Tengyuan Liang

48 papers A* 7A 2Journal 39

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity. Trans. Mach. Learn. Res. Tengyuan Liang, Kulunu Dharmakeerthi, Takuya Koriyama
2025	J	`jnl`	Distributional Shrinkage I: Universal Denoisers in Multi-Dimensions. CoRR Tengyuan Liang
2025	J	`jnl`	Distributional Shrinkage II: Optimal Transport Denoisers with Higher-Order Scores. CoRR Tengyuan Liang
2025	A*	`conf`	Gaussianized Design Optimization for Covariate Balance in Randomized Experiments. EC Wenxuan Guo, Tengyuan Liang, Panos Toulis
2025	J	`jnl`	No-Regret Generative Modeling via Parabolic Monge-Ampère PDE. CoRR Nabarun Deb, Tengyuan Liang
2025	J	`jnl`	Textual Factors: A Scalable, Interpretable, and Data-Driven Approach to Analyzing Unstructured Information. Manag. Sci. Lin William Cong, Tengyuan Liang, Xiao Zhang, Wu Zhu
2024	J	`jnl`	Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. J. Mach. Learn. Res. Tengyuan Liang
2024	J	`jnl`	Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity. CoRR Tengyuan Liang, Kulunu Dharmakeerthi, Takuya Koriyama
2024	J	`jnl`	Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction. CoRR Kulunu Dharmakeerthi, Yoonhaeng Hur, Tengyuan Liang
2024	J	`jnl`	Reversible Gromov-Monge Sampler for Simulation-Based Inference. SIAM J. Math. Data Sci. Yoonhaeng Hur, Wenxuan Guo, Tengyuan Liang
2023	J	`jnl`	Interpolating Classifiers Make Few Mistakes. J. Mach. Learn. Res. Tengyuan Liang, Benjamin Recht
2022	J	`jnl`	Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. CoRR Tengyuan Liang
2022	J	`jnl`	High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. CoRR Tengyuan Liang, Subhabrata Sen, Pragya Sur
2022	A*	`conf`	Online Learning to Transport via the Minimal Selection Principle. COLT Wenxuan Guo, Yoonhaeng Hur, Tengyuan Liang, Chris Ryan
2022	J	`jnl`	Online Learning to Transport via the Minimal Selection Principle. CoRR Wenxuan Guo, Yoonhaeng Hur, Tengyuan Liang, Christopher Ryan
2021	J	`jnl`	How Well Generative Adversarial Networks Learn Distributions. J. Mach. Learn. Res. Tengyuan Liang
2021	J	`jnl`	Interpolating Classifiers Make Few Mistakes. CoRR Tengyuan Liang, Benjamin Recht
2021	J	`jnl`	Reversible Gromov-Monge Sampler for Simulation-Based Inference. CoRR Yoonhaeng Hur, Wenxuan Guo, Tengyuan Liang
2021	J	`jnl`	Universal Prediction Band via Semi-Definite Programming. CoRR Tengyuan Liang
2020	J	`jnl`	A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers. CoRR Tengyuan Liang, Pragya Sur
2020	J	`jnl`	Deep Learning for Individual Heterogeneity. CoRR Max H. Farrell, Tengyuan Liang, Sanjog Misra
2020	J	`jnl`	Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. CoRR Tengyuan Liang, Hai Tran-Bach
2020	A*	`conf`	On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. COLT Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
2020	J	`jnl`	Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. J. Mach. Learn. Res. T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
2019	A	`conf`	Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
2019	A	`conf`	Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. AISTATS Tengyuan Liang, James Stokes
2019	J	`jnl`	On the Minimax Optimality of Estimating the Wasserstein Metric. CoRR Tengyuan Liang
2019	J	`jnl`	On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. CoRR Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
2019	J	`jnl`	Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. CoRR Xialiang Dou, Tengyuan Liang
2018	J	`jnl`	Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. CoRR Max H. Farrell, Tengyuan Liang, Sanjog Misra
2018	J	`jnl`	Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. CoRR Tengyuan Liang, James Stokes
2018	J	`jnl`	Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. CoRR Tengyuan Liang, Alexander Rakhlin
2018	A*	`conf`	Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. COLT Belinda Tzen, Tengyuan Liang, Maxim Raginsky
2018	J	`jnl`	Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. CoRR Belinda Tzen, Tengyuan Liang, Maxim Raginsky
2018	J	`jnl`	On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. CoRR Tengyuan Liang
2017	A*	`conf`	Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. ICML Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
2017	J	`jnl`	Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. CoRR Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
2017	J	`jnl`	Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
2017	J	`jnl`	How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View. CoRR Tengyuan Liang
2017	J	`jnl`	On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Trans. Netw. Sci. Eng. T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
2016	J	`jnl`	On Detection and Structural Reconstruction of Small-World Random Networks. CoRR T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
2015	A*	`conf`	Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. COLT Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
2015	J	`jnl`	Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. CoRR Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
2015	J	`jnl`	Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions. J. Multivar. Anal. T. Tony Cai, Tengyuan Liang, Harrison H. Zhou
2015	A*	`conf`	Learning with Square Loss: Localization through Offset Rademacher Complexity. COLT Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
2015	J	`jnl`	Learning with Square Loss: Localization through Offset Rademacher Complexity. CoRR Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
2014	J	`jnl`	On Zeroth-Order Stochastic Convex Optimization via Random Walks. CoRR Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
2013	J	`jnl`	Law of Log Determinant of Sample Covariance Matrix and Optimal Estimation of Differential Entropy for High-Dimensional Gaussian Distributions. CoRR T. Tony Cai, Tengyuan Liang, Harrison H. Zhou