| 2020 | Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. | David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola |
| 2020 | A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. | Foivos Alimisis, Antonio Orvieto, Gary Bcigneul, Aurlien Lucchi |
| 2020 | Fair Correlation Clustering. | Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian |
| 2020 | Causal Bayesian Optimization. | Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier Gonzlez |
| 2020 | Expressiveness and Learning of Hidden Quantum Markov Models. | Sandesh Adhikary, Siddarth Srinivasan, Geoffrey J. Gordon, Byron Boots |
| 2020 | On the Sample Complexity of Learning Sum-Product Networks. | Ishaq Aden-Ali, Hassan Ashtiani |
| 2020 | Doubly Sparse Variational Gaussian Processes. | Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman |
| 2020 | Budget Learning via Bracketing. | Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama |
| 2020 | Value Preserving State-Action Abstractions. | David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman |
| 2020 | Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters. | Yuhao Wang, Uma Roy, Caroline Uhler |
| 2020 | Minimizing Dynamic Regret and Adaptive Regret Simultaneously. | Lijun Zhang, Shiyin Lu, Tianbao Yang |
| 2020 | Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks. | Jinming Xu, Ye Tian, Ying Sun, Gesualdo Scutari |
| 2020 | Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. | Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh |
| 2020 | Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. | Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth |
| 2020 | Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons. | Jingyan Wang, Nihar B. Shah, R. Ravi |
| 2020 | Deep Structured Mixtures of Gaussian Processes. | Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen |
| 2020 | Tighter Theory for Local SGD on Identical and Heterogeneous Data. | Ahmed Khaled, Konstantin Mishchenko, Peter Richtrik |
| 2020 | On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. | Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar |
| 2020 | Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. | Hussein Hazimeh, Rahul Mazumder |
| 2020 | Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. | Alexander Levine, Soheil Feizi |
| 2020 | Neighborhood Growth Determines Geometric Priors for Relational Representation Learning. | Melanie Weber |
| 2019 | Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. | Difan Zou, Pan Xu, Quanquan Gu |
| 2019 | An Optimal Algorithm for Stochastic and Adversarial Bandits. | Julian Zimmert, Yevgeny Seldin |
| 2019 | High Dimensional Inference in Partially Linear Models. | Ying Zhu, Zhuqing Yu, Guang Cheng |
| 2019 | Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit. | Shengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen |