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Journal Articles
- 2024
[j7]Tengyuan Liang:
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. J. Mach. Learn. Res. 25: 140:1-140:27 (2024)- [j6]YoonHaeng Hur
, Wenxuan Guo, Tengyuan Liang:
Reversible Gromov-Monge Sampler for Simulation-Based Inference. SIAM J. Math. Data Sci. 6(2): 283-310 (2024) - 2023
- [j5]Tengyuan Liang, Benjamin Recht:
Interpolating Classifiers Make Few Mistakes. J. Mach. Learn. Res. 24: 20:1-20:27 (2023) - 2021
- [j4]Tengyuan Liang:
How Well Generative Adversarial Networks Learn Distributions. J. Mach. Learn. Res. 22: 228:1-228:41 (2021) - 2020
- [j3]T. Tony Cai, Tengyuan Liang, Alexander Rakhlin:
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. J. Mach. Learn. Res. 21: 11:1-11:34 (2020) - 2017
- [j2]T. Tony Cai, Tengyuan Liang, Alexander Rakhlin:
On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Trans. Netw. Sci. Eng. 4(3): 165-176 (2017) - 2015
- [j1]T. Tony Cai, Tengyuan Liang, Harrison H. Zhou:
Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions. J. Multivar. Anal. 137: 161-172 (2015)
Conference and Workshop Papers
- 2022
- [c8]Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan:
Online Learning to Transport via the Minimal Selection Principle. COLT 2022: 4085-4109 - 2020
- [c7]Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai:
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. COLT 2020: 2683-2711 - 2019
- [c6]Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS 2019: 888-896 - [c5]Tengyuan Liang, James Stokes:
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. AISTATS 2019: 907-915 - 2018
- [c4]Belinda Tzen, Tengyuan Liang, Maxim Raginsky:
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. COLT 2018: 857-875 - 2017
- [c3]Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál:
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. ICML 2017: 1780-1788 - 2015
- [c2]Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. COLT 2015: 240-265 - [c1]Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. COLT 2015: 1260-1285
Informal and Other Publications
- 2024
- [i26]Kulunu Dharmakeerthi, YoonHaeng Hur, Tengyuan Liang:
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction. CoRR abs/2406.15904 (2024) - 2022
- [i25]Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Christopher Ryan:
Online Learning to Transport via the Minimal Selection Principle. CoRR abs/2202.04732 (2022) - [i24]Tengyuan Liang, Subhabrata Sen, Pragya Sur:
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. CoRR abs/2204.04476 (2022) - [i23]Tengyuan Liang:
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. CoRR abs/2212.02457 (2022) - 2021
- [i22]Tengyuan Liang, Benjamin Recht:
Interpolating Classifiers Make Few Mistakes. CoRR abs/2101.11815 (2021) - [i21]Tengyuan Liang:
Universal Prediction Band via Semi-Definite Programming. CoRR abs/2103.17203 (2021) - [i20]YoonHaeng Hur, Wenxuan Guo, Tengyuan Liang:
Reversible Gromov-Monge Sampler for Simulation-Based Inference. CoRR abs/2109.14090 (2021) - 2020
- [i19]Tengyuan Liang, Pragya Sur:
A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers. CoRR abs/2002.01586 (2020) - [i18]Tengyuan Liang, Hai Tran-Bach:
Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. CoRR abs/2004.04767 (2020) - [i17]Max H. Farrell, Tengyuan Liang, Sanjog Misra:
Deep Learning for Individual Heterogeneity. CoRR abs/2010.14694 (2020) - 2019
- [i16]Xialiang Dou, Tengyuan Liang:
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. CoRR abs/1901.07114 (2019) - [i15]Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai:
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. CoRR abs/1908.10292 (2019) - [i14]Tengyuan Liang:
On the Minimax Optimality of Estimating the Wasserstein Metric. CoRR abs/1908.10324 (2019) - 2018
- [i13]Tengyuan Liang, James Stokes:
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. CoRR abs/1802.06132 (2018) - [i12]Belinda Tzen, Tengyuan Liang, Maxim Raginsky:
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. CoRR abs/1802.06439 (2018) - [i11]Tengyuan Liang, Alexander Rakhlin:
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. CoRR abs/1808.00387 (2018) - [i10]Max H. Farrell, Tengyuan Liang, Sanjog Misra:
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. CoRR abs/1809.09953 (2018) - [i9]Tengyuan Liang:
On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. CoRR abs/1811.03179 (2018) - 2017
- [i8]Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál:
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. CoRR abs/1706.04690 (2017) - [i7]Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR abs/1711.01530 (2017) - [i6]Tengyuan Liang:
How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View. CoRR abs/1712.08244 (2017) - 2016
- [i5]T. Tony Cai, Tengyuan Liang, Alexander Rakhlin:
On Detection and Structural Reconstruction of Small-World Random Networks. CoRR abs/1604.06474 (2016) - 2015
- [i4]Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. CoRR abs/1501.07242 (2015) - [i3]Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. CoRR abs/1502.06134 (2015) - 2014
- [i2]Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
On Zeroth-Order Stochastic Convex Optimization via Random Walks. CoRR abs/1402.2667 (2014) - 2013
- [i1]T. Tony Cai, Tengyuan Liang, Harrison H. Zhou:
Law of Log Determinant of Sample Covariance Matrix and Optimal Estimation of Differential Entropy for High-Dimensional Gaussian Distributions. CoRR abs/1309.0482 (2013)
Coauthor Index
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