Molei Tao
27 papers · 2020–2026 · 8 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (41) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Keyword Champion
(4)
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Keyword Pioneer
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Triple Crown
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Prolific Year · Productive
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Unstoppable · Bronze
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The Questioner
(2)
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Century Club · Bronze
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Keyword Collector
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Top co-authors
Research topics
Machine Learning
(25)
Optimization & Theory · Machine Learning
(22)
Deep Learning
(17)
Mathematics & Optimization
(14)
Optimization
(12)
Models
(9)
Optimization & Theory · Deep Learning
(7)
Bayesian & Probabilistic · Machine Learning
(5)
Artificial Intelligence
(4)
Probability
(4)
Application Areas
(3)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Core Methods
(3)
Learning Types
(3)
Computer Vision
(2)
Keywords
diffusion model
(7)
markov chain monte carlo
(5)
lie group
(4)
convergence rate
(4)
gradient descent
(4)
langevin monte carlo
(3)
implicit bia
(3)
learning rate
(3)
non-convex optimization
(3)
image generation
(3)
sampling error
(2)
optimal transport
(2)
matrix factorization
(2)
stochastic differential equation
(2)
score function
(2)
convex optimization
(2)
conditional generation
(2)
wasserstein distance
(2)
generative model
(2)
nesterov accelerated gradient
(2)
Papers
How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-dimensional Neural Network Regression?
COLT 2026
Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces
ICML 2025
Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images
ICLR 2025
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult
JMLR 2025
Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling
ICLR 2025
SODA: Spectral Orthogonal Decomposition Adaptation for Diffusion Models
WACV 2025
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
ICLR 2025
Variational Schrödinger Momentum Diffusion
AISTATS 2025
Convergence of Kinetic Langevin Monte Carlo on Lie groups
COLT 2024
Evaluating the design space of diffusion-based generative models
NeurIPS 2024
Extragradient Type Methods for Riemannian Variational Inequality Problems
AISTATS 2024
Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
NeurIPS 2024
Quantitative Convergences of Lie Group Momentum Optimizers
NeurIPS 2024
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion
NeurIPS 2024
Deep Momentum Multi-Marginal Schrödinger Bridge
NeurIPS 2023
gDDIM: Generalized denoising diffusion implicit models
ICLR 2023
Mirror Diffusion Models for Constrained and Watermarked Generation
NeurIPS 2023
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport
ICLR 2023
Alternating Mirror Descent for Constrained Min-Max Games
NeurIPS 2022
Hessian-Free High-Resolution Nesterov Acceleration For Sampling
ICML 2022
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
ICLR 2022
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
ICLR 2022
The Mirror Langevin Algorithm Converges with Vanishing Bias
ALT 2022
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
ICML 2021
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
NeurIPS 2020
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems
AISTATS 2020
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
NeurIPS 2020