Symbolic Learning to Optimize: Towards Interpretability and Scalability

Symbolic Learning to Optimize: Towards Interpretability and Scalability

13 March 2022

Papers citing "Symbolic Learning to Optimize: Towards Interpretability and Scalability"

16 / 16 papers shown

Title
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 J. Zhang Tianlong Chen 55 1 0 14 Mar 2025
Narrowing the Focus: Learned Optimizers for Pretrained Models Gus Kristiansen Mark Sandler A. Zhmoginov Nolan Miller Anirudh Goyal Jihwan Lee Max Vladymyrov 36 1 0 17 Aug 2024
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process Zhenan Fan Bissan Ghaddar Xinglu Wang Linzi Xing Yong Zhang Zirui Zhou AI4CE 47 11 0 06 Jan 2024
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation Wenqing Zheng S. Sharan Ajay Jaiswal Kevin Wang Yihan Xi Dejia Xu Zhangyang Wang 44 24 0 28 Apr 2023
MetaViewer: Towards A Unified Multi-View Representation Ren Wang Haoliang Sun Yuling Ma Xiaoming Xi Yilong Yin 46 9 0 11 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
Online Symbolic Regression with Informative Query Pengwei Jin Di Huang Rui Zhang Xingui Hu Ziyuan Nan Zidong Du Qi Guo Yunji Chen 16 2 0 21 Feb 2023
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search Wenqing Zheng S. Sharan Zhiwen Fan Kevin Wang Yihan Xi Zhangyang Wang 58 9 0 30 Dec 2022
Symbolic Distillation for Learned TCP Congestion Control S. Sharan Wenqing Zheng Kuo-Feng Hsu Jiarong Xing Ang Chen Zhangyang Wang 15 5 0 24 Oct 2022
Efficient Non-Parametric Optimizer Search for Diverse Tasks Ruochen Wang Yuanhao Xiong Minhao Cheng Cho-Jui Hsieh 27 5 0 27 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
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
Tutorial on amortized optimization Brandon Amos OffRL 75 43 0 01 Feb 2022
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 38 225 0 23 Mar 2021
L $^2$ -GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks Yuning You Tianlong Chen Zhangyang Wang Yang Shen GNN 101 82 0 30 Mar 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017