Papers
Bayesian Multi-type Mean Field Multi-agent Imitation Learning
Fan Yang, Alina Vereshchaka, Changyou Chen et al.
Inverse Reinforcement Learning from a Gradient-based Learner
Giorgia Ramponi, Gianluca Drappo, Marcello Restelli
Information Maximization for Few-Shot Learning
Malik Boudiaf, Imtiaz Ziko, Jérôme Rony et al.
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang, Kangchen Lv, Rui Huang et al.
Consistent feature selection for analytic deep neural networks
Vu C Dinh, Lam S Ho
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
Jianfeng Zhang, Xuecheng Nie, Jiashi Feng
Approximation Based Variance Reduction for Reparameterization Gradients
Tomas Geffner, Justin Domke
Practical Quasi-Newton Methods for Training Deep Neural Networks
Donald Goldfarb, Yi Ren, Achraf Bahamou
On Power Laws in Deep Ensembles
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan et al.
Falcon: Fast Spectral Inference on Encrypted Data
Qian Lou, Wen-jie Lu, Cheng Hong et al.
Ensemble Distillation for Robust Model Fusion in Federated Learning
Tao Lin, Lingjing Kong, Sebastian U Stich et al.
Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi Meronen, Christabella Irwanto, Arno Solin
Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza et al.
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
Dinghuai Zhang, Mao Ye, Chengyue Gong et al.
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth et al.
Rescuing neural spike train models from bad MLE
Diego Arribas, Yuan Zhao, ll Memming Park
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Hyun-Suk Lee, Yao Zhang, William Zame et al.
Forethought and Hindsight in Credit Assignment
Veronica Chelu, Doina Precup, Hado P van Hasselt
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Lénaïc Chizat, Pierre Roussillon, Flavien Léger et al.
Gradient Regularized V-Learning for Dynamic Treatment Regimes
Yao Zhang, Mihaela van der Schaar
Pointer Graph Networks
Petar Veličković, Lars Buesing, Matthew Overlan et al.
Rethinking pooling in graph neural networks
Diego Mesquita, Amauri Souza, Samuel Kaski
Cross-lingual Retrieval for Iterative Self-Supervised Training
Chau Tran, Yuqing Tang, Xian Li et al.
Towards Problem-dependent Optimal Learning Rates
Yunbei Xu, Assaf J. Zeevi
Self-Distillation as Instance-Specific Label Smoothing
Zhilu Zhang, Mert Sabuncu