| Functional variational Bayesian neural networks S Sun, G Zhang, J Shi, R Grosse arXiv preprint arXiv:1903.05779, 2019 | 348 | 2019 |
| On the spectral efficiency of massive MIMO systems with low-resolution ADCs J Zhang, L Dai, S Sun, Z Wang IEEE Communications Letters 20 (5), 842-845, 2016 | 307 | 2016 |
| Noisy Natural Gradient as Variational Inference G Zhang, S Sun, D Duvenaud, R Grosse International Conference on Machine Learning 2018, 2017 | 278 | 2017 |
| Nemotron-4 340b technical report B Adler, N Agarwal, A Aithal, DH Anh, P Bhattacharya, A Brundyn, ... arXiv preprint arXiv:2406.11704, 2024 | 158 | 2024 |
| Learning structured weight uncertainty in bayesian neural networks S Sun, C Chen, L Carin International Conference on Artificial Intelligence and Statistics 2017 …, 2017 | 156 | 2017 |
| A Spectral Approach to Gradient Estimation for Implicit Distributions J Shi, S Sun, J Zhu International Conference on Machine Learning 2018, 2018 | 119 | 2018 |
| Differentiable Compositional Kernel Learning for Gaussian Processes S Sun, G Zhang, C Wang, W Zeng, J Li, R Grosse International Conference on Machine Learning 2018, 2018 | 109 | 2018 |
| Aggregated momentum: Stability through passive damping J Lucas, S Sun, R Zemel, R Grosse arXiv preprint arXiv:1804.00325, 2018 | 97 | 2018 |
| Kernel implicit variational inference J Shi, S Sun, J Zhu International Conference on Learning Representations 2018, 2017 | 72 | 2017 |
| Information-theoretic online memory selection for continual learning S Sun, D Calandriello, H Hu, A Li, M Titsias arXiv preprint arXiv:2204.04763, 2022 | 66 | 2022 |
| ZhuSuan: A library for Bayesian deep learning J Shi, J Chen, J Zhu, S Sun, Y Luo, Y Gu, Y Zhou arXiv preprint arXiv:1709.05870, 2017 | 47 | 2017 |
| Understanding the variance collapse of SVGD in high dimensions J Ba, MA Erdogdu, M Ghassemi, S Sun, T Suzuki, D Wu, T Zhang International Conference on Learning Representations, 2021 | 43 | 2021 |
| Fast-rate PAC-Bayes generalization bounds via shifted Rademacher processes J Yang, S Sun, DM Roy Advances in Neural Information Processing Systems 32, 2019 | 40 | 2019 |
| Nemo-aligner: Scalable toolkit for efficient model alignment G Shen, Z Wang, O Delalleau, J Zeng, Y Dong, D Egert, S Sun, J Zhang, ... arXiv preprint arXiv:2405.01481, 2024 | 26 | 2024 |
| Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? C Wang, S Sun, R Grosse International Conference on Artificial Intelligence and Statistics, 2476-2484, 2021 | 24 | 2021 |
| Towards characterizing the high-dimensional bias of kernel-based particle inference algorithms J Ba, MA Erdogdu, M Ghassemi, T Suzuki, S Sun, D Wu, T Zhang 2nd Symposium on Advances in Approximate Bayesian Inference, 1-17, 2019 | 12 | 2019 |
| Scalable variational Gaussian processes via harmonic kernel decomposition S Sun, J Shi, AG Wilson, R Grosse arXiv preprint arXiv:2106.05992, 2021 | 10 | 2021 |
| Neural networks as inter-domain inducing points S Sun, J Shi, RB Grosse Third Symposium on Advances in Approximate Bayesian Inference, 2020 | 7 | 2020 |
| Adversarial training of reward models A Bukharin, H Qian, S Sun, A Renduchintala, S Singhal, Z Wang, ... arXiv preprint arXiv:2504.06141, 2025 | 5 | 2025 |
| Reward-aware preference optimization: A unified mathematical framework for model alignment S Sun, Y Zhang, A Bukharin, D Mosallanezhad, J Zeng, S Singhal, ... arXiv preprint arXiv:2502.00203, 2025 | 4 | 2025 |
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
