Papers
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong et al.
Multi-task Learning for Aggregated Data using Gaussian Processes
Fariba Yousefi, Michael T Smith, Mauricio Álvarez
Variational Denoising Network: Toward Blind Noise Modeling and Removal
Zongsheng Yue, Hongwei Yong, Qian Zhao et al.
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
Jiawang Bian, Zhichao Li, Naiyan Wang et al.
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Online Forecasting of Total-Variation-bounded Sequences
Dheeraj Baby, Yu-Xiang Wang
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang
Gradient-based Adaptive Markov Chain Monte Carlo
Michalis Titsias, Petros Dellaportas
Multivariate Triangular Quantile Maps for Novelty Detection
Jingjing Wang, Sun Sun, Yaoliang Yu
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Kaidi Cao, Colin Wei, Adrien Gaidon et al.
Solving Interpretable Kernel Dimensionality Reduction
Chieh Wu, Jared Miller, Yale Chang et al.
Sampling Sketches for Concave Sublinear Functions of Frequencies
Edith Cohen, Ofir Geri
The Thermodynamic Variational Objective
Vaden Masrani, Tuan Anh Le, Frank Wood
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks
Gabriele Farina, Chun Kai Ling, Fei Fang et al.
Pure Exploration with Multiple Correct Answers
Rémy Degenne, Wouter M. Koolen
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama et al.
Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, Song Han
k-Means Clustering of Lines for Big Data
Yair Marom, Dan Feldman
Variance Reduced Policy Evaluation with Smooth Function Approximation
Hoi-To Wai, Mingyi Hong, Zhuoran Yang et al.
Is Deeper Better only when Shallow is Good?
Eran Malach, Shai Shalev-Shwartz
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Joshua Lee, Prasanna Sattigeri, Gregory Wornell
Learning to Learn By Self-Critique
Antreas Antoniou, Amos J. Storkey
Blind Super-Resolution Kernel Estimation using an Internal-GAN
Sefi Bell-Kligler, Assaf Shocher, Michal Irani
On Making Stochastic Classifiers Deterministic
Andrew Cotter, Maya Gupta, Harikrishna Narasimhan