Amortized Implicit Differentiation for Stochastic Bilevel Optimization

29 November 2021

Papers citing "Amortized Implicit Differentiation for Stochastic Bilevel Optimization"

35 / 35 papers shown

Title
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy Chen Wang Kaiyi Ji Junyi Geng Zhongqiang Ren Taimeng Fu ... Yi Du Qihang Li Yue Yang Xiao Lin Zhipeng Zhao SSL 129 10 0 28 Jan 2025
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems Huaqing Zhang Lesi Chen Jing Xu J.N. Zhang 99 1 0 28 Jan 2025
Fully First-Order Methods for Decentralized Bilevel Optimization Xiaoyu Wang Xuxing Chen Shiqian Ma Tong Zhang 82 0 0 25 Oct 2024
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity Yan Yang Bin Gao Ya-xiang Yuan 99 2 0 30 May 2024
LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace Bin Gao Yan Yang Ya-xiang Yuan 67 2 0 04 Apr 2024
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization M. D. Santis Jordan Frécon Francesco Rinaldi Saverio Salzo Martin Schmidt Martin Schmidt 73 0 0 21 Aug 2023
Provably Faster Algorithms for Bilevel Optimization Junjie Yang Kaiyi Ji Yingbin Liang 70 135 0 08 Jun 2021
Efficient and Modular Implicit Differentiation Mathieu Blondel Quentin Berthet Marco Cuturi Roy Frostig Stephan Hoyer Felipe Llinares-López Fabian Pedregosa Jean-Philippe Vert 39 227 0 31 May 2021
Lower Bounds and Accelerated Algorithms for Bilevel Optimization Kaiyi Ji Yingbin Liang 36 57 0 07 Feb 2021
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond Risheng Liu Jiaxin Gao Jin Zhang Deyu Meng Zhouchen Lin AI4CE 127 225 0 27 Jan 2021
Convergence Properties of Stochastic Hypergradients Riccardo Grazzi Massimiliano Pontil Saverio Salzo 80 26 0 13 Nov 2020
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance Thinh T. Doan 62 46 0 03 Nov 2020
Bilevel Optimization: Convergence Analysis and Enhanced Design Kaiyi Ji Junjie Yang Yingbin Liang 156 256 0 15 Oct 2020
On the Iteration Complexity of Hypergradient Computation Riccardo Grazzi Luca Franceschi Massimiliano Pontil Saverio Salzo 93 195 0 29 Jun 2020
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding Bruno Lecouat Jean Ponce Julien Mairal AI4CE 29 6 0 26 Jun 2020
Super-efficiency of automatic differentiation for functions defined as a minimum Pierre Ablin Gabriel Peyré Thomas Moreau 44 42 0 10 Feb 2020
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise Maxim Kaledin Eric Moulines A. Naumov V. Tadic Hoi-To Wai 48 73 0 04 Feb 2020
Lower Bounds for Non-Convex Stochastic Optimization Yossi Arjevani Y. Carmon John C. Duchi Dylan J. Foster Nathan Srebro Blake E. Woodworth 67 349 0 05 Dec 2019
Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine Paul Vicol David Duvenaud DD 104 409 0 06 Nov 2019
Meta-Learning with Implicit Gradients Aravind Rajeswaran Chelsea Finn Sham Kakade Sergey Levine 91 848 0 10 Sep 2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization A. Kulunchakov Julien Mairal 28 27 0 07 May 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 56 45 0 25 Jan 2019
Truncated Back-propagation for Bilevel Optimization Amirreza Shaban Ching-An Cheng Nathan Hatch Byron Boots 88 265 0 25 Oct 2018
Bilevel Programming for Hyperparameter Optimization and Meta-Learning Luca Franceschi P. Frasconi Saverio Salzo Riccardo Grazzi Massimiliano Pontil 173 722 0 13 Jun 2018
Meta-learning with differentiable closed-form solvers Luca Bertinetto João F. Henriques Philip Torr Andrea Vedaldi ODL 78 924 0 21 May 2018
Reviving and Improving Recurrent Back-Propagation Renjie Liao Yuwen Xiong Ethan Fetaya Lisa Zhang Kijung Yoon Xaq Pitkow R. Urtasun R. Zemel BDL 63 118 0 16 Mar 2018
$Stochastic subgradient method converges at the rate $O(k^{-1/4})$ on weakly convex functions$ Stochastic subgradient method converges at the rate $O(k^{-1/4})$ on weakly convex functions Damek Davis Dmitriy Drusvyatskiy 53 101 0 08 Feb 2018
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice Hongzhou Lin Julien Mairal Zaïd Harchaoui 38 138 0 15 Dec 2017
Forward and Reverse Gradient-Based Hyperparameter Optimization Luca Franceschi Michele Donini P. Frasconi Massimiliano Pontil 207 414 0 06 Mar 2017
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization Stephen Gould Basura Fernando A. Cherian Peter Anderson Rodrigo Santa Cruz Edison Guo 49 223 0 19 Jul 2016
Hyperparameter optimization with approximate gradient Fabian Pedregosa 93 449 0 07 Feb 2016
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 141 2,775 0 20 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.2K 149,474 0 22 Dec 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 273 1,246 0 10 Sep 2013
Task-Driven Dictionary Learning Julien Mairal Francis R. Bach Jean Ponce 90 898 0 27 Sep 2010