| 2019 | Cost aware Inference for IoT Devices. | Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama |
| 2019 | A Robust Zero-Sum Game Framework for Pool-based Active Learning. | Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang |
| 2019 | Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds. | Pan Zhou, Xiao-Tong Yuan, Jiashi Feng |
| 2019 | LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. | Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood |
| 2019 | Direct Acceleration of SAGA using Sampled Negative Momentum. | Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo |
| 2019 | Scalable High-Order Gaussian Process Regression. | Shandian Zhe, Wei W. Xing, Robert M. Kirby |
| 2019 | An Optimal Algorithm for Stochastic Three-Composite Optimization. | Renbo Zhao, William B. Haskell, Vincent Y. F. Tan |
| 2019 | Learning One-hidden-layer ReLU Networks via Gradient Descent. | Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu |
| 2019 | Scalable Thompson Sampling via Optimal Transport. | Ruiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang, Tong Yu, Lawrence Carin |
| 2019 | Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex. | Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov |
| 2019 | Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. | Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon |
| 2019 | Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation. | Jian Zhang, Avner May, Tri Dao, Christopher R |
| 2019 | Defending against Whitebox Adversarial Attacks via Randomized Discretization. | Yuchen Zhang, Percy Liang |
| 2019 | Online Multiclass Boosting with Bandit Feedback. | Daniel T. Zhang, Young Hun Jung, Ambuj Tewari |
| 2019 | Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks. | Yue Yu, Jiaxiang Wu, Junzhou Huang |
| 2019 | Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy. | Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Amir Salman Avestimehr |
| 2019 | AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI. | Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu |
| 2019 | Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables. | Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2019 | Learning Influence-Receptivity Network Structure with Guarantee. | Ming Yu, Varun Gupta, Mladen Kolar |
| 2019 | A Stein-Papangelou Goodness-of-Fit Test for Point Processes. | Jiasen Yang, Vinayak A. Rao, Jennifer Neville |
| 2019 | Multi-Order Information for Working Set Selection of Sequential Minimal Optimization. | Qimao Yang, Changrong Li, Jun Guo |
| 2019 | Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design. | Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue |
| 2019 | Lovasz Convolutional Networks. | Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Pratim Talukdar |
| 2019 | Variance reduction properties of the reparameterization trick. | Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson |
| 2019 | Decentralized Gradient Tracking for Continuous DR-Submodular Maximization. | Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian |