Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

27 December 2021

Paul Vicol

Papers citing "Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies"

46 / 46 papers shown

Title
Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization Hongshu Guo Wenjie Qiu Zeyuan Ma Xuzhi Zhang Jun Zhang Jiawei Liu 49 1 0 24 Apr 2025
Make Optimization Once and for All with Fine-grained Guidance Mingjia Shi Ruihan Lin Xuxi Chen Yuhao Zhou Zezhen Ding ... Tong Wang Kai Wang Zhangyang Wang Jingyang Zhang Tianlong Chen 55 1 0 14 Mar 2025
Variational Bayesian Pseudo-Coreset Hyungi Lee S. Lee Juho Lee BDL 38 0 0 28 Feb 2025
Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation Tatsuya Aoyama Hanting Yang Hiroyuki Hanada Satoshi Akahane Tomonari Tanaka ... Noriaki Hashimoto Taro Murayama Hanju Lee Shinya Kojima Ichiro Takeuchi 72 0 0 18 Feb 2025
Learning Versatile Optimizers on a Compute Diet A. Moudgil Boris Knyazev Guillaume Lajoie Eugene Belilovsky 147 0 0 22 Jan 2025
Can Learned Optimization Make Reinforcement Learning Less Difficult? Alexander David Goldie Chris Xiaoxuan Lu Matthew Jackson Shimon Whiteson Jakob N. Foerster 42 3 0 09 Jul 2024
$μ$ LO: Compute-Efficient Meta-Generalization of Learned Optimizers Benjamin Thérien Charles-Étienne Joseph Boris Knyazev Edouard Oyallon Irina Rish Eugene Belilovsky AI4CE 38 1 0 31 May 2024
Universal Neural Functionals Allan Zhou Chelsea Finn James Harrison 32 12 0 07 Feb 2024
Moco: A Learnable Meta Optimizer for Combinatorial Optimization Tim Dernedde Daniela Thyssens Soren Dittrich Maximilan Stubbemann Lars Schmidt-Thieme 59 5 0 07 Feb 2024
Can We Learn Communication-Efficient Optimizers? Charles-Étienne Joseph Benjamin Thérien A. Moudgil Boris Knyazev Eugene Belilovsky 40 1 0 02 Dec 2023
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies Guangchen Lan FedML 29 0 0 05 Nov 2023
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms Shenao Zhang Boyi Liu Zhaoran Wang Tuo Zhao 24 2 0 30 Oct 2023
Fast Graph Condensation with Structure-based Neural Tangent Kernel Lin Wang Wenqi Fan Jiatong Li Yao Ma Qing Li DD 31 27 0 17 Oct 2023
Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering Yasasa Abeysirigoonawardena Kevin Xie Chuhan Chen Salar Hosseini Ruiting Chen Ruiqi Wang Florian Shkurti 36 2 0 27 Sep 2023
B2Opt: Learning to Optimize Black-box Optimization with Little Budget Xiaobin Li K. Wu Xiaoyu Zhang Handing Wang Jiaheng Liu 22 9 0 24 Apr 2023
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single Paul Vicol Zico Kolter Kevin Swersky 13 6 0 21 Apr 2023
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li James Harrison Jascha Narain Sohl-Dickstein Virginia Smith Luke Metz 46 5 0 21 Apr 2023
Improving physics-informed neural networks with meta-learned optimization Alexander Bihlo PINN 36 18 0 13 Mar 2023
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation Junjie Yang Xuxi Chen Tianlong Chen Zhangyang Wang Yitao Liang 18 2 0 28 Feb 2023
Denoising Diffusion Samplers Francisco Vargas Will Grathwohl Arnaud Doucet DiffM 24 75 0 27 Feb 2023
Dataset Distillation with Convexified Implicit Gradients Noel Loo Ramin Hasani Mathias Lechner Daniela Rus DD 31 41 0 13 Feb 2023
Learning to Optimize for Reinforcement Learning Qingfeng Lan Rupam Mahmood Shuicheng Yan Zhongwen Xu OffRL 26 6 0 03 Feb 2023
Mnemosyne: Learning to Train Transformers with Transformers Deepali Jain K. Choromanski Kumar Avinava Dubey Sumeet Singh Vikas Sindhwani Tingnan Zhang Jie Tan OffRL 39 9 0 02 Feb 2023
Probabilistic Bilevel Coreset Selection Xiao Zhou Renjie Pi Weizhong Zhang Yong Lin Tong Zhang NoLa 28 27 0 24 Jan 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 34 1 0 18 Jan 2023
Data Distillation: A Survey Noveen Sachdeva Julian McAuley DD 45 73 0 11 Jan 2023
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization Qiqi Duan Guochen Zhou Chang Shao Zhuowei Wang Mingyang Feng Yuwei Huang Yajing Tan Yijun Yang Qi Zhao Yuhui Shi 26 5 0 12 Dec 2022
evosax: JAX-based Evolution Strategies R. T. Lange 30 54 0 08 Dec 2022
Transformer-Based Learned Optimization Erik Gartner Luke Metz Mykhaylo Andriluka C. Freeman C. Sminchisescu 18 11 0 02 Dec 2022
Discovering Evolution Strategies via Meta-Black-Box Optimization R. T. Lange Tom Schaul Yutian Chen Tom Zahavy Valenti Dallibard Chris Xiaoxuan Lu Satinder Singh Sebastian Flennerhag 44 47 0 21 Nov 2022
VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz James Harrison C. Freeman Amil Merchant Lucas Beyer ... Naman Agrawal Ben Poole Igor Mordatch Adam Roberts Jascha Narain Sohl-Dickstein 29 60 0 17 Nov 2022
Efficient Non-Parametric Optimizer Search for Diverse Tasks Ruochen Wang Yuanhao Xiong Minhao Cheng Cho-Jui Hsieh 27 5 0 27 Sep 2022
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients Risto Vuorio Jacob Beck Shimon Whiteson Jakob N. Foerster Gregory Farquhar 17 8 0 22 Sep 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 47 22 0 22 Sep 2022
Theseus: A Library for Differentiable Nonlinear Optimization Luis Pineda Taosha Fan Maurizio Monge S. Venkataraman Paloma Sodhi ... Austin S. Wang Stuart Anderson Jing Dong Brandon Amos Mustafa Mukadam 24 76 0 19 Jul 2022
Dataset Distillation using Neural Feature Regression Yongchao Zhou E. Nezhadarya Jimmy Ba DD FedML 39 149 0 01 Jun 2022
Metrizing Fairness Yves Rychener Bahar Taşkesen Daniel Kuhn FaML 36 4 0 30 May 2022
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning Jiahui Gao Renjie Pi Yong Lin Hang Xu Jiacheng Ye Zhiyong Wu Weizhong Zhang Xiaodan Liang Zhenguo Li Lingpeng Kong SyDa VLM 67 45 0 25 May 2022
Hyper-Learning for Gradient-Based Batch Size Adaptation Calum MacLellan Feng Dong 6 0 0 17 May 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 33 32 0 22 Mar 2022
Neural Simulated Annealing Alvaro H. C. Correia Daniel E. Worrall Roberto Bondesan 11 7 0 04 Mar 2022
Amortized Proximal Optimization Juhan Bae Paul Vicol Jeff Z. HaoChen Roger C. Grosse ODL 25 14 0 28 Feb 2022
Tutorial on amortized optimization Brandon Amos OffRL 75 43 0 01 Feb 2022
Bilevel Programming for Hyperparameter Optimization and Meta-Learning Luca Franceschi P. Frasconi Saverio Salzo Riccardo Grazzi Massimiliano Pontil 110 716 0 13 Jun 2018
Forward and Reverse Gradient-Based Hyperparameter Optimization Luca Franceschi Michele Donini P. Frasconi Massimiliano Pontil 127 406 0 06 Mar 2017
Variational Optimization J. Staines David Barber DRL 65 53 0 18 Dec 2012