Gu, Quanquan
235 publications
ICML
2025
Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
NeurIPS
2024
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
ICLR
2024
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-Based Molecular Optimization
NeurIPS
2024
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
NeurIPSW
2024
Imbalance-Regularized LoRA: A Plug-and-Play Method for Improving Fine-Tuning of Foundation Models
NeurIPS
2024
Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent
NeurIPSW
2024
Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
NeurIPSW
2023
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-Agent Dynamical Systems
NeurIPSW
2023
CryoSTAR: Cryo-EM Heterogeneous Reconstruction of Atomic Models with Structural Regularization
ICMLW
2023
DiffMol: 3D Structured Molecule Generation with Discrete Denoising Diffusion Probabilistic Models
NeurIPS
2023
Implicit Bias of Gradient Descent for Two-Layer ReLU and Leaky ReLU Networks on Nearly-Orthogonal Data
NeurIPSW
2023
MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction
ICML
2023
On the Interplay Between Misspecification and Sub-Optimality Gap in Linear Contextual Bandits
NeurIPS
2023
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
ICLR
2023
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
AISTATS
2022
Near-Optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
AISTATS
2022
Nearly Minimax Optimal Regret for Learning Infinite-Horizon Average-Reward MDPs with Linear Function Approximation
NeurIPSW
2022
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
NeurIPS
2022
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
ICML
2022
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
NeurIPS
2022
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium
ACML
2022
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes
NeurIPS
2022
The Power and Limitation of Pretraining-Finetuning for Linear Regression Under Covariate Shift
ICLR
2021
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
NeurIPSW
2021
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
COLT
2021
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
NeurIPS
2021
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
NeurIPS
2021
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
NeurIPS
2021
Risk Bounds for Over-Parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
IJCAI
2020
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
AAAI
2020
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks
AISTATS
2020
Understanding the Intrinsic Robustness of Image Distributions Using Conditional Generative Models
NeurIPS
2019
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
AISTATS
2019
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics
AISTATS
2018
Accelerated Stochastic Mirror Descent: From Continuous-Time Dynamics to Discrete-Time Algorithms
ICML
2018
Continuous and Discrete-Time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
NeurIPS
2018
Third-Order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
AISTATS
2017
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
AISTATS
2017
Efficient Algorithm for Sparse Tensor-Variate Gaussian Graphical Models via Gradient Descent
NeurIPS
2017
Speeding up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
ICML
2017
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference