Papers
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic et al.
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
Marten Van Dijk, Lam Nguyen, Phuong Ha Nguyen et al.
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
Mario Lezcano-Casado, David Martı́nez-Rubio
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Tom Kenter, Vincent Wan, Chun-An Chan et al.
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
Guo Zhang, Hao He, Dina Katabi
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
Cognitive model priors for predicting human decisions
David D. Bourgin, Joshua C. Peterson, Daniel Reichman et al.
Collaborative Channel Pruning for Deep Networks
Hanyu Peng, Jiaxiang Wu, Shifeng Chen et al.
Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar et al.
Collective Model Fusion for Multiple Black-Box Experts
Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low et al.
Co-manifold learning with missing data
Gal Mishne, Eric Chi, Ronald Coifman
Combating Label Noise in Deep Learning using Abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes et al.
Combining parametric and nonparametric models for off-policy evaluation
Omer Gottesman, Yao Liu, Scott Sussex et al.
COMIC: Multi-view Clustering Without Parameter Selection
Xi Peng, Zhenyu Huang, Jiancheng Lv et al.
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
Jayadev Acharya, Ziteng Sun
Communication-Constrained Inference and the Role of Shared Randomness
Jayadev Acharya, Clement Canonne, Himanshu Tyagi
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games
Adrian Rivera Cardoso, Jacob Abernethy, He Wang et al.
CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai et al.
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Menon et al.
Complexity of Linear Regions in Deep Networks
Boris Hanin, David Rolnick
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan et al.
Composing Entropic Policies using Divergence Correction
Jonathan Hunt, Andre Barreto, Timothy Lillicrap et al.
Composing Value Functions in Reinforcement Learning
Benjamin Van Niekerk, Steven James, Adam Earle et al.