Papers
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin et al.
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Hongteng Xu, Dixin Luo, Hongyuan Zha et al.
Power k-Means Clustering
Jason Xu, Kenneth Lange
Calibrated Approximate Bayesian Inference
Hanwen Xing, Geoff Nicholls, Jeong Lee
Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Guangcan Liu et al.
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Cong Xie, Sanmi Koyejo, Indranil Gupta
On Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie, Minshuo Chen, Haoming Jiang et al.
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu, Ezra Winston, Divyansh Kaushik et al.
Simplifying Graph Convolutional Networks
Felix Wu, Amauri Souza, Tianyi Zhang et al.
Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alex Dimakis, Sujay Sanghavi et al.
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao et al.
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank Schmidt, Zico Kolter
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini et al.
Fairness risk measures
Robert Williamson, Aditya Menon
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
William Wilkinson, Michael Andersen, Joshua D. Reiss et al.
Moment-Based Variational Inference for Markov Jump Processes
Christian Wildner, Heinz Koeppl
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Jacob Whitehill, Anand Ramakrishnan
Improving Model Selection by Employing the Test Data
Max Westphal, Werner Brannath
Learning deep kernels for exponential family densities
Li Wenliang, Danica J. Sutherland, Heiko Strathmann et al.
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Lily Weng, Pin-Yu Chen, Lam Nguyen et al.
Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé Iii et al.
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Gellert Weisz, Andras Gyorgy, Csaba Szepesvari
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
Xiaohan Wei, Zhuoran Yang, Zhaoran Wang