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Ruqi Zhang
Ruqi Zhang
Assistant Professor of Computer Science, Purdue University
Verified email at purdue.edu - Homepage
Title
Cited by
Cited by
Year
Cyclical stochastic gradient MCMC for Bayesian deep learning
R Zhang, C Li, J Zhang, C Chen, AG Wilson
International Conference on Learning Representations (ICLR), 2019
3002019
A Langevin-like Sampler for Discrete Distributions
R Zhang, X Liu, Q Liu
International Conference on Machine Learning, 26375-26396, 2022
312022
Asymptotically optimal exact minibatch metropolis-hastings
R Zhang, AF Cooper, CM De Sa
Advances in Neural Information Processing Systems 33, 19500-19510, 2020
202020
AMAGOLD: Amortized Metropolis adjustment for efficient stochastic gradient MCMC
R Zhang, AF Cooper, C De Sa
International Conference on Artificial Intelligence and Statistics, 2142-2152, 2020
192020
Low-Precision Stochastic Gradient Langevin Dynamics
R Zhang, AG Wilson, C De Sa
International Conference on Machine Learning, 26624-26644, 2022
152022
Large Scale Sparse Clustering.
R Zhang, Z Lu
IJCAI, 2336-2342, 2016
152016
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
R Zhang, CM De Sa
Advances in Neural Information Processing Systems, 4922-4931, 2019
122019
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ...
Forty-first International Conference on Machine Learning, 2024
9*2024
Calibrating the Rigged Lottery: Making All Tickets Reliable
B Lei, R Zhang, D Xu, B Mallick
arXiv preprint arXiv:2302.09369, 2023
92023
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
R Zhang, Q Liu, X Tong
Advances in Neural Information Processing Systems 35, 37108-37120, 2022
82022
Rethinking data distillation: Do not overlook calibration
D Zhu, B Lei, J Zhang, Y Fang, Y Xie, R Zhang, D Xu
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
72023
Meta-Learning Divergences for Variational Inference
R Zhang, Y Li, C De Sa, S Devlin, C Zhang
International Conference on Artificial Intelligence and Statistics, 4024-4032, 2021
72021
Balance is essence: Accelerating sparse training via adaptive gradient correction
B Lei, D Xu, R Zhang, S He, B Mallick
Conference on Parsimony and Learning, 341-378, 2024
52024
DP-Fast MH: Private, fast, and accurate Metropolis-Hastings for large-scale Bayesian inference
W Zhang, R Zhang
International Conference on Machine Learning, 41847-41860, 2023
32023
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
J Li, Z Miao, Q Qiu, R Zhang
arXiv preprint arXiv:2402.11025, 2024
22024
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
T Islam, R Zhang, D Goldwasser
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 15-25, 2023
22023
Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation
Y Xiang, D Zhu, B Lei, D Xu, R Zhang
International Conference on Artificial Intelligence and Statistics, 7288-7310, 2023
22023
Discs: A benchmark for discrete sampling
K Goshvadi, H Sun, X Liu, A Nova, R Zhang, W Grathwohl, D Schuurmans, ...
Advances in Neural Information Processing Systems 36, 2024
12024
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
Z Wang, Y Chen, Q Song, R Zhang
arXiv preprint arXiv:2310.16320, 2023
12023
Entropy-MCMC: Sampling from Flat Basins with Ease
B Li, R Zhang
arXiv preprint arXiv:2310.05401, 2023
12023
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