Gradient-based Hyperparameter Optimization through Reversible Learning

11 February 2015

Papers citing "Gradient-based Hyperparameter Optimization through Reversible Learning"

50 / 498 papers shown

Title
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li James Harrison Jascha Narain Sohl-Dickstein Virginia Smith Luke Metz 56 5 0 21 Apr 2023
Class-Incremental Exemplar Compression for Class-Incremental Learning Zilin Luo Yaoyao Liu Bernt Schiele Qianru Sun VLM CLL 96 44 0 24 Mar 2023
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models Dohwan Ko Joon-Young Choi Hyeong Kyu Choi Kyoung-Woon On Byungseok Roh Hyunwoo J. Kim 57 19 0 23 Mar 2023
Achieving Hierarchy-Free Approximation for Bilevel Programs With Equilibrium Constraints Jiayang Li Jiahao Yu Boyi Liu Zhaoran Wang Y. Nie 40 6 0 20 Feb 2023
Nystrom Method for Accurate and Scalable Implicit Differentiation Ryuichiro Hataya M. Yamada ODL 47 8 0 20 Feb 2023
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization Mathieu Dagréou Thomas Moreau Samuel Vaiter Pierre Ablin 39 12 0 17 Feb 2023
Revisiting Weighted Aggregation in Federated Learning with Neural Networks Zexi Li Tao R. Lin Xinyi Shang Chao-Xiang Wu FedML 50 61 0 14 Feb 2023
Dataset Distillation with Convexified Implicit Gradients Noel Loo Ramin Hasani Mathias Lechner Daniela Rus DD 36 42 0 13 Feb 2023
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems Junyi Li Feihu Huang Heng Huang FedML 32 12 0 13 Feb 2023
Achieving Linear Speedup in Non-IID Federated Bilevel Learning Minhui Huang Dewei Zhang Kaiyi Ji FedML 40 18 0 10 Feb 2023
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation Peiyao Xiao Kaiyi Ji FedML 32 11 0 09 Feb 2023
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity Risheng Liu Yaohua Liu Wei-Ting Yao Shangzhi Zeng Jin Zhang 32 24 0 07 Feb 2023
Scaling Laws for Hyperparameter Optimization Arlind Kadra Maciej Janowski Martin Wistuba Josif Grabocka 33 9 0 01 Feb 2023
Online Loss Function Learning Christian Raymond Qi Chen Bing Xue Mengjie Zhang 38 5 0 30 Jan 2023
Massively Scaling Heteroscedastic Classifiers Mark Collier Rodolphe Jenatton Basil Mustafa N. Houlsby Jesse Berent E. Kokiopoulou 28 8 0 30 Jan 2023
Probabilistic Bilevel Coreset Selection Xiao Zhou Renjie Pi Weizhong Zhang Yong Lin Tong Zhang NoLa 43 27 0 24 Jan 2023
Model Agnostic Sample Reweighting for Out-of-Distribution Learning Xiao Zhou Yong Lin Renjie Pi Weizhong Zhang Renzhe Xu Peng Cui Tong Zhang OODD 44 61 0 24 Jan 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 39 1 0 18 Jan 2023
Dataset Distillation: A Comprehensive Review Ruonan Yu Songhua Liu Xinchao Wang DD 60 121 0 17 Jan 2023
A Comprehensive Survey of Dataset Distillation Shiye Lei Dacheng Tao DD 54 88 0 13 Jan 2023
Analyzing Inexact Hypergradients for Bilevel Learning Matthias Joachim Ehrhardt Lindon Roberts 24 8 0 11 Jan 2023
Data Distillation: A Survey Noveen Sachdeva Julian McAuley DD 52 73 0 11 Jan 2023
Optimistic Meta-Gradients Sebastian Flennerhag Tom Zahavy Brendan O'Donoghue Hado van Hasselt András Gyorgy Satinder Singh 47 3 0 09 Jan 2023
Enhancement attacks in biomedical machine learning M. Rosenblatt J. Dadashkarimi D. Scheinost AAML 29 4 0 05 Jan 2023
First-order penalty methods for bilevel optimization Zhaosong Lu Sanyou Mei 66 31 0 04 Jan 2023
On Implicit Bias in Overparameterized Bilevel Optimization Paul Vicol Jon Lorraine Fabian Pedregosa David Duvenaud Roger C. Grosse AI4CE 40 37 0 28 Dec 2022
Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence Jian Guo Saizhuo Wang L. Ni H. Shum AIFin 26 8 0 13 Dec 2022
Accelerating Dataset Distillation via Model Augmentation Lei Zhang Jie M. Zhang Bowen Lei Subhabrata Mukherjee Xiang Pan Bo Zhao Caiwen Ding Heng Chang Dongkuan Xu DD 52 62 0 12 Dec 2022
Transformer-Based Learned Optimization Erik Gartner Luke Metz Mykhaylo Andriluka C. Freeman C. Sminchisescu 33 11 0 02 Dec 2022
Task Discovery: Finding the Tasks that Neural Networks Generalize on Andrei Atanov Andrei Filatov Teresa Yeo Ajay Sohmshetty Amir Zamir OOD 55 10 0 01 Dec 2022
Implicit Training of Energy Model for Structure Prediction Shiv Shankar Vihari Piratla 27 0 0 21 Nov 2022
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation Jiawei Du Yiding Jiang Vincent Y. F. Tan Qiufeng Wang Haizhou Li DD 46 112 0 20 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 40 60 0 17 Nov 2022
Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization Quan-Wu Xiao Han Shen W. Yin Tianyi Chen 23 8 0 14 Nov 2022
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming Parvin Nazari Ahmad Mousavi Davoud Ataee Tarzanagh George Michailidis 41 4 0 08 Nov 2022
Black-box Coreset Variational Inference Dionysis Manousakas H. Ritter Theofanis Karaletsos BDL 21 4 0 04 Nov 2022
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity Xuxing Chen Minhui Huang Shiqian Ma Krishnakumar Balasubramanian 27 25 0 23 Oct 2022
Efficient Dataset Distillation Using Random Feature Approximation Noel Loo Ramin Hasani Alexander Amini Daniela Rus DD 81 98 0 21 Oct 2022
A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions Yongsheng Mei Tian-Shing Lan Mahdi Imani Suresh Subramaniam GP 33 16 0 13 Oct 2022
Equivariance-aware Architectural Optimization of Neural Networks Kaitlin Maile Dennis G. Wilson Patrick Forré AI4CE 52 9 0 11 Oct 2022
Decentralized Hyper-Gradient Computation over Time-Varying Directed Networks Naoyuki Terashita Satoshi Hara FedML 26 1 0 05 Oct 2022
Learning to Learn with Generative Models of Neural Network Checkpoints William S. Peebles Ilija Radosavovic Tim Brooks Alexei A. Efros Jitendra Malik UQCV 75 65 0 26 Sep 2022
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels Matthieu Darcy B. Hamzi Giulia Livieri H. Owhadi P. Tavallali 41 27 0 24 Sep 2022
Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms Hesameddin Mohammadi Meisam Razaviyayn Mihailo R. Jovanović 39 7 0 24 Sep 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 49 22 0 22 Sep 2022
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning Christian Raymond Qi Chen Bing Xue Mengjie Zhang FedML 33 11 0 19 Sep 2022
Differentiable Bilevel Programming for Stackelberg Congestion Games Jiayang Li Jiahao Yu Qianni Wang Boyi Liu Zhaoran Wang Y. Nie 31 14 0 15 Sep 2022
Meta-Gradients in Non-Stationary Environments Jelena Luketina Sebastian Flennerhag Yannick Schroecker David Abel Tom Zahavy Satinder Singh 31 10 0 13 Sep 2022
Simple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models Jared Lichtarge Chris Alberti Shankar Kumar 50 4 0 10 Sep 2022
Task Selection for AutoML System Evaluation Jon Lorraine Nihesh Anderson Chansoo Lee Quentin de Laroussilhe Mehadi Hassen 57 4 0 26 Aug 2022