Papers
A Solver-free Framework for Scalable Learning in Neural ILP Architectures
Yatin Nandwani, Rishabh Ranjan, - Mausam et al.
A Spectral Approach to Item Response Theory
Duc Nguyen, Anderson Ye Zhang
ASPiRe: Adaptive Skill Priors for Reinforcement Learning
Mengda Xu, Manuela M. Veloso, Shuran Song
Assaying Out-Of-Distribution Generalization in Transfer Learning
Florian Wenzel, Andrea Dittadi, Peter V. Gehler et al.
Assistive Teaching of Motor Control Tasks to Humans
Megha Srivastava, Erdem Biyik, Suvir Mirchandani et al.
Associating Objects and Their Effects in Video through Coordination Games
Erika Lu, Forrester Cole, Weidi Xie et al.
Association Graph Learning for Multi-Task Classification with Category Shifts
Jiayi Shen, Zehao Xiao, Xiantong Zhen et al.
A Statistical Online Inference Approach in Averaged Stochastic Approximation
Chuhan Xie, Zhihua Zhang
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization
Songtao Lu, Siliang Zeng, Xiaodong Cui et al.
A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets
Miri Zilka, Bradley Butcher, Adrian Weller
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Xin-Chun Li, Wen-shu Fan, Shaoming Song et al.
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
HuiYang Shao, Qianqian Xu, Zhiyong Yang et al.
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective
Jiadong Liang, Yuze Han, Xiang Li et al.
Asymptotic Properties for Bayesian Neural Network in Besov Space
Kyeongwon Lee, Jaeyong Lee
Asymptotics of $\ell_2$ Regularized Network Embeddings
Andrew Davison
Asymptotics of smoothed Wasserstein distances in the small noise regime
Yunzi Ding, Jonathan Niles-Weed
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Yuchen Xiao, Weihao Tan, Christopher Amato
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
Konstantin Mishchenko, Francis R. Bach, Mathieu Even et al.
ATD: Augmenting CP Tensor Decomposition by Self Supervision
Chaoqi Yang, Cheng Qian, Navjot Singh et al.
A Theoretical Framework for Inference Learning
Nick Alonso, Beren Millidge, Jeffrey Krichmar et al.
A Theoretical Study on Solving Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi et al.
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Bo Liu, Xidong Feng, Jie Ren et al.
A Theoretical View on Sparsely Activated Networks
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy et al.
A Theory of PAC Learnability under Transformation Invariances
Han Shao, Omar Montasser, Avrim Blum
A theory of weight distribution-constrained learning
Weishun Zhong, Ben Sorscher, Daniel D. Lee et al.