39]Jian Peng, Tamir Hazan, Nathan Srebro, Jinbo Xu:
Approximate Inference by Intersecting Semidefinite Bound and Local Polytope. AISTATS 2012: 868-876 - [e1]Shie Mannor, Nathan Srebro, Robert C. Williamson:
COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland. JMLR Proceedings 23, JMLR.org 2012 [contents] - [i17]Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro:
Semi-supervised Learning with Density Based Distances. CoRR abs/1202.3702 (2012) - [i16]Ali Jalali, Nathan Srebro:
Clustering using Max-norm Constrained Optimization. CoRR abs/1202.5598 (2012) - [i15]Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro:
The Kernelized Stochastic Batch Perceptron. CoRR abs/1204.0566 (2012) - [i14]Sivan Sabato, Nati Srebro, Naftali Tishby:
Characterizing the Sample Complexity of Large-Margin Learning With Second-Order Statistics. CoRR abs/1204.1276 (2012) - [i13]Andreas Argyriou, Rina Foygel, Nathan Srebro:
Sparse Prediction with the k-Overlap Norm. CoRR abs/1204.5043 (2012) - [i12]Francesco Orabona, Andreas Argyriou, Nathan Srebro:
PRISMA: PRoximal Iterative SMoothing Algorithm. CoRR abs/1206.2372 (2012) - [i11]Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha:
Complexity of Inference in Graphical Models. CoRR abs/1206.3240 (2012) - [i10]Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov:
Matrix reconstruction with the local max norm. CoRR abs/1210.5196 (2012) - [i9]Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang:
Learning Sparse Low-Threshold Linear Classifiers. CoRR abs/1212.3276 (2012) - 2011
- [j6]Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter:
Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1): 3-30 (2011) - [c38]Andrew Cotter, Nathan Srebro, Joseph Keshet:
A GPU-tailored approach for training kernelized SVMs. KDD 2011: 805-813 - [c37]Xueyuan Zhou, Mikhail Belkin, Nathan Srebro:
An iterated graph laplacian approach for ranking on manifolds. KDD 2011: 877-885 - [c36]Elad Hazan, Tomer Koren, Nati Srebro:
Beating SGD: Learning SVMs in Sublinear Time. NIPS 2011: 1233-1241 - [c35]Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. NIPS 2011: 1647-1655 - [c34]Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro:
Learning with the weighted trace-norm under arbitrary sampling distributions. NIPS 2011: 2133-2141 - [c33]Nati Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. NIPS 2011: 2645-2653 - [c32]Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro:
Semi-supervised Learning with Density Based Distances. UAI 2011: 43-50 - [c31]Rina Foygel, Nathan Srebro:
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. COLT 2011: 315-340 - [c30]Xueyuan Zhou, Nathan Srebro:
Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. AISTATS 2011: 901-908 - [i8]Rina Foygel, Nathan Srebro:
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. CoRR abs/1102.3923 (2011) - [i7]Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nathan Srebro:
Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions. CoRR abs/1106.4251 (2011) - [i6]Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. CoRR abs/1106.4574 (2011) - [i5]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. CoRR abs/1107.4080 (2011) - [i4]Andrew Cotter, Joseph Keshet, Nathan Srebro:
Explicit Approximations of the Gaussian Kernel. CoRR abs/1109.4603 (2011) - 2010
- [j5]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability, Stability and Uniform Convergence. J. Mach. Learn. Res. 11: 2635-2670 (2010) - [j4]Shai Shalev-Shwartz, Nathan Srebro, Tong Zhang:
Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. SIAM J. Optim. 20(6): 2807-2832 (2010) - [c29]Percy Liang, Nati Srebro:
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning. ICML 2010: 647-654 - [c28]Jason D. Lee, Ben Recht, Ruslan Salakhutdinov, Nathan Srebro, Joel A. Tropp:
Practical Large-Scale Optimization for Max-norm Regularization. NIPS 2010: 1297-1305 - [c27]Sivan Sabato, Nathan Srebro, Naftali Tishby:
Tight Sample Complexity of Large-Margin Learning. NIPS 2010: 2038-2046 - [c26]Ruslan Salakhutdinov, Nathan Srebro:
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm. NIPS 2010: 2056-2064 - [c25]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low Noise and Fast Rates. NIPS 2010: 2199-2207 - [c24]Sivan Sabato, Nathan Srebro, Naftali Tishby:
Reducing Label Complexity by Learning From Bags. AISTATS 2010: 685-692 - [i3]Ruslan Salakhutdinov, Nathan Srebro:
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm. CoRR abs/1002.2780 (2010) - [i2]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low-Noise and Fast Rates. CoRR abs/1009.3896 (2010) - [i1]Sivan Sabato, Nathan Srebro, Naftali Tishby:
Tight Sample Complexity of Large-Margin Learning. CoRR abs/1011.5053 (2010)
2000 – 2009
- 2009
- [c23]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Stochastic Convex Optimization. COLT 2009 - [c22]Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability and Stability in the General Learning Setting. COLT 2009 - [c21]Boaz Nadler, Nathan Srebro, Xueyuan Zhou:
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data. NIPS 2009: 1330-1338 - 2008
- [j3]Maria-Florina Balcan, Avrim Blum, Nathan Srebro:
A theory of learning with similarity functions. Mach. Learn. 72(1-2): 89-112 (2008) - [c20]Maria-Florina Balcan, Avrim Blum, Nathan Srebro:
Improved Guarantees for Learning via Similarity Functions. COLT 2008: 287-298 - [c19]Shai Shalev-Shwartz, Nathan Srebro:
SVM optimization: inverse dependence on training set size. ICML 2008: 928-935 - [c18]Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro:
Fast Rates for Regularized Objectives. NIPS 2008: 1545-1552 - [c17]Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha:
Complexity of Inference in Graphical Models. UAI 2008: 70-78 - 2007
- [c16]Nathan Srebro:
How Good Is a Kernel When Used as a Similarity Measure? COLT 2007: 323-335 - [c15]Saharon Rosset, Grzegorz Swirszcz, Nathan Srebro, Ji Zhu:
l1 Regularization in Infinite Dimensional Feature Spaces. COLT 2007: 544-558 - [c14]Nathan Srebro:
Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? COLT 2007: 628-629 - [c13]Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman:
Uncovering shared structures in multiclass classification. ICML 2007: 17-24 - [c12]Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro:
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. ICML 2007: 807-814 - 2006
- [c11]Nathan Srebro, Shai Ben-David:
Learning Bounds for Support Vector Machines with Learned Kernels. COLT 2006: 169-183 - [c10]Nathan Srebro, Gregory Shakhnarovich, Sam T. Roweis:
An investigation of computational and informational limits in Gaussian mixture clustering. ICML 2006: 865-872 - 2005
- [c9]Nathan Srebro, Adi Shraibman:
Rank, Trace-Norm and Max-Norm. COLT 2005: 545-560 - [c8]Jason D. M. Rennie, Nathan Srebro:
Fast maximum margin matrix factorization for collaborative prediction. ICML 2005: 713-719 - 2004
- [b1]Nathan Srebro:
Learning with matrix factorizations. Massachusetts Institute of Technology, Cambridge, MA, USA, 2004 - [c7]Nathan Srebro, Noga Alon, Tommi S. Jaakkola:
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. NIPS 2004: 1321-1328 - [c6]Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola:
Maximum-Margin Matrix Factorization. NIPS 2004: 1329-1336 - 2003
- [j2]Nathan Srebro:
Maximum likelihood bounded tree-width Markov networks. Artif. Intell. 143(1): 123-138 (2003) - [j1]Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi S. Jaakkola:
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. Bioinform. 19(9): 1070-1078 (2003) - [c5]Nathan Srebro, Tommi S. Jaakkola:
Weighted Low-Rank Approximations. ICML 2003: 720-727 - [c4]Nathan Srebro, Tommi S. Jaakkola:
Linear Dependent Dimensionality Reduction. NIPS 2003: 145-152 - 2002
- [c3]Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi S. Jaakkola, Nathan Srebro:
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. WABI 2002: 506-520 - 2001
- [c2]David R. Karger, Nathan Srebro:
Learning Markov networks: maximum bounded tree-width graphs. SODA 2001: 392-401 - [c1]Nathan Srebro:
Maximum Likelihood Bounded Tree-Width Markov Networks. UAI 2001: 504-511
Coauthor Index
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of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-10 22:16 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
