Papers
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
Casey Chu, Jose Blanchet, Peter Glynn
Unifying Orthogonal Monte Carlo Methods
Krzysztof Choromanski, Mark Rowland, Wenyu Chen et al.
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel et al.
Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover et al.
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
Uthsav Chitra, Benjamin Raphael
Variational Inference for sparse network reconstruction from count data
Julien Chiquet, Stephane Robin, Mahendra Mariadassou
Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff et al.
Control Regularization for Reduced Variance Reinforcement Learning
Richard Cheng, Abhinav Verma, Gabor Orosz et al.
RaFM: Rank-Aware Factorization Machines
Xiaoshuang Chen, Yin Zheng, Jiaxing Wang et al.
Robust Decision Trees Against Adversarial Examples
Hongge Chen, Huan Zhang, Duane Boning et al.
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang et al.
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen, Yi Xu, Haoyuan Hu et al.
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Pin-Yu Chen, Lingfei Wu, Sijia Liu et al.
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long et al.
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang et al.
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen et al.
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li et al.
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang
Proportionally Fair Clustering
Xingyu Chen, Brandon Fain, Liang Lyu et al.
Particle Flow Bayes’ Rule
Xinshi Chen, Hanjun Dai, Le Song
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, Francois-Xavier Briol et al.
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar et al.
Online learning with kernel losses
Niladri Chatterji, Aldo Pacchiano, Peter Bartlett