Denoising Cluster Analysis. - Ruqi Zhang; Zhirong Yang; Jukka Corander (ICONIP (3), 2015)

Large Scale Sparse Clustering. - Ruqi Zhang; Zhiwu Lu 001 (IJCAI, 2016)

Acupoint Selection Rule Mining of Premature Ovarian Failure Treatment with Acupuncture and Moxibustion Based on the Data Analysis of Clinical Literature. - Xiaochun Han; Yanni Fan; Haijun Zhao; Shijun Wang; Sisheng Tian; Hongyuan Zhang; Zhe Yang; Ruqi Zhang; Jing Zhang; Wei Shi (BIBM, 2018)

Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. - Ruqi Zhang; Christopher De Sa (NeurIPS, 2019)

AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. - Ruqi Zhang; A. Feder Cooper; Christopher De Sa (AISTATS, 2020)

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. - Ruqi Zhang; Chunyuan Li; Jianyi Zhang; Changyou Chen; Andrew Gordon Wilson (ICLR, 2020)

Asymptotically Optimal Exact Minibatch Metropolis-Hastings. - Ruqi Zhang; A. Feder Cooper; Christopher De Sa (NeurIPS, 2020)

Meta-Learning for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (CoRR, 2020)

Meta-Learning Divergences for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (AISTATS, 2021)

A Langevin-like Sampler for Discrete Distributions. - Ruqi Zhang; Xingchao Liu; Qiang Liu 001 (ICML, 2022)

Low-Precision Stochastic Gradient Langevin Dynamics. - Ruqi Zhang; Andrew Gordon Wilson; Christopher De Sa (ICML, 2022)

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. - Ruqi Zhang; Qiang Liu 001; Xin T. Tong (NeurIPS, 2022)

Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging. - Tunazzina Islam; Ruqi Zhang; Dan Goldwasser (AIES, 2023)

Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation. - Yue Xiang; Dongyao Zhu; Bowen Lei; Dongkuan Xu; Ruqi Zhang (AISTATS, 2023)

Rethinking Data Distillation: Do Not Overlook Calibration. - Dongyao Zhu; Yanbo Fang; Bowen Lei; Yiqun Xie; Dongkuan Xu; Jie Zhang 081; Ruqi Zhang (ICCV, 2023)

Calibrating the Rigged Lottery: Making All Tickets Reliable. - Bowen Lei; Ruqi Zhang; Dongkuan Xu; Bani K. Mallick (ICLR, 2023)

DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference. - Wanrong Zhang 001; Ruqi Zhang (ICML, 2023)

DISCS: A Benchmark for Discrete Sampling. - Katayoon Goshvadi; Haoran Sun; Xingchao Liu; Azade Nova; Ruqi Zhang; Will Grathwohl; Dale Schuurmans; Hanjun Dai (NeurIPS, 2023)

Long-tailed Classification from a Bayesian-decision-theory Perspective. - Bolian Li; Ruqi Zhang (CoRR, 2023)

Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction. - Bowen Lei; Dongkuan Xu; Ruqi Zhang; Shuren He; Bani K. Mallick (CPAL, 2024)

Training Bayesian Neural Networks with Sparse Subspace Variational Inference. - Junbo Li; Zichen Miao; Qiang Qiu; Ruqi Zhang (ICLR, 2024)

Entropy-MCMC: Sampling from Flat Basins with Ease. - Bolian Li; Ruqi Zhang (ICLR, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Adaptive Planning with Generative Models under Uncertainty. - Pascal Jutras-Dubé; Ruqi Zhang; Aniket Bera (IROS, 2024)

Gradient-based Discrete Sampling with Automatic Cyclical Scheduling. - Patrick Pynadath; Riddhiman Bhattacharya; Arun Hariharan; Ruqi Zhang (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Cascade Reward Sampling for Efficient Decoding-Time Alignment. - Bolian Li; Yifan Wang; Ananth Grama; Ruqi Zhang (CoRR, 2024)

Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion. - Vineet Punyamoorty; Pascal Jutras-Dubé; Ruqi Zhang; Vaneet Aggarwal; Damon Conover; Aniket Bera (CoRR, 2024)

Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World. - Bowen Lei; Dongkuan Xu; Ruqi Zhang; Bani K. Mallick (Trans. Mach. Learn. Res., 2024)

Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo. - Ziyi Wang; Yujie Chen; Qifan Song; Ruqi Zhang (Trans. Mach. Learn. Res., 2024)

Adaptive Draft-Verification for Efficient Large Language Model Decoding. - Xukun Liu; Bowen Lei; Ruqi Zhang; Dongkuan Xu (AAAI, 2025)

Scalable and Efficient Probabilistic Inference for Bayesian Deep Learning and Generative Modeling. - Ruqi Zhang (AAAI, 2025)

CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought. - Boxuan Zhang 001; Ruqi Zhang (ACL (Findings), 2025)

Optimal Stochastic Trace Estimation in Generative Modeling. - Xinyang Liu; Hengrong Du; Wei Deng 002; Ruqi Zhang (AISTATS, 2025)

ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time. - Yi Ding; Bolian Li; Ruqi Zhang (ICLR, 2025)

Controlled LLM Decoding via Discrete Auto-regressive Biasing. - Patrick Pynadath; Ruqi Zhang (ICLR, 2025)

TraceAwareness and dual-strategy fuzz testing: Enhancing path coverage and crash localization with stochastic science and large language models. - Xiaoquan Chen; Jian Liu; Yingkai Zhang; Qinsong Hu; Yupeng Han; Ruqi Zhang; Jingqi Ran; Lei Yan; Baiqi Huang; Shengtin Ma (Comput. Electr. Eng., 2025)

Making Reliable and Flexible Decisions in Long-tailed Classification. - Bolian Li; Ruqi Zhang (Trans. Mach. Learn. Res., 2025)

Reheated Gradient-based Discrete Sampling for Combinatorial Optimization. - Muheng Li; Ruqi Zhang (Trans. Mach. Learn. Res., 2025)

Exploring Non-Convex Discrete Energy Landscapes: A Langevin-Like Sampler with Replica Exchange. - Haoyang Zheng; Ruqi Zhang; Guang Lin 001 (CoRR, 2025)

Single-Step Consistent Diffusion Samplers. - Pascal Jutras-Dubé; Patrick Pynadath; Ruqi Zhang (CoRR, 2025)

Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design. - Chris Zhuang; Debadyuti Mukherjee; Yingzhou Lu; Tianfan Fu; Ruqi Zhang (CoRR, 2025)

Bayesian Computation in Deep Learning. - Wenlong Chen; Bolian Li; Ruqi Zhang; Yingzhen Li (CoRR, 2025)

More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment. - Yifan Wang; Runjin Chen; Bolian Li; David Cho; Yihe Deng; Ruqi Zhang; Tianlong Chen 001; Zhangyang Wang; Ananth Grama; Junyuan Hong (CoRR, 2025)

Energy-Based Reward Models for Robust Language Model Alignment. - Anamika Lochab; Ruqi Zhang (CoRR, 2025)

Entropy-Guided Sampling of Flat Modes in Discrete Spaces. - Pinaki Mohanty; Riddhiman Bhattacharya; Ruqi Zhang (CoRR, 2025)

Sherlock: Self-Correcting Reasoning in Vision-Language Models. - Yi Ding; Ruqi Zhang (CoRR, 2025)

Inference Acceleration of Autoregressive Normalizing Flows by Selective Jacobi Decoding. - Jiaru Zhang; Juanwu Lu; Ziran Wang; Ruqi Zhang (CoRR, 2025)

Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization. - Xinyu Luo; Cedar Site Bai; Bolian Li; Petros Drineas; Ruqi Zhang; Brian Bullins (CoRR, 2025)

VERA: Variational Inference Framework for Jailbreaking Large Language Models. - Anamika Lochab; Lu Yan; Patrick Pynadath; Xiangyu Zhang 001; Ruqi Zhang (CoRR, 2025)

On Learning Closed-Loop Probabilistic Multi-Agent Simulator. - Juanwu Lu; Rohit Gupta; Ahmadreza Moradipari; Kyungtae Han; Ruqi Zhang; Ziran Wang (CoRR, 2025)

ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving. - Can Cui; Yupeng Zhou; Juntong Peng; Sung-Yeon Park; Zichong Yang; Prashanth Sankaranarayanan; Jiaru Zhang; Ruqi Zhang; Ziran Wang (CoRR, 2025)

Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner. - Bolian Li; Yanran Wu; Xinyu Luo; Ruqi Zhang (CoRR, 2025)