Papers
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.
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation
Zhihan Liu, Yufeng Zhang, Zuyue Fu et al.
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent
Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos et al.
Learning Infinite-horizon Average-reward Markov Decision Process with Constraints
Liyu Chen, Rahul Jain, Haipeng Luo
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu et al.
Learning Iterative Reasoning through Energy Minimization
Yilun Du, Shuang Li, Joshua Tenenbaum et al.
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
Qinghua Liu, Yuanhao Wang, Chi Jin
Learning Mixtures of Linear Dynamical Systems
Yanxi Chen, H. Vincent Poor
Learning Multiscale Transformer Models for Sequence Generation
Bei Li, Tong Zheng, Yi Jing et al.
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
Henrique Aguiar, Mauro Santos, Peter Watkinson et al.
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
Pengjie Gu, Mengchen Zhao, Chen Chen et al.