Zhang, Ruqi

42 publications

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

ICLR 2025 Controlled LLM Decoding via Discrete Auto-Regressive Biasing Patrick Pynadath, Ruqi Zhang

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

TMLR 2025 Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design Debadyuti Mukherjee, Chris Zhuang, Yingzhou Lu, Tianfan Fu, Ruqi Zhang

ICLRW 2025 Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design Chris Zhuang, Debadyuti Mukherjee, Yingzhou Lu, Tianfan Fu, Ruqi Zhang

TMLR 2025 Making Reliable and Flexible Decisions in Long-Tailed Classification Bolian Li, Ruqi Zhang

AISTATS 2025 Optimal Stochastic Trace Estimation in Generative Modeling Xinyang Liu, Hengrong Du, Wei Deng, Ruqi Zhang

TMLR 2025 Reheated Gradient-Based Discrete Sampling for Combinatorial Optimization Muheng Li, Ruqi Zhang

AAAI 2025 Scalable and Efficient Probabilistic Inference for Bayesian Deep Learning and Generative Modeling Ruqi Zhang

NeurIPS 2025 Sherlock: Self-Correcting Reasoning in Vision-Language Models Yi Ding, Ruqi Zhang

ICLRW 2025 Single-Step Consistent Diffusion Samplers Pascal Jutras Dube, Patrick Pynadath, Ruqi Zhang

ICML 2025 Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization Xinyu Luo, Site Bai, Bolian Li, Petros Drineas, Ruqi Zhang, Brian Bullins

NeurIPS 2025 VERA: Variational Inference Framework for Jailbreaking Large Language Models Anamika Lochab, Lu Yan, Patrick Pynadath, Xiangyu Zhang, Ruqi Zhang

CPAL 2024 Balance Is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei, Dongkuan Xu, Ruqi Zhang, Shuren He, Bani Mallick

ICMLW 2024 Cascade Reward Sampling for Efficient Decoding-Time Alignment Bolian Li, Yifan Wang, Ananth Grama, Ruqi Zhang

TMLR 2024 Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World Bowen Lei, Dongkuan Xu, Ruqi Zhang, Bani Mallick

TMLR 2024 Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo Ziyi Wang, Yujie Chen, Qifan Song, Ruqi Zhang

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

NeurIPS 2024 Gradient-Based Discrete Sampling with Automatic Cyclical Scheduling Patrick Pynadath, Riddhiman Bhattacharya, Arun Hariharan, Ruqi Zhang

ICMLW 2024 Gradient-Based Discrete Sampling with Automatic Cyclical Scheduling Patrick Pynadath, Riddhiman Bhattacharya, Arun Narayanan Hariharan, Ruqi Zhang

ICML 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 Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

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

ICLR 2023 Calibrating the Rigged Lottery: Making All Tickets Reliable Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani Mallick

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

ICMLW 2023 DISCS: A Benchmark for Discrete Sampling Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Sussman Grathwohl, Dale Schuurmans, Hanjun Dai

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

AISTATS 2023 Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang

NeurIPSW 2023 Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo Ziyi Wang, Yujie Chen, Ruqi Zhang, Qifan Song

NeurIPSW 2023 Entropy-MCMC: Sampling from Flat Basins with Ease Bolian Li, Ruqi Zhang

NeurIPSW 2023 GAD-EBM: Graph Anomaly Detection Using Energy-Based Models Amit Roy, Juan Shu, Olivier Elshocht, Jeroen Smeets, Ruqi Zhang, Pan Li

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

NeurIPSW 2023 Training Bayesian Neural Networks with Sparse Subspace Variational Inference Junbo Li, Zichen Miao, Qiang Qiu, Ruqi Zhang

ICML 2022 A Langevin-like Sampler for Discrete Distributions Ruqi Zhang, Xingchao Liu, Qiang Liu

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

NeurIPSW 2022 On Equivalences Between Weight and Function-Space Langevin Dynamics Ziyu Wang, Yuhao Zhou, Ruqi Zhang, Jun Zhu

NeurIPS 2022 Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent Ruqi Zhang, Qiang Liu, Xin Tong

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

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

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

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

NeurIPS 2019 Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees Ruqi Zhang, Christopher M De Sa

IJCAI 2016 Large Scale Sparse Clustering Ruqi Zhang, Zhiwu Lu