Unbiased Gradient Estimation in Unrolled Computation Graphs with
Persistent Evolution Strategies

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

27 December 2021

Paul Vicol

Jascha Narain Sohl-Dickstein

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

19 / 19 papers shown

Title
Learning Versatile Optimizers on a Compute Diet A. Moudgil Boris Knyazev Guillaume Lajoie Eugene Belilovsky 135 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 40 3 0 09 Jul 2024
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 29 2 0 27 Sep 2023
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li James Harrison Jascha Narain Sohl-Dickstein Virginia Smith Luke Metz 44 5 0 21 Apr 2023
Improving physics-informed neural networks with meta-learned optimization Alexander Bihlo PINN 33 18 0 13 Mar 2023
Dataset Distillation with Convexified Implicit Gradients Noel Loo Ramin Hasani Mathias Lechner Daniela Rus DD 29 41 0 13 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 33 9 0 02 Feb 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 43 73 0 11 Jan 2023
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 26 60 0 17 Nov 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 44 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
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
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 104 716 0 13 Jun 2018
Forward and Reverse Gradient-Based Hyperparameter Optimization Luca Franceschi Michele Donini P. Frasconi Massimiliano Pontil 127 405 0 06 Mar 2017
Variational Optimization J. Staines David Barber DRL 65 53 0 18 Dec 2012