Oral
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Junhong Lin · Volkan Cevher
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes over the data. We show that optimal generalization error bounds can be retained for distributed SGM provided that the partition level is not too large. Our results are superior to the state-of-the-art theory, covering the cases that the regression function may not be in the hypothesis spaces. Particularly, our results show that distributed SGM has a smaller theoretical computational complexity, compared with distributed kernel ridge regression (KRR) and classic SGM.
Author Information
Junhong Lin (EPFL)
Volkan Cevher (EPFL)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Poster: Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods »
Thu Jul 12th 06:15 -- 09:00 PM Room Hall B
More from the Same Authors
-
2019 Poster: Almost surely constrained convex optimization »
Olivier Fercoq · Ahmet Alacaoglu · Ion Necoara · Volkan Cevher -
2019 Poster: Finding Mixed Nash Equilibria of Generative Adversarial Networks »
Ya-Ping Hsieh · Chen Liu · Volkan Cevher -
2019 Poster: Efficient learning of smooth probability functions from Bernoulli tests with guarantees »
Paul Rolland · Ali Kavis · Alexander Niklaus Immer · Adish Singla · Volkan Cevher -
2019 Oral: Finding Mixed Nash Equilibria of Generative Adversarial Networks »
Ya-Ping Hsieh · Chen Liu · Volkan Cevher -
2019 Oral: Efficient learning of smooth probability functions from Bernoulli tests with guarantees »
Paul Rolland · Ali Kavis · Alexander Niklaus Immer · Adish Singla · Volkan Cevher -
2019 Oral: Almost surely constrained convex optimization »
Olivier Fercoq · Ahmet Alacaoglu · Ion Necoara · Volkan Cevher -
2019 Poster: On Certifying Non-Uniform Bounds against Adversarial Attacks »
Chen Liu · Ryota Tomioka · Volkan Cevher -
2019 Poster: Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator »
Alp Yurtsever · Suvrit Sra · Volkan Cevher -
2019 Poster: A Conditional-Gradient-Based Augmented Lagrangian Framework »
Alp Yurtsever · Olivier Fercoq · Volkan Cevher -
2019 Oral: Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator »
Alp Yurtsever · Suvrit Sra · Volkan Cevher -
2019 Oral: A Conditional-Gradient-Based Augmented Lagrangian Framework »
Alp Yurtsever · Olivier Fercoq · Volkan Cevher -
2019 Oral: On Certifying Non-Uniform Bounds against Adversarial Attacks »
Chen Liu · Ryota Tomioka · Volkan Cevher -
2018 Poster: A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming »
Alp Yurtsever · Olivier Fercoq · Francesco Locatello · Volkan Cevher -
2018 Oral: A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming »
Alp Yurtsever · Olivier Fercoq · Francesco Locatello · Volkan Cevher -
2018 Poster: Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games »
Ehsan Asadi Kangarshahi · Ya-Ping Hsieh · Mehmet Fatih Sahin · Volkan Cevher -
2018 Oral: Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games »
Ehsan Asadi Kangarshahi · Ya-Ping Hsieh · Mehmet Fatih Sahin · Volkan Cevher -
2018 Poster: Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces »
Junhong Lin · Volkan Cevher -
2018 Oral: Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces »
Junhong Lin · Volkan Cevher -
2017 Poster: Robust Submodular Maximization: A Non-Uniform Partitioning Approach »
Ilija Bogunovic · Slobodan Mitrovic · Jonathan Scarlett · Volkan Cevher -
2017 Talk: Robust Submodular Maximization: A Non-Uniform Partitioning Approach »
Ilija Bogunovic · Slobodan Mitrovic · Jonathan Scarlett · Volkan Cevher
