| 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 | 316 | 2019 |
| A Langevin-like Sampler for Discrete Distributions R Zhang, X Liu, Q Liu International Conference on Machine Learning, 26375-26396, 2022 | 36 | 2022 |
| 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 | 23* | 2024 |
| Asymptotically optimal exact minibatch metropolis-hastings R Zhang, AF Cooper, CM De Sa Advances in Neural Information Processing Systems 33, 19500-19510, 2020 | 21 | 2020 |
| 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 | 20 | 2020 |
| Low-Precision Stochastic Gradient Langevin Dynamics R Zhang, AG Wilson, C De Sa International Conference on Machine Learning, 26624-26644, 2022 | 15 | 2022 |
| Large Scale Sparse Clustering. R Zhang, Z Lu IJCAI, 2336-2342, 2016 | 15 | 2016 |
| Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees R Zhang, CM De Sa Advances in Neural Information Processing Systems, 4922-4931, 2019 | 12 | 2019 |
| 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 | 10 | 2023 |
| Calibrating the Rigged Lottery: Making All Tickets Reliable B Lei, R Zhang, D Xu, B Mallick arXiv preprint arXiv:2302.09369, 2023 | 9 | 2023 |
| 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 | 9 | 2022 |
| 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 | 7 | 2021 |
| 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 | 5 | 2024 |
| 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 | 4 | 2023 |
| Training Bayesian Neural Networks with Sparse Subspace Variational Inference J Li, Z Miao, Q Qiu, R Zhang arXiv preprint arXiv:2402.11025, 2024 | 3 | 2024 |
| 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 | 3 | 2023 |
| GAD-EBM: Graph Anomaly Detection using Energy-Based Models A Roy, J Shu, O Elshocht, J Smeets, R Zhang, P Li NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023 | 2 | 2023 |
| Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo Z Wang, Y Chen, Q Song, R Zhang arXiv preprint arXiv:2310.16320, 2023 | 2 | 2023 |
| Entropy-MCMC: Sampling from Flat Basins with Ease B Li, R Zhang arXiv preprint arXiv:2310.05401, 2023 | 2 | 2023 |
| 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 | 2 | 2023 |
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
