Timezone: »
Oral
Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner · Tomer Koren · Brendan McMahan · Nati Srebro · Kunal Talwar
We consider convex SGD updates with a blockcyclic structure, i.e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution. This situation arises, e.g., in Federated Learning where the mobile devices available for updates at different times during the day have different characteristics. We show that such block-cyclic structure can significantly deteriorate the performance of SGD, but propose a simple correction approach that allows prediction with the same performance guarantees as for i.i.d., non-cyclic, sampling.
Author Information
Hubert Eichner (Google)
Tomer Koren (Google Brain)
Brendan McMahan (Google)
Nati Srebro (Toyota Technological Institute at Chicago)
Kunal Talwar (Google)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Poster: Semi-Cyclic Stochastic Gradient Descent »
Fri. Jun 14th 01:30 -- 04:00 AM Room Pacific Ballroom #148
More from the Same Authors
-
2023 : When is Agnostic Reinforcement Learning Statistically Tractable? »
Gene Li · Zeyu Jia · Alexander Rakhlin · Ayush Sekhari · Nati Srebro -
2023 : On the Still Unreasonable Effectiveness of Federated Averaging for Heterogeneous Distributed Learning »
Kumar Kshitij Patel · Margalit Glasgow · Lingxiao Wang · Nirmit Joshi · Nati Srebro -
2023 : Brendan McMahan: Advances in Privacy and Federated Learning, with Applications to GBoard »
Brendan McMahan -
2023 Poster: Federated Online and Bandit Convex Optimization »
Kumar Kshitij Patel · Lingxiao Wang · Aadirupa Saha · Nati Srebro -
2023 Poster: Continual Learning in Linear Classification on Separable Data »
Itay Evron · Edward Moroshko · Gon Buzaglo · Maroun Khriesh · Badea Marjieh · Nati Srebro · Daniel Soudry -
2022 Poster: Implicit Bias of the Step Size in Linear Diagonal Neural Networks »
Mor Shpigel Nacson · Kavya Ravichandran · Nati Srebro · Daniel Soudry -
2022 Spotlight: Implicit Bias of the Step Size in Linear Diagonal Neural Networks »
Mor Shpigel Nacson · Kavya Ravichandran · Nati Srebro · Daniel Soudry -
2021 Poster: Fast margin maximization via dual acceleration »
Ziwei Ji · Nati Srebro · Matus Telgarsky -
2021 Poster: Practical and Private (Deep) Learning Without Sampling or Shuffling »
Peter Kairouz · Brendan McMahan · Shuang Song · Om Dipakbhai Thakkar · Abhradeep Guha Thakurta · Zheng Xu -
2021 Spotlight: Fast margin maximization via dual acceleration »
Ziwei Ji · Nati Srebro · Matus Telgarsky -
2021 Spotlight: Practical and Private (Deep) Learning Without Sampling or Shuffling »
Peter Kairouz · Brendan McMahan · Shuang Song · Om Dipakbhai Thakkar · Abhradeep Guha Thakurta · Zheng Xu -
2021 Poster: Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels »
Eran Malach · Pritish Kamath · Emmanuel Abbe · Nati Srebro -
2021 Spotlight: Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels »
Eran Malach · Pritish Kamath · Emmanuel Abbe · Nati Srebro -
2021 Poster: Dropout: Explicit Forms and Capacity Control »
Raman Arora · Peter Bartlett · Poorya Mianjy · Nati Srebro -
2021 Spotlight: Dropout: Explicit Forms and Capacity Control »
Raman Arora · Peter Bartlett · Poorya Mianjy · Nati Srebro -
2021 Poster: On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent »
Shahar Azulay · Edward Moroshko · Mor Shpigel Nacson · Blake Woodworth · Nati Srebro · Amir Globerson · Daniel Soudry -
2021 Oral: On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent »
Shahar Azulay · Edward Moroshko · Mor Shpigel Nacson · Blake Woodworth · Nati Srebro · Amir Globerson · Daniel Soudry -
2020 : Keynote Session 5: Advances and Open Problems in Federated Learning, by Brendan McMahan (Google) »
Brendan McMahan -
2020 Poster: Efficiently Learning Adversarially Robust Halfspaces with Noise »
Omar Montasser · Surbhi Goel · Ilias Diakonikolas · Nati Srebro -
2020 Poster: Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently »
Asaf Cassel · Alon Cohen · Tomer Koren -
2020 Poster: Is Local SGD Better than Minibatch SGD? »
Blake Woodworth · Kumar Kshitij Patel · Sebastian Stich · Zhen Dai · Brian Bullins · Brendan McMahan · Ohad Shamir · Nati Srebro -
2020 Poster: Fair Learning with Private Demographic Data »
Hussein Mozannar · Mesrob Ohannessian · Nati Srebro -
2019 : Nati Srebro: Optimization’s Untold Gift to Learning: Implicit Regularization »
Nati Srebro -
2019 : Panel Discussion (Nati Srebro, Dan Roy, Chelsea Finn, Mikhail Belkin, Aleksander Mądry, Jason Lee) »
Nati Srebro · Daniel Roy · Chelsea Finn · Mikhail Belkin · Aleksander Madry · Jason Lee -
2019 Workshop: Understanding and Improving Generalization in Deep Learning »
Dilip Krishnan · Hossein Mobahi · Behnam Neyshabur · Behnam Neyshabur · Peter Bartlett · Dawn Song · Nati Srebro -
2019 Poster: Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret »
Alon Cohen · Tomer Koren · Yishay Mansour -
2019 Oral: Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret »
Alon Cohen · Tomer Koren · Yishay Mansour -
2019 Poster: Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints »
Andrew Cotter · Maya Gupta · Heinrich Jiang · Nati Srebro · Karthik Sridharan · Serena Wang · Blake Woodworth · Seungil You -
2019 Poster: Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models »
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry -
2019 Oral: Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints »
Andrew Cotter · Maya Gupta · Heinrich Jiang · Nati Srebro · Karthik Sridharan · Serena Wang · Blake Woodworth · Seungil You -
2019 Oral: Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models »
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry -
2018 Poster: Online Linear Quadratic Control »
Alon Cohen · Avinatan Hasidim · Tomer Koren · Nevena Lazic · Yishay Mansour · Kunal Talwar -
2018 Oral: Online Linear Quadratic Control »
Alon Cohen · Avinatan Hasidim · Tomer Koren · Nevena Lazic · Yishay Mansour · Kunal Talwar -
2018 Poster: Shampoo: Preconditioned Stochastic Tensor Optimization »
Vineet Gupta · Tomer Koren · Yoram Singer -
2018 Poster: Characterizing Implicit Bias in Terms of Optimization Geometry »
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro -
2018 Oral: Characterizing Implicit Bias in Terms of Optimization Geometry »
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro -
2018 Oral: Shampoo: Preconditioned Stochastic Tensor Optimization »
Vineet Gupta · Tomer Koren · Yoram Singer -
2017 Poster: Efficient Distributed Learning with Sparsity »
Jialei Wang · Mladen Kolar · Nati Srebro · Tong Zhang -
2017 Poster: Distributed Mean Estimation with Limited Communication »
Ananda Theertha Suresh · Felix Xinnan Yu · Sanjiv Kumar · Brendan McMahan -
2017 Talk: Efficient Distributed Learning with Sparsity »
Jialei Wang · Mladen Kolar · Nati Srebro · Tong Zhang -
2017 Talk: Distributed Mean Estimation with Limited Communication »
Ananda Theertha Suresh · Felix Xinnan Yu · Sanjiv Kumar · Brendan McMahan -
2017 Poster: Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis »
Dan Garber · Ohad Shamir · Nati Srebro -
2017 Talk: Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis »
Dan Garber · Ohad Shamir · Nati Srebro