Stop the war!Остановите войну!
for scientists:
Google
Google Scholar
Semantic Scholar
IA Scholar
CiteSeerX
ORCIDNathan Srebro
Nati Srebro
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2022
[i105]Gal Vardi, Ohad Shamir, Nathan Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR abs/2202.06233 (2022)- [i104]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) - 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 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 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 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 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 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]