Nonintrusive and Structure Preserving Multiscale Integration of Stiff ODEs, SDEs, and Hamiltonian Systems with Hidden Slow Dynamics via Flow Averaging. - Molei Tao; Houman Owhadi; Jerrold E. Marsden (Multiscale Model. Simul., 2010)

Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids. - Shuo Han 011; Ufuk Topcu; Molei Tao; Houman Owhadi; Richard M. Murray (ACC, 2013)

Variational integrators for electric circuits. - Sina Ober-Blöbaum; Molei Tao; Mulin Cheng; Houman Owhadi; Jerrold E. Marsden (J. Comput. Phys., 2013)

Convex Optimal Uncertainty Quantification. - Shuo Han 011; Molei Tao; Ufuk Topcu; Houman Owhadi; Richard M. Murray (SIAM J. Optim., 2015)

Explicit high-order symplectic integrators for charged particles in general electromagnetic fields. - Molei Tao (J. Comput. Phys., 2016)

Parametric Resonant Control of Macroscopic Behaviors of Multiple Oscillators. - Pengcheng Xie; Molei Tao (ACC, 2019)

Simply improved averaging for coupled oscillators and weakly nonlinear waves. - Molei Tao (Commun. Nonlinear Sci. Numer. Simul., 2019)

Data-driven multiscale decompositions for forecasting and model discovery. - Daniel Dylewsky; Molei Tao; J. Nathan Kutz (CoRR, 2019)

Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems. - Molei Tao; Tomoki Ohsawa (AISTATS, 2020)

Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. - Kaixuan Huang; Yuqing Wang; Molei Tao; Tuo Zhao (NeurIPS, 2020)

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function. - Lingkai Kong; Molei Tao (NeurIPS, 2020)

Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients. - Ruilin Li; Xin Wang 016; Hongyuan Zha; Molei Tao (CoRR, 2020)

AhKin: A modular and efficient code for the Doppler shift attenuation method. - Alejandro Garzón; Wilmar Rodriguez; Fernando Cristancho; Molei Tao (Comput. Phys. Commun., 2020)

Variational Symplectic Accelerated Optimization on Lie Groups. - Taeyoung Lee; Molei Tao; Melvin Leok (CDC, 2021)

Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps. - Renyi Chen; Molei Tao (ICML, 2021)

A Derivative-Free Optimization Method With Application to Functions With Exploding and Vanishing Gradients. - Said Al-Abri; Tony X. Lin; Molei Tao; Fumin Zhang 001 (IEEE Control. Syst. Lett., 2021)

The Mirror Langevin Algorithm Converges with Vanishing Bias. - Ruilin Li; Molei Tao; Santosh S. Vempala; Andre Wibisono (ALT, 2022)

Sqrt(d) Dimension Dependence of Langevin Monte Carlo. - Ruilin Li; Hongyuan Zha; Molei Tao (ICLR, 2022)

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. - Yuqing Wang; Minshuo Chen; Tuo Zhao; Molei Tao (ICLR, 2022)

Hessian-Free High-Resolution Nesterov Acceleration For Sampling. - Ruilin Li; Hongyuan Zha; Molei Tao (ICML, 2022)

Alternating Mirror Descent for Constrained Min-Max Games. - Andre Wibisono; Molei Tao; Georgios Piliouras (NeurIPS, 2022)

Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian. - Oswin So; Gongjie Li; Evangelos A. Theodorou; Molei Tao (CoRR, 2022)

Accurate and efficient simulations of Hamiltonian mechanical systems with discontinuous potentials. - Molei Tao; Shi Jin 003 (J. Comput. Phys., 2022)

Parametric Resonance for Enhancing the Rate of Metastable Transition. - Ying Chao; Molei Tao (SIAM J. Appl. Math., 2022)

Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport. - Lingkai Kong; Yuqing Wang; Molei Tao (ICLR, 2023)

gDDIM: Generalized denoising diffusion implicit models. - Qinsheng Zhang; Molei Tao; Yongxin Chen (ICLR, 2023)

Deep Momentum Multi-Marginal Schrödinger Bridge. - Tianrong Chen; Guan-Horng Liu; Molei Tao; Evangelos A. Theodorou (NeurIPS, 2023)

Mirror Diffusion Models for Constrained and Watermarked Generation. - Guan-Horng Liu; Tianrong Chen; Evangelos A. Theodorou; Molei Tao (NeurIPS, 2023)

Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult. - Yuqing Wang; Zhenghao Xu; Tuo Zhao; Molei Tao (CoRR, 2023)

Markov Chain Monte Carlo for Gaussian: A Linear Control Perspective. - Bo Yuan 016; Jiaojiao Fan; Yuqing Wang; Molei Tao; Yongxin Chen (IEEE Control. Syst. Lett., 2023)

NySALT: Nyström-type inference-based schemes adaptive to large time-stepping. - Xingjie Li 001; Fei Lu 016; Molei Tao; Felix X.-F. Ye (J. Comput. Phys., 2023)

Extragradient Type Methods for Riemannian Variational Inequality Problems. - Zihao Hu; Guanghui Wang; Xi Wang; Andre Wibisono; Jacob D. Abernethy; Molei Tao (AISTATS, 2024)

Convergence of Kinetic Langevin Monte Carlo on Lie groups. - Lingkai Kong; Molei Tao (COLT, 2024)

Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion. - Ye He 003; Kevin Rojas; Molei Tao (NeurIPS, 2024)

Quantitative Convergences of Lie Group Momentum Optimizers. - Lingkai Kong; Molei Tao (NeurIPS, 2024)

Evaluating the design space of diffusion-based generative models. - Yuqing Wang; Ye He 003; Molei Tao (NeurIPS, 2024)

Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks. - Zhenghao Xu; Yuqing Wang; Tuo Zhao; Rachel Ward; Molei Tao (NeurIPS, 2024)

DFU: scale-robust diffusion model for zero-shot super-resolution image generation. - Alexander Havrilla; Kevin Rojas; Wenjing Liao; Molei Tao (CoRR, 2024)

Quantum State Generation with Structure-Preserving Diffusion Model. - Yuchen Zhu; Tianrong Chen; Evangelos A. Theodorou; Xie Chen 009; Molei Tao (CoRR, 2024)

Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models. - Xinxi Zhang; Song Wen 001; Ligong Han; Felix Juefei-Xu; Akash Srivastava; Junzhou Huang; Hao Wang 014; Molei Tao; Dimitris N. Metaxas (CoRR, 2024)

Robust First and Second-Order Differentiation for Regularized Optimal Transport. - Xingjie Li 001; Fei Lu 016; Molei Tao; Felix X.-F. Ye (CoRR, 2024)

DeepTTV: Deep Learning Prediction of Hidden Exoplanet From Transit Timing Variations. - Chen Chen; Lingkai Kong; Gongjie Li; Molei Tao (CoRR, 2024)

Plug-and-Play Controllable Generation for Discrete Masked Models. - Wei Guo; Yuchen Zhu; Molei Tao; Yongxin Chen (CoRR, 2024)

Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks. - Zhenghao Xu; Yuqing Wang; Tuo Zhao; Rachel Ward; Molei Tao (CoRR, 2024)

Generative modeling assisted simulation of measurement-altered quantum criticality. - Yuchen Zhu; Molei Tao; Yuebo Jin; Xie Chen 009 (CoRR, 2024)

Automated construction of effective potential via algorithmic implicit bias. - Xingjie Helen Li 001; Molei Tao (J. Comput. Phys., 2024)

Variational Schrödinger Momentum Diffusion. - Kevin Rojas; Yixin Tan; Molei Tao; Yuriy Nevmyvaka; Wei Deng 002 (AISTATS, 2025)

Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling. - Wei Guo; Molei Tao; Yongxin Chen (ICLR, 2025)

Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups. - Yuchen Zhu; Tianrong Chen; Lingkai Kong; Evangelos A. Theodorou; Molei Tao (ICLR, 2025)

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images. - Sichen Zhu; Yuchen Zhu; Molei Tao; Peng Qiu (ICLR, 2025)

SODA: Spectral Orthogonal Decomposition Adaptation for Diffusion Models. - Xinxi Zhang; Song Wen 001; Ligong Han; Felix Juefei-Xu; Akash Srivastava; Junzhou Huang; Vladimir Pavlovic 001; Hao Wang 014; Molei Tao; Dimitris N. Metaxas (WACV, 2025)

Appropriate State-Dependent Friction Coefficient Accelerates Kinetic Langevin Dynamics. - Keunwoo Lim; Molei Tao (SIAM J. Appl. Math., 2025)

Robust First- and Second-Order Differentiation for Regularized Optimal Transport. - Xingjie Li; Fei Lu; Molei Tao; Felix X.-F. Ye (SIAM J. Sci. Comput., 2025)

Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms. - Yinuo Ren; Haoxuan Chen; Yuchen Zhu; Wei Guo; Yongxin Chen; Grant M. Rotskoff; Molei Tao; Lexing Ying (CoRR, 2025)

Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond. - Wei Guo; Molei Tao; Yongxin Chen (CoRR, 2025)

Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces. - Kevin Rojas; Yuchen Zhu; Sichen Zhu; Felix X.-F. Ye; Molei Tao (CoRR, 2025)

What Exactly Does Guidance Do in Masked Discrete Diffusion Models. - Ye He 003; Kevin Rojas; Molei Tao (CoRR, 2025)

Variational Learning Finds Flatter Solutions at the Edge of Stability. - Avrajit Ghosh; Bai Cong; Rio Yokota; Saiprasad Ravishankar; Rongrong Wang; Molei Tao; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)

Non-equilibrium Annealed Adjoint Sampler. - Jaemoo Choi; Yongxin Chen; Molei Tao; Guan-Horng Liu (CoRR, 2025)

Theory-Informed Improvements to Classifier-Free Guidance for Discrete Diffusion Models. - Kevin Rojas; Ye He 003; Chieh-Hsin Lai; Yuhta Takida; Yuki Mitsufuji; Molei Tao (CoRR, 2025)

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control. - Yuchen Zhu; Wei Guo; Jaemoo Choi; Guan-Horng Liu; Yongxin Chen; Molei Tao (CoRR, 2025)