Lexing Ying
21 papers · 2020–2026 · 7 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
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Machine Learning
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Optimization & Theory Β· Machine Learning
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Deep Learning
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Mathematics & Optimization
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Core Methods
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distribution shift
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gradient descent
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data distribution
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physics-informed neural network
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stochastic gradient descent
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minimax optimality
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diffusion model
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partial differential equation
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wasserstein gradient flow
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optimal transport
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neural network optimization
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online learning
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loss landscape
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regret minimization
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langevin dynamics
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global convergence
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nonparametric regression
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multi-label classification
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adversarial robustness
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reproducing kernel hilbert space
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Papers
A Unified Approach to Analysis and Design of Denoising Markov Models
JMLR 2026
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
UAI 2025
How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
ICLR 2025
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
NeurIPS 2024
Accelerating Sinkhorn algorithm with sparse Newton iterations
ICLR 2024
Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
AISTATS 2024
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty
ICML 2024
Statistical Spatially Inhomogeneous Diffusion Inference
AAAI 2024
Understanding the Generalization Benefits of Late Learning Rate Decay
AISTATS 2024
Continuous-in-time Limit for Bayesian Bandits
JMLR 2023
Minimax Optimal Kernel Operator Learning via Multilevel Training
ICLR 2023
When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality
NeurIPS 2023
How to Learn when Data Gradually Reacts to Your Model
AISTATS 2022
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
ICLR 2022
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games
ICLR 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
NeurIPS 2022
How to Learn when Data Reacts to Your Model: Performative Gradient Descent
ICML 2021
On Linear Stability of SGD and Input-Smoothness of Neural Networks
NeurIPS 2021
Top-k eXtreme Contextual Bandits with Arm Hierarchy
ICML 2021
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients
ICLR 2021
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth
ICML 2020