Summary of Foundations and Frontiers Of Graph Learning Theory, by Yu Huang et al.
Foundations and Frontiers of Graph Learning Theoryby Yu Huang, Min Zhou, Menglin Yang, Zhen Wang,…
Foundations and Frontiers of Graph Learning Theoryby Yu Huang, Min Zhou, Menglin Yang, Zhen Wang,…
Stereo Risk: A Continuous Modeling Approach to Stereo Matchingby Ce Liu, Suryansh Kumar, Shuhang Gu,…
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processesby Asaf Cassel, Aviv RosenbergFirst…
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Stepsby Junchi Yang, Murat Yildirim, Qiu FengFirst…
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation Systemby Yang Zhao,…
Gradient descent with generalized Newton’s methodby Zhiqi Bu, Shiyun XuFirst submitted to arxiv on: 3…
PWM: Policy Learning with Multi-Task World Modelsby Ignat Georgiev, Varun Giridhar, Nicklas Hansen, Animesh GargFirst…
How to Boost Any Loss Functionby Richard Nock, Yishay MansourFirst submitted to arxiv on: 2…
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Databy Andrej Tschalzev, Sascha Marton,…
Automated Knowledge Graph Learning in Industrial Processesby Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr, Georgios C.…