On Zeroth-Order Stochastic Convex Optimization via Random Walks. - Tengyuan Liang; Hariharan Narayanan; Alexander Rakhlin (CoRR, 2014)

Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. - Alexandre Belloni; Tengyuan Liang; Hariharan Narayanan; Alexander Rakhlin (COLT, 2015)

Learning with Square Loss: Localization through Offset Rademacher Complexity. - Tengyuan Liang; Alexander Rakhlin; Karthik Sridharan (COLT, 2015)

Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions. - T. Tony Cai; Tengyuan Liang; Harrison H. Zhou (J. Multivar. Anal., 2015)

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. - Satyen Kale; Zohar S. Karnin; Tengyuan Liang; Dávid Pál (ICML, 2017)

How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View. - Tengyuan Liang (CoRR, 2017)

On Detection and Structural Reconstruction of Small-World Random Networks. - T. Tony Cai; Tengyuan Liang; Alexander Rakhlin (IEEE Trans. Netw. Sci. Eng., 2017)

Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. - Belinda Tzen; Tengyuan Liang; Maxim Raginsky (COLT, 2018)

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. - Tengyuan Liang; Alexander Rakhlin (CoRR, 2018)

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. - Max H. Farrell; Tengyuan Liang; Sanjog Misra (CoRR, 2018)

On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. - Tengyuan Liang (CoRR, 2018)

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. - Tengyuan Liang; Tomaso A. Poggio; Alexander Rakhlin; James Stokes (AISTATS, 2019)

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. - Tengyuan Liang; James Stokes (AISTATS, 2019)

Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. - Xialiang Dou; Tengyuan Liang (CoRR, 2019)

On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. - Tengyuan Liang; Alexander Rakhlin; Xiyu Zhai (CoRR, 2019)

On the Minimax Optimality of Estimating the Wasserstein Metric. - Tengyuan Liang (CoRR, 2019)

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. - Tengyuan Liang; Alexander Rakhlin; Xiyu Zhai (COLT, 2020)

A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers. - Tengyuan Liang; Pragya Sur (CoRR, 2020)

Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. - Tengyuan Liang; Hai Tran-Bach (CoRR, 2020)

Deep Learning for Individual Heterogeneity. - Max H. Farrell; Tengyuan Liang; Sanjog Misra (CoRR, 2020)

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. - T. Tony Cai; Tengyuan Liang; Alexander Rakhlin (J. Mach. Learn. Res., 2020)

Universal Prediction Band via Semi-Definite Programming. - Tengyuan Liang (CoRR, 2021)

How Well Generative Adversarial Networks Learn Distributions. - Tengyuan Liang (J. Mach. Learn. Res., 2021)

Online Learning to Transport via the Minimal Selection Principle. - Wenxuan Guo; YoonHaeng Hur; Tengyuan Liang; Chris Ryan (COLT, 2022)

Online Learning to Transport via the Minimal Selection Principle. - Wenxuan Guo; YoonHaeng Hur; Tengyuan Liang; Christopher Ryan (CoRR, 2022)

High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. - Tengyuan Liang; Subhabrata Sen; Pragya Sur (CoRR, 2022)

Interpolating Classifiers Make Few Mistakes. - Tengyuan Liang; Benjamin Recht (J. Mach. Learn. Res., 2023)

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction. - Kulunu Dharmakeerthi; YoonHaeng Hur; Tengyuan Liang (CoRR, 2024)

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity. - Tengyuan Liang; Kulunu Dharmakeerthi; Takuya Koriyama (CoRR, 2024)

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. - Tengyuan Liang (J. Mach. Learn. Res., 2024)

Reversible Gromov-Monge Sampler for Simulation-Based Inference. - YoonHaeng Hur; Wenxuan Guo; Tengyuan Liang (SIAM J. Math. Data Sci., 2024)

Gaussianized Design Optimization for Covariate Balance in Randomized Experiments. - Wenxuan Guo; Tengyuan Liang; Panos Toulis (EC, 2025)

No-Regret Generative Modeling via Parabolic Monge-Ampère PDE. - Nabarun Deb; Tengyuan Liang (CoRR, 2025)