Papers
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei et al.
A Collective Learning Framework to Boost GNN Expressiveness for Node Classification
Mengyue Hang, Jennifer Neville, Bruno Ribeiro
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu et al.
Active Covering
Heinrich Jiang, Afshin Rostamizadeh
Active Deep Probabilistic Subsampling
Hans Van Gorp, Iris Huijben, Bastiaan S Veeling et al.
Active Feature Acquisition with Generative Surrogate Models
Yang Li, Junier Oliva
Active Learning for Distributionally Robust Level-Set Estimation
Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi
Active Learning of Continuous-time Bayesian Networks through Interventions
Dominik Linzner, Heinz Koeppl
Active Slices for Sliced Stein Discrepancy
Wenbo Gong, Kaibo Zhang, Yingzhen Li et al.
Active Testing: Sample-Efficient Model Evaluation
Jannik Kossen, Sebastian Farquhar, Yarin Gal et al.
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
Jianfei Chen, Lianmin Zheng, Zhewei Yao et al.
Adapting to Delays and Data in Adversarial Multi-Armed Bandits
Andras Gyorgy, Pooria Joulani
Adapting to misspecification in contextual bandits with offline regression oracles
Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
Jonathan Lacotte, Yifei Wang, Mert Pilanci
Adaptive Sampling for Best Policy Identification in Markov Decision Processes
Aymen Al Marjani, Alexandre Proutiere
AdaXpert: Adapting Neural Architecture for Growing Data
Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu et al.
Additive Error Guarantees for Weighted Low Rank Approximation
Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena
Addressing Catastrophic Forgetting in Few-Shot Problems
Pauching Yap, Hippolyt Ritter, David Barber
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Scott Fujimoto, David Meger, Doina Precup
A Discriminative Technique for Multiple-Source Adaptation
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh et al.
A Distribution-dependent Analysis of Meta Learning
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvari
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
Dmitry Kovalev, Egor Shulgin, Peter Richtarik et al.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
Yanjun Han, Yining Wang, Xi Chen
Adversarial Dueling Bandits
Aadirupa Saha, Tomer Koren, Yishay Mansour