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2020 – today
- 2024
[j2]Bowen Lei, Dongkuan Xu, Ruqi Zhang, Bani K. Mallick:
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World. Trans. Mach. Learn. Res. 2024 (2024)- [j1]Ziyi Wang, Yujie Chen, Qifan Song, Ruqi Zhang:
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo. Trans. Mach. Learn. Res. 2024 (2024) - [c20]Junbo Li, Zichen Miao, Qiang Qiu, Ruqi Zhang:
Training Bayesian Neural Networks with Sparse Subspace Variational Inference. ICLR 2024 - [c19]Bolian Li, Ruqi Zhang:
Entropy-MCMC: Sampling from Flat Basins with Ease. ICLR 2024 - [c18]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, 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:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i25]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, 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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i24]Junbo Li, Zichen Miao, Qiang Qiu, Ruqi Zhang:
Training Bayesian Neural Networks with Sparse Subspace Variational Inference. CoRR abs/2402.11025 (2024) - [i23]Patrick Pynadath, Riddhiman Bhattacharya, Arun Hariharan, Ruqi Zhang:
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling. CoRR abs/2402.17699 (2024) - [i22]Bowen Lei, Dongkuan Xu, Ruqi Zhang, Bani K. Mallick:
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World. CoRR abs/2403.20047 (2024) - [i21]Bolian Li, Yifan Wang, Ananth Grama, Ruqi Zhang:
Cascade Reward Sampling for Efficient Decoding-Time Alignment. CoRR abs/2406.16306 (2024) - [i20]Xukun Liu, Bowen Lei, Ruqi Zhang, Dongkuan Xu:
Adaptive Draft-Verification for Efficient Large Language Model Decoding. CoRR abs/2407.12021 (2024) - [i19]Pascal Jutras-Dubé, Ruqi Zhang, Aniket Bera:
Adaptive Planning with Generative Models under Uncertainty. CoRR abs/2408.01510 (2024) - [i18]Vineet Punyamoorty, Pascal Jutras-Dubé, Ruqi Zhang, Vaneet Aggarwal, Damon Conover, Aniket Bera:
Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion. CoRR abs/2409.16950 (2024) - 2023
- [c17]Tunazzina Islam
, Ruqi Zhang, Dan Goldwasser:
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging. AIES 2023: 15-25 - [c16]Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang:
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation. AISTATS 2023: 7288-7310 - [c15]Dongyao Zhu, Yanbo Fang, Bowen Lei, Yiqun Xie, Dongkuan Xu, Jie Zhang, Ruqi Zhang:
Rethinking Data Distillation: Do Not Overlook Calibration. ICCV 2023: 4912-4922 - [c14]Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani K. Mallick:
Calibrating the Rigged Lottery: Making All Tickets Reliable. ICLR 2023 - [c13]Wanrong Zhang, Ruqi Zhang:
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference. ICML 2023: 41847-41860 - [c12]Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, Dale Schuurmans, Hanjun Dai:
DISCS: A Benchmark for Discrete Sampling. NeurIPS 2023 - [i17]Bowen Lei, Dongkuan Xu, Ruqi Zhang, Shuren He, Bani K. Mallick:
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction. CoRR abs/2301.03573 (2023) - [i16]Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani K. Mallick:
Calibrating the Rigged Lottery: Making All Tickets Reliable. CoRR abs/2302.09369 (2023) - [i15]Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang:
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation. CoRR abs/2302.13929 (2023) - [i14]Bolian Li, Ruqi Zhang:
Long-tailed Classification from a Bayesian-decision-theory Perspective. CoRR abs/2303.06075 (2023) - [i13]Wanrong Zhang, Ruqi Zhang:
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference. CoRR abs/2303.06171 (2023) - [i12]Tunazzina Islam, Ruqi Zhang, Dan Goldwasser:
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging. CoRR abs/2305.06174 (2023) - [i11]Dongyao Zhu, Bowen Lei, Jie Zhang, Yanbo Fang, Ruqi Zhang, Yiqun Xie, Dongkuan Xu:
Rethinking Data Distillation: Do Not Overlook Calibration. CoRR abs/2307.12463 (2023) - [i10]Bolian Li, Ruqi Zhang:
Entropy-MCMC: Sampling from Flat Basins with Ease. CoRR abs/2310.05401 (2023) - [i9]Ziyi Wang, Yujie Chen, Qifan Song, Ruqi Zhang:
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo. CoRR abs/2310.16320 (2023) - 2022
- [c11]Ruqi Zhang, Xingchao Liu, Qiang Liu:
A Langevin-like Sampler for Discrete Distributions. ICML 2022: 26375-26396 - [c10]Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa:
Low-Precision Stochastic Gradient Langevin Dynamics. ICML 2022: 26624-26644 - [c9]Ruqi Zhang, Qiang Liu, Xin T. Tong:
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. NeurIPS 2022 - [i8]Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa:
Low-Precision Stochastic Gradient Langevin Dynamics. CoRR abs/2206.09909 (2022) - [i7]Ruqi Zhang, Xingchao Liu, Qiang Liu:
A Langevin-like Sampler for Discrete Distributions. CoRR abs/2206.09914 (2022) - [i6]Ruqi Zhang, Qiang Liu, Xin T. Tong:
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. CoRR abs/2210.06447 (2022) - 2021
- [c8]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. AISTATS 2021: 4024-4032 - 2020
- [c7]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. AISTATS 2020: 2142-2152 - [c6]Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson:
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. ICLR 2020 - [c5]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. NeurIPS 2020 - [i5]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. CoRR abs/2003.00193 (2020) - [i4]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. CoRR abs/2006.11677 (2020) - [i3]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning for Variational Inference. CoRR abs/2007.02912 (2020)
2010 – 2019
- 2019
- [c4]Ruqi Zhang, Christopher De Sa:
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. NeurIPS 2019: 4923-4932 - [i2]Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson:
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. CoRR abs/1902.03932 (2019) - [i1]Ruqi Zhang, Christopher De Sa:
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. CoRR abs/1911.09771 (2019) - 2018
- [c3]Xiaochun Han, Yanni Fan, Haijun Zhao, Shijun Wang, Sisheng Tian, Hongyuan Zhang, Zhe Yang, Ruqi Zhang, Jing Zhang, Wei Shi:
Acupoint Selection Rule Mining of Premature Ovarian Failure Treatment with Acupuncture and Moxibustion Based on the Data Analysis of Clinical Literature. BIBM 2018: 1866-1871 - 2016
- [c2]Ruqi Zhang, Zhiwu Lu:
Large Scale Sparse Clustering. IJCAI 2016: 2336-2342 - 2015
- [c1]Ruqi Zhang, Zhirong Yang, Jukka Corander:
Denoising Cluster Analysis. ICONIP (3) 2015: 435-442
Coauthor Index
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