Stochastic model-based minimization of weakly convex functions

17 March 2018

Papers citing "Stochastic model-based minimization of weakly convex functions"

48 / 48 papers shown

Title
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization Tianyi Lin Chi Jin Michael I. Jordan 52 6 0 28 Jan 2025
Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis Jiajin Li Lingling Zhu Anthony Man-Cho So 54 4 0 17 Jan 2025
Towards Certified Unlearning for Deep Neural Networks Binchi Zhang Yushun Dong Tianhao Wang Jundong Li MU 62 7 0 01 Aug 2024
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 35 0 0 19 Jul 2024
Single-loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions Quanqi Hu Qi Qi Zhaosong Lu Tianbao Yang 34 1 0 28 May 2024
Random Scaling and Momentum for Non-smooth Non-convex Optimization Qinzi Zhang Ashok Cutkosky 35 4 0 16 May 2024
Non-Convex Stochastic Composite Optimization with Polyak Momentum Yuan Gao Anton Rodomanov Sebastian U. Stich 29 6 0 05 Mar 2024
Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints Hari Dahal Wei Liu Yangyang Xu 29 5 0 15 Nov 2023
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization Reza Mirzaeifard Naveen K. D. Venkategowda A. Jung Stefan Werner 19 0 0 31 Aug 2023
Provable convergence guarantees for black-box variational inference Justin Domke Guillaume Garrigos Robert Mansel Gower 18 18 0 04 Jun 2023
Modified Gauss-Newton Algorithms under Noise Krishna Pillutla Vincent Roulet Sham Kakade Zaïd Harchaoui 11 3 0 18 May 2023
MoMo: Momentum Models for Adaptive Learning Rates Fabian Schaipp Ruben Ohana Michael Eickenberg Aaron Defazio Robert Mansel Gower 30 10 0 12 May 2023
Decentralized Adversarial Training over Graphs Ying Cao Elsa Rizk Stefan Vlaski A. H. Sayed AAML 35 1 0 23 Mar 2023
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold S. Schechtman D. Tiapkin Michael Muehlebach Eric Moulines 27 6 0 16 Mar 2023
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning Ezekiel Williams C. Bredenberg Guillaume Lajoie 11 6 0 24 Feb 2023
Deterministic Nonsmooth Nonconvex Optimization Michael I. Jordan Guy Kornowski Tianyi Lin Ohad Shamir Manolis Zampetakis 49 24 0 16 Feb 2023
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule Maor Ivgi Oliver Hinder Y. Carmon ODL 24 56 0 08 Feb 2023
Robust variance-regularized risk minimization with concomitant scaling Matthew J. Holland 28 1 0 27 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 32 2 0 09 Jan 2023
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization Tianyi Lin Zeyu Zheng Michael I. Jordan 49 51 0 12 Sep 2022
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games Fivos Kalogiannis Ioannis Anagnostides Ioannis Panageas Emmanouil-Vasileios Vlatakis-Gkaragkounis Vaggos Chatziafratis S. Stavroulakis 31 13 0 03 Aug 2022
AUC Maximization in the Era of Big Data and AI: A Survey Tianbao Yang Yiming Ying 33 178 0 28 Mar 2022
Flexible risk design using bi-directional dispersion Matthew J. Holland 32 5 0 28 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 21 46 0 09 Mar 2022
Consistent Approximations in Composite Optimization J. Royset 14 8 0 13 Jan 2022
Mitigating Divergence of Latent Factors via Dual Ascent for Low Latency Event Prediction Models A. Shtoff Yair Koren 9 0 0 15 Nov 2021
Learning equilibria with personalized incentives in a class of nonmonotone games F. Fabiani Andrea Simonetto Paul Goulart 14 11 0 06 Nov 2021
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis Jikai Jin Samir Bhatt Haiyang Wang Liwei Wang 25 47 0 24 Oct 2021
Tight and Robust Private Mean Estimation with Few Users Cheng-Han Chiang Vahab Mirrokni Hung-yi Lee FedML 31 28 0 22 Oct 2021
Stochastic Bias-Reduced Gradient Methods Hilal Asi Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 14 29 0 17 Jun 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency Yuyang Deng M. Mahdavi 27 58 0 25 Feb 2021
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness Vien V. Mai M. Johansson 16 38 0 12 Feb 2021
Block majorization-minimization with diminishing radius for constrained nonconvex optimization Hanbaek Lyu Yuchen Li 13 10 0 07 Dec 2020
Coping with Label Shift via Distributionally Robust Optimisation J. Zhang A. Menon Andreas Veit Srinadh Bhojanapalli Sanjiv Kumar S. Sra OOD 21 70 0 23 Oct 2020
Practical Precoding via Asynchronous Stochastic Successive Convex Approximation Basil M. Idrees J. Akhtar K. Rajawat 8 6 0 03 Oct 2020
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization Benjamin Grimmer Haihao Lu Pratik Worah Vahab Mirrokni 29 9 0 15 Jun 2020
A Stochastic Subgradient Method for Distributionally Robust Non-Convex Learning Mert Gurbuzbalaban A. Ruszczynski Landi Zhu 13 9 0 08 Jun 2020
Geometry-Aware Gradient Algorithms for Neural Architecture Search Liam Li M. Khodak Maria-Florina Balcan Ameet Talwalkar 22 66 0 16 Apr 2020
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning Yifan Hu Siqi Zhang Xin Chen Niao He ODL 28 54 0 25 Feb 2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization Quoc Tran-Dinh Nhan H. Pham Lam M. Nguyen 13 22 0 17 Feb 2020
Protection Against Reconstruction and Its Applications in Private Federated Learning Abhishek Bhowmick John C. Duchi Julien Freudiger Gaurav Kapoor Ryan M. Rogers FedML 14 357 0 03 Dec 2018
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence Yi Tian Xu Qi Qi Qihang Lin R. L. Jin Tianbao Yang 35 40 0 28 Nov 2018
A Sufficient Condition for Convergences of Adam and RMSProp Fangyu Zou Li Shen Zequn Jie Weizhong Zhang Wei Liu 19 362 0 23 Nov 2018
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity Hilal Asi John C. Duchi 6 123 0 12 Oct 2018
Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning Hassan Rafique Mingrui Liu Qihang Lin Tianbao Yang 15 107 0 04 Oct 2018
Stochastic Methods for Composite and Weakly Convex Optimization Problems John C. Duchi Feng Ruan 10 126 0 24 Mar 2017
Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition Yi Tian Xu Qihang Lin Tianbao Yang 28 11 0 04 Jul 2016
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark W. Schmidt Francis R. Bach 121 259 0 10 Dec 2012