Papers
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar et al.
Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen, Saurabh Kumar, Ramki Gummadi et al.
Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication
Sangwoo Hong, Heecheol Yang, Youngseok Yoon et al.
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
Hanshu Yan, Jingfeng Zhang, Gang Niu et al.
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
Songhua Wu, Xiaobo Xia, Tongliang Liu et al.
Classification with Rejection Based on Cost-sensitive Classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang et al.
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed
Maria Refinetti, Sebastian Goldt, Florent Krzakala et al.
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Dani Kiyasseh, Tingting Zhu, David A Clifton
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Zhaowei Zhu, Yiwen Song, Yang Liu
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
Yann Fraboni, Richard Vidal, Laetitia Kameni et al.
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition
Bo Liu, Qiang Liu, Peter Stone et al.
Coded-InvNet for Resilient Prediction Serving Systems
Tuan Dinh, Kangwook Lee
Collaborative Bayesian Optimization with Fair Regret
Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low et al.
Combinatorial Blocking Bandits with Stochastic Delays
Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu et al.
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
Sebastian Curi, Ilija Bogunovic, Andreas Krause
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Anselm Paulus, Michal Rolinek, Vit Musil et al.
Communication-Efficient Distributed Optimization with Quantized Preconditioners
Foivos Alimisis, Peter Davies, Dan Alistarh
Communication-Efficient Distributed SVD via Local Power Iterations
Xiang Li, Shusen Wang, Kun Chen et al.
Commutative Lie Group VAE for Disentanglement Learning
Xinqi Zhu, Chang Xu, Dacheng Tao
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
Sang Michael Xie, Tengyu Ma, Percy Liang
Composing Normalizing Flows for Inverse Problems
Jay Whang, Erik Lindgren, Alex Dimakis
Compositional Video Synthesis with Action Graphs
Amir Bar, Roei Herzig, Xiaolong Wang et al.
Compressed Maximum Likelihood
Yi Hao, Alon Orlitsky
Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability
Fabien Collas, Ekhine Irurozki