Papers
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.
Compositional Fairness Constraints for Graph Embeddings
Avishek Bose, William Hamilton
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Vatsal Sharan, Kai Sheng Tai, Peter Bailis et al.
Compressing Gradient Optimizers via Count-Sketches
Ryan Spring, Anastasios Kyrillidis, Vijai Mohan et al.
Concentration Inequalities for Conditional Value at Risk
Philip Thomas, Erik Learned-Miller
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
Muhammed Fatih Balın, Abubakar Abid, James Zou
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
Alp Yurtsever, Suvrit Sra, Volkan Cevher
Conditional Independence in Testing Bayesian Networks
Yujia Shen, Haiying Huang, Arthur Choi et al.
Conditioning by adaptive sampling for robust design
David Brookes, Hahnbeom Park, Jennifer Listgarten
Connectivity-Optimized Representation Learning via Persistent Homology
Christoph Hofer, Roland Kwitt, Marc Niethammer et al.
Context-Aware Zero-Shot Learning for Object Recognition
Eloi Zablocki, Patrick Bordes, Laure Soulier et al.