Slavomír Hanzely

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All published works

Action	Title	Year	Authors
+ PDF Chat	$\psi$DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning	2024	Klea Ziu Slavomír Hanzely Loka Li Kun Zhang Martin Takáč Dmitry Kamzolov
+ PDF Chat	Damped Newton Method with Near-Optimal Global $\mathcal {O}\left(k^{-3} \right)$ Convergence Rate	2024	Slavomír Hanzely Farshed Abdukhakimov Martin Takáč
+	Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes	2023	Konstantin Mishchenko Slavomír Hanzely Peter Richtárik
+	Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates	2023	Slavomír Hanzely
+	Adaptive Optimization Algorithms for Machine Learning	2023	Slavomír Hanzely
+	Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation	2022	Rustem Islamov Xun Qian Slavomír Hanzely Mher Safaryan Peter Richtárik
+	A Damped Newton Method Achieves Global $O\left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate	2022	Slavomír Hanzely Dmitry Kamzolov Dmitry Pasechnyuk Alexander Gasnikov Peter Richtárik Martin Takáč
+	ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation	2021	Zhize Li Slavomír Hanzely Peter Richtárik
+	Lower Bounds and Optimal Algorithms for Personalized Federated Learning	2020	Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik
+	Adaptive Learning of the Optimal Mini-Batch Size of SGD.	2020	Motasem Alfarra Slavomír Hanzely Alyazeed Albasyoni Bernard Ghanem Peter Richtárik
+	Lower Bounds and Optimal Algorithms for Personalized Federated Learning	2020	Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik

Common Coauthors

Coauthor	Papers Together
Peter Richtárik	7
Martin Takáč	3
Filip Hanzely	2
Dmitry Kamzolov	2
Samuel Horváth	2
Bernard Ghanem	1
Alexander Gasnikov	1
Mher Safaryan	1
Kun Zhang	1
Klea Ziu	1
Zhize Li	1
Farshed Abdukhakimov	1
Loka Li	1
Dmitry Pasechnyuk	1
Rustem Islamov	1
Xun Qian	1
Konstantin Mishchenko	1
Motasem Alfarra	1
Alyazeed Albasyoni	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Optimal methods of smooth convex minimization	1985	Arkadi Nemirovski Yu. E. Nesterov	1
+	Robust Stochastic Approximation Approach to Stochastic Programming	2009	Arkadi Nemirovski Anatoli Juditsky Guanghui Lan Alexander Shapiro	1
+ PDF Chat	Adaptive step size random search	1968	M. Schumer K. Steiglitz	1
+ PDF Chat	Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization	2014	Saeed Ghadimi Guanghui Lan Hongchao Zhang	1
+ PDF Chat	On lower complexity bounds for large-scale smooth convex optimization	2014	Cristóbal Guzmán Arkadi Nemirovski	1
+	Two-Point Step Size Gradient Methods	1988	Jonathan Barzilai Jonathan M. Borwein	1
+	A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems	2009	Amir Beck Marc Teboulle	1
+ PDF Chat	Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization	2014	Shai Shalev‐Shwartz Tong Zhang	1
+ PDF Chat	Information-Based Complexity, Feedback and Dynamics in Convex Programming	2011	Maxim Raginsky Alexander Rakhlin	1
+	An Accelerated Randomized Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization	2015	Qihang Lin Zhaosong Lu Lin Xiao	1
+	Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization	2011	Mark Schmidt Nicolas Le Roux Francis Bach	1
+	Federated Learning of Deep Networks using Model Averaging	2016	H. Brendan McMahan Eider Moore Daniel Ramage Blaise Agüera y Arcas	1
+	Federated Optimization: Distributed Machine Learning for On-Device Intelligence	2016	Jakub Konečný H. Brendan McMahan Daniel Ramage Peter Richtárik	1
+ PDF Chat	Distributed Multi-Task Relationship Learning	2017	Sulin Liu Sinno Jialin Pan Qirong Ho	1
+ PDF Chat	Fixed-point optimization of deep neural networks with adaptive step size retraining	2017	Sungho Shin Yoonho Boo Wonyong Sung	1
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	1
+	Scaling SGD Batch Size to 32K for ImageNet Training.	2017	Yang You Igor Gitman Boris Ginsburg	1
+	Large Batch Training of Convolutional Networks	2017	Yang You Igor Gitman Boris Ginsburg	1
+	Distributed Stochastic Multi-Task Learning with Graph Regularization	2018	Weiran Wang Jialei Wang Mladen Kolar Nathan Srebro	1
+	Revisiting Small Batch Training for Deep Neural Networks	2018	Dominic Masters Carlo Luschi	1
+	Stochastic Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching	2018	Robert M. Gower Peter Richtárik Francis Bach	1
+	A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates	2018	Kaiwen Zhou Fanhua Shang James Cheng	1
+	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization	2018	Blake Woodworth Jialei Wang Adam Smith Brendan McMahan Nati Srebro	1
+	Federated Learning with Non-IID Data	2018	Yue Zhao Meng Li Liangzhen Lai Naveen Suda Damon Civin Vikas Chandra	1
+	Direct Acceleration of SAGA using Sampled Negative Momentum	2018	Kaiwen Zhou	1
+	Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization	2018	Eric Balkanski Yaron Singer	1
+	SEGA: Variance Reduction via Gradient Sketching	2018	Filip Hanzely Konstantin Mishchenko Peter Richtárik	1
+	Lower Bounds for Parallel and Randomized Convex Optimization	2018	Jelena Diakonikolas Cristóbal Guzmán	1
+	Federated Learning for Mobile Keyboard Prediction	2018	Andrew Hard Chloé Kiddon Daniel Ramage Françoise Beaufays Hubert Eichner K. Praveen Kumar Rao Rajiv Mathews Sean Augenstein	1
+	Optimal mini-batch and step sizes for SAGA	2019	Nidham Gazagnadou Robert M. Gower Joseph Salmon	1
+	Federated Optimization in Heterogeneous Networks	2018	Li Tian Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith	1
+	Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization	2019	Quoc Tran-Dinh Nhan H. Pham Dzung T. Phan Lam M. Nguyen	1
+	Convergence of Distributed Stochastic Variance Reduced Methods Without Sampling Extra Data	2020	Shicong Cen Huishuai Zhang Yuejie Chi Wei Chen Tie‐Yan Liu	1
+	L-SVRG and L-Katyusha with Arbitrary Sampling	2019	Xun Qian Zheng Qu Peter Richtárik	1
+	Variational Federated Multi-Task Learning	2019	Luca Corinzia Joachim M. Buhmann	1
+	Better Mini-Batch Algorithms via Accelerated Gradient Methods	2011	Andrew Cotter Ohad Shamir Nathan Srebro Karthik Sridharan	1
+	SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives	2014	Aaron Defazio Francis Bach Simon Lacoste-Julien	1
+	Barzilai-Borwein step size for stochastic gradient descent	2016	Conghui Tan Shiqian Ma Yu‐Hong Dai Yuqiu Qian	1
+	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient	2017	Lam M. Nguyen Jie Liu Katya Scheinberg Martin Takáč	1
+	Direct Acceleration of SAGA using Sampled Negative Momentum	2019	Kaiwen Zhou Qinghua Ding Fanhua Shang James Cheng Danli Li Zhi‐Quan Luo	1
+	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator	2018	Cong Fang Chris Junchi Li Zhouchen Lin Tong Zhang	1
+	A Simple Practical Accelerated Method for Finite Sums	2016	Aaron Defazio	1
+ PDF Chat	Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming	2013	Saeed Ghadimi Guanghui Lan	1
+	Non-convex Finite-Sum Optimization Via SCSG Methods	2017	Lihua Lei Cheng Ju Jianbo Chen Michael I. Jordan	1
+	Don't decay the learning rate, increase the batch size	2018	Samuel Smith Pieter-Jan Kindermans Chris Ying Quoc V. Le	1
+ PDF Chat	Communication-efficient algorithms for decentralized and stochastic optimization	2018	Guanghui Lan Soomin Lee Yi Zhou	1
+	Stochastic Variance Reduction for Nonconvex Optimization	2016	Sashank J. Reddi Ahmed Hefny Suvrit Sra Barnabás Póczos Alex Smola	1
+ PDF Chat	An optimal randomized incremental gradient method	2017	Guanghui Lan Yi Zhou	1
+	Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization	2019	Rong Ge Zhize Li Weiyao Wang Xiang Wang	1
+	Adaptive Gradient-Based Meta-Learning Methods	2019	Mikhail Khodak Maria-Florina Balcan Ameet Talwalkar	1