Lingjiong Zhu
16 papers · 2019–2026 · 5 venues · across top CS/AI venues
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🗺️ Taxonomy Completionist (25) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Research topics
Machine Learning
(16)
Optimization & Theory · Machine Learning
(16)
Deep Learning
(8)
Mathematics & Optimization
(6)
Optimization
(5)
Optimization & Theory · Deep Learning
(5)
Artificial Intelligence
(3)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Models
(2)
Bayesian & Probabilistic · Machine Learning
(1)
Learning Paradigms · Artificial Intelligence
(1)
Learning Paradigms · Machine Learning
(1)
Probability
(1)
Techniques
(1)
Keywords
stochastic gradient descent
(11)
wasserstein distance
(5)
langevin dynamics
(4)
markov chain monte carlo
(3)
algorithmic stability
(3)
generalization bound
(3)
stochastic gradient
(2)
heavy-tailed noise
(2)
bayesian inference
(2)
stochastic differential equation
(2)
heavy-tailed distribution
(2)
langevin monte carlo
(2)
constrained sampling
(2)
non-convex optimization
(2)
generalization error
(2)
score-based generative model
(2)
natural gradient
(1)
neural network optimization
(1)
decentralized optimization
(1)
bayesian learning
(1)
Papers
Accelerating Constrained Sampling: A Large Deviations Approach
JMLR 2026
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances
AISTATS 2025
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
JMLR 2025
Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling
JMLR 2024
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
ICML 2023
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares
ALT 2023
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent
NeurIPS 2023
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
JMLR 2022
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
ICML 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
NeurIPS 2021
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
JMLR 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
NeurIPS 2021
The Heavy-Tail Phenomenon in SGD
ICML 2021
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
NeurIPS 2020
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
ICML 2020
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
ICML 2019