| 2022 | COLT | Online Learning to Transport via the Minimal Selection Principle. | Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan |
| 2020 | COLT | On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. | Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai |
| 2019 | AISTATS | Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. | Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes |
| 2019 | AISTATS | Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. | Tengyuan Liang, James Stokes |
| 2018 | COLT | Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. | Belinda Tzen, Tengyuan Liang, Maxim Raginsky |
| 2017 | ICML | Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. | Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dvid Pl |
| 2015 | COLT | Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. | Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin |
| 2015 | COLT | Learning with Square Loss: Localization through Offset Rademacher Complexity. | Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan |