A Generalizable Approach to Learning Optimizers

A Generalizable Approach to Learning Optimizers

2 June 2021

Wojciech Zaremba

Papers citing "A Generalizable Approach to Learning Optimizers"

16 / 16 papers shown

Title
Reinforcement Teaching Alex Lewandowski Calarina Muslimani Dale Schuurmans Matthew E. Taylor Jun-Jie Luo 74 1 0 28 Jan 2025
Learning Versatile Optimizers on a Compute Diet A. Moudgil Boris Knyazev Guillaume Lajoie Eugene Belilovsky 123 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
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent Kaan Ozkara Can Karakus Parameswaran Raman Mingyi Hong Shoham Sabach B. Kveton V. Cevher 19 2 0 17 Jan 2024
Sentiment analysis in Tourism: Fine-tuning BERT or sentence embeddings concatenation? Ibrahim Bouabdallaoui Fatima Guerouate Samya Bouhaddour C. Saadi Mohammed Sbihi 14 0 0 12 Dec 2023
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation Li Ding M. Zoghi Guy Tennenholtz Maryam Karimzadehgan 16 0 0 29 Oct 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 21 60 0 17 Nov 2022
Learning to Learn with Generative Models of Neural Network Checkpoints William S. Peebles Ilija Radosavovic Tim Brooks Alexei A. Efros Jitendra Malik UQCV 73 64 0 26 Sep 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
Meta-Gradients in Non-Stationary Environments Jelena Luketina Sebastian Flennerhag Yannick Schroecker David Abel Tom Zahavy Satinder Singh 21 10 0 13 Sep 2022
Automated Dynamic Algorithm Configuration Steven Adriaensen André Biedenkapp Gresa Shala Noor H. Awad Theresa Eimer Marius Lindauer Frank Hutter 27 36 0 27 May 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 31 32 0 22 Mar 2022
Symbolic Learning to Optimize: Towards Interpretability and Scalability Wenqing Zheng Tianlong Chen Ting-Kuei Hu Zhangyang Wang 37 18 0 13 Mar 2022
ModelPred: A Framework for Predicting Trained Model from Training Data Yingyan Zeng Jiachen T. Wang Si-An Chen H. Just Ran Jin R. Jia TDI MU 20 2 0 24 Nov 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 30 224 0 23 Mar 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,453 0 23 Jan 2020