Katyusha: The First Direct Acceleration of Stochastic Gradient Methods

18 March 2016

Papers citing "Katyusha: The First Direct Acceleration of Stochastic Gradient Methods"

50 / 297 papers shown

Title
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization Wei Zhang Arif Hassan Zidan Afrar Jahin Wei Zhang Tianming Liu ODL 7 0 0 16 May 2025
Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Study Wei Zhang Arif Hassan Zidan Afrar Jahin Wei Zhang Tianming Liu 7 0 0 16 May 2025
Adaptive Diffusion Policy Optimization for Robotic Manipulation Huiyun Jiang Zhuang Yang 34 0 0 13 May 2025
Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation Ninh Pham Rasmus Pagh 42 0 0 13 May 2025
Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy Gleb Molodtsov Daniil Medyakov Sergey Skorik Nikolas Khachaturov Shahane Tigranyan Vladimir Aletov A. Avetisyan Martin Takáč Aleksandr Beznosikov AAML 37 0 0 12 May 2025
Accelerated Distributed Optimization with Compression and Error Feedback Yuan Gao Anton Rodomanov Jeremy Rack Sebastian U. Stich 56 0 0 11 Mar 2025
Quantum Non-Linear Bandit Optimization Zakaria Shams Siam Chaowen Guan Chong Liu 34 0 0 04 Mar 2025
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling Daniil Medyakov Gleb Molodtsov S. Chezhegov Alexey Rebrikov Aleksandr Beznosikov 107 0 0 21 Feb 2025
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 36 0 0 28 Jan 2025
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity Dmitry Bylinkin Aleksandr Beznosikov 76 1 0 21 Dec 2024
On the SAGA algorithm with decreasing step Luis Fredes Bernard Bercu Eméric Gbaguidi 29 1 0 02 Oct 2024
Debiasing Federated Learning with Correlated Client Participation Zhenyu Sun Ziyang Zhang Zheng Xu Gauri Joshi Pranay Sharma Ermin Wei FedML 29 0 0 02 Oct 2024
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning Dmitry Bylinkin Kirill Degtyarev Aleksandr Beznosikov FedML 34 0 0 22 Sep 2024
Gradient-Free Method for Heavily Constrained Nonconvex Optimization Wanli Shi Hongchang Gao Bin Gu 21 5 0 31 Aug 2024
Efficient Continual Finite-Sum Minimization Ioannis Mavrothalassitis Stratis Skoulakis L. Dadi V. Cevher 40 0 0 07 Jun 2024
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization Yexin Zhang Chenyi Zhang Cong Fang Liwei Wang Tongyang Li 48 2 0 05 Jun 2024
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit Jason D. Lee Kazusato Oko Taiji Suzuki Denny Wu MLT 87 21 0 03 Jun 2024
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity Qihao Zhou Haishan Ye Luo Luo 26 0 0 25 May 2024
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems Michal Dereziñski Daniel LeJeune Deanna Needell E. Rebrova 42 4 0 09 May 2024
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning Haobin Zhang Zhuang Yang 40 0 0 08 May 2024
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients Sachin Garg A. Berahas Michal Dereziñski 46 1 0 23 Apr 2024
Improving the Bit Complexity of Communication for Distributed Convex Optimization Mehrdad Ghadiri Yin Tat Lee Swati Padmanabhan W. Swartworth David P. Woodruff Guanghao Ye 35 5 0 28 Mar 2024
A Novel Loss Function-based Support Vector Machine for Binary Classification Yan Li Liping Zhang 22 0 0 25 Mar 2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction Yury Demidovich Grigory Malinovsky Peter Richtárik 56 2 0 11 Mar 2024
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition Yunyan Bai Yuxing Liu Luo Luo 26 0 0 04 Feb 2024
FedCore: Straggler-Free Federated Learning with Distributed Coresets Hongpeng Guo Haotian Gu Xiaoyang Wang Bo Chen Eun Kyung Lee Tamar Eilam Deming Chen Klara Nahrstedt FedML 32 1 0 31 Jan 2024
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 47 4 0 10 Jan 2024
From Optimization to Control: Quasi Policy Iteration Mohammad Amin Sharifi Kolarijani Peyman Mohajerin Esfahani 32 2 0 18 Nov 2023
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 38 1 0 09 Nov 2023
An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent Zhao Song Chiwun Yang 36 9 0 17 Oct 2023
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions Xu Cai Ahmet Alacaoglu Jelena Diakonikolas 49 7 0 04 Oct 2023
Ordering for Non-Replacement SGD Yuetong Xu Baharan Mirzasoleiman 23 0 0 28 Jun 2023
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning Kuan-Fu Ding Jingyang Li Kim-Chuan Toh 33 8 0 26 Jun 2023
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters Zhenyu Sun Xiaochun Niu Ermin Wei FedML MLT 33 21 0 06 Jun 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features Aleksandr Beznosikov David Dobre Gauthier Gidel 27 5 0 23 Apr 2023
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization Zebang Shen Hui Qian Tongzhou Mu Chao Zhang ODL 14 0 0 23 Apr 2023
Understanding Accelerated Gradient Methods: Lyapunov Analyses and Hamiltonian Assisted Interpretations Penghui Fu Zhiqiang Tan 8 0 0 20 Apr 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis Dachao Lin Yuze Han Haishan Ye Zhihua Zhang 25 11 0 15 Apr 2023
Unified analysis of SGD-type methods Eduard A. Gorbunov 30 2 0 29 Mar 2023
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization Cheuk Yin Lin Chaobing Song Jelena Diakonikolas 27 5 0 28 Mar 2023
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise Zijian Liu Jiawei Zhang Zhengyuan Zhou 34 12 0 14 Feb 2023
Improved Rate of First Order Algorithms for Entropic Optimal Transport Yiling Luo Yiling Xie X. Huo 19 8 0 23 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 36 2 0 09 Jan 2023
Variance-Reduced Conservative Policy Iteration Naman Agarwal Brian Bullins Karan Singh 32 3 0 12 Dec 2022
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization Xu Cai Chaobing Song Stephen J. Wright Jelena Diakonikolas 36 14 0 09 Dec 2022
Statistical and Computational Guarantees for Influence Diagnostics Jillian R. Fisher Lang Liu Krishna Pillutla Y. Choi Zaïd Harchaoui TDI 29 0 0 08 Dec 2022
Stochastic Steffensen method Minda Zhao Zehua Lai Lek-Heng Lim ODL 15 3 0 28 Nov 2022
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 20 9 0 26 Nov 2022
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates Zachary Frangella Pratik Rathore Shipu Zhao Madeleine Udell 13 5 0 16 Nov 2022
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization Ali Kavis Stratis Skoulakis Kimon Antonakopoulos L. Dadi V. Cevher 27 15 0 03 Nov 2022