Papers
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
Bodhisattwa Prasad Majumder, Oana Camburu, Thomas Lukasiewicz et al.
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Matthias Weissenbacher, Samarth Sinha, Animesh Garg et al.
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Michael A. Hedderich, Jonas Fischer, Dietrich Klakow et al.
Label-Free Explainability for Unsupervised Models
Jonathan Crabbé, Mihaela van der Schaar
Label Ranking through Nonparametric Regression
Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki
Langevin Monte Carlo for Contextual Bandits
Pan Xu, Hongkai Zheng, Eric V Mazumdar et al.
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak et al.
Large Batch Experience Replay
Thibault Lahire, Matthieu Geist, Emmanuel Rachelson
Large-Scale Graph Neural Architecture Search
Chaoyu Guan, Xin Wang, Hong Chen et al.
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong et al.
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
Jingfeng Wu, Difan Zou, Vladimir Braverman et al.
Latent Diffusion Energy-Based Model for Interpretable Text Modelling
Peiyu Yu, Sirui Xie, Xiaojian Ma et al.
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Chen Qiu, Aodong Li, Marius Kloft et al.
Lazy Estimation of Variable Importance for Large Neural Networks
Yue Gao, Abby Stevens, Garvesh Raskutti et al.
LCANets: Lateral Competition Improves Robustness Against Corruption and Attack
Michael Teti, Garrett Kenyon, Ben Migliori et al.
Learning Augmented Binary Search Trees
Honghao Lin, Tian Luo, David Woodruff
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
Jan Kaiser, Oliver Stein, Annika Eichler
Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan Chang, Kaiwen Wang, Nathan Kallus et al.
Learning Domain Adaptive Object Detection with Probabilistic Teacher
Meilin Chen, Weijie Chen, Shicai Yang et al.
Learning Dynamics and Generalization in Deep Reinforcement Learning
Clare Lyle, Mark Rowland, Will Dabney et al.
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
Weiming Zhi, Tin Lai, Lionel Ott et al.
Learning fair representation with a parametric integral probability metric
Dongha Kim, Kunwoong Kim, Insung Kong et al.
Learning from a Learning User for Optimal Recommendations
Fan Yao, Chuanhao Li, Denis Nekipelov et al.
Learning from Counterfactual Links for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang et al.