| 2019 | Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators. | Oren Mangoubi, Aaron Smith |
| 2019 | Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. | Anton Mallasto, Sren Hauberg, Aasa Feragen |
| 2019 | Learning the Structure of a Nonstationary Vector Autoregression. | Daniel Malinsky, Peter Spirtes |
| 2019 | A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. | Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson |
| 2019 | Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. | Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright |
| 2019 | Learning Invariant Representations with Kernel Warping. | Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang |
| 2019 | Representation Learning on Graphs: A Reinforcement Learning Application. | Sephora Madjiheurem, Laura Toni |
| 2019 | Adversarial Variational Optimization of Non-Differentiable Simulators. | Gilles Louppe, Joeri Hermans, Kyle Cranmer |
| 2019 | Active multiple matrix completion with adaptive confidence sets. | Andrea Locatelli, Alexandra Carpentier, Michal Valko |
| 2019 | Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators. | Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao |
| 2019 | Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning. | Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Junzhou Huang, Dimitris N. Metaxas |
| 2019 | Generalized Boltzmann Machine with Deep Neural Structure. | Yingru Liu, Dongliang Xie, Xin Wang |
| 2019 | Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. | Linfeng Liu, Liping Liu |
| 2019 | On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes. | Xiaoyu Li, Francesco Orabona |
| 2019 | Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. | Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba E. Ba |
| 2019 | Towards a Theoretical Understanding of Hashing-Based Neural Nets. | Yibo Lin, Zhao Song, Lin F. Yang |
| 2019 | On Target Shift in Adversarial Domain Adaptation. | Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson |
| 2019 | Nonconvex Matrix Factorization from Rank-One Measurements. | Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi |
| 2019 | Implicit Kernel Learning. | Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabs Pczos |
| 2019 | Bandit Online Learning with Unknown Delays. | Bingcong Li, Tianyi Chen, Georgios B. Giannakis |
| 2019 | On Connecting Stochastic Gradient MCMC and Differential Privacy. | Bai Li, Changyou Chen, Hao Liu, Lawrence Carin |
| 2019 | Adversarial Learning of a Sampler Based on an Unnormalized Distribution. | Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin |
| 2019 | Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization. | Xiangru Lian, Ji Liu |
| 2019 | Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. | Tengyuan Liang, James Stokes |
| 2019 | Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. | Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes |