| Probabilistic symmetries and invariant neural networks B Bloem-Reddy, YW Teh Journal of Machine Learning Research 21 (90), 1-61, 2020 | 180 | 2020 |
| On the Benefits of Invariance in Neural Networks C Lyle, M van der Wilk, M Kwiatkowska, Y Gal, B Bloem-Reddy arXiv preprint arXiv:2005.00178, 2020 | 106 | 2020 |
| Lossy compression for lossless prediction Y Dubois, B Bloem-Reddy, K Ullrich, CJ Maddison Advances in Neural Information Processing Systems 34, 14014-14028, 2021 | 86 | 2021 |
| Random Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs B Bloem-Reddy, P Orbanz Journal of the Royal Statistical Society, Series B: Statistical Methodology, 2018 | 42 | 2018 |
| Indeterminacy in Generative Models: Characterization and Strong Identifiability Q Xi, B Bloem-Reddy International Conference on Artificial Intelligence and Statistics, 6912-6939, 2023 | 26* | 2023 |
| Preferential Attachment and Vertex Arrival Times B Bloem-Reddy, P Orbanz arXiv preprint arXiv:1710.02159, 2017 | 16 | 2017 |
| Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks B Bloem-Reddy, A Foster, E Mathieu, YW Teh Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, 2018 | 10 | 2018 |
| Stability of the axial phase of superfluid in aerogel with globally anisotropic scattering JP Davis, J Pollanen, B Reddy, KR Shirer, H Choi, WP Halperin Physical Review B—Condensed Matter and Materials Physics 77 (14), 140502, 2008 | 10 | 2008 |
| Uncertainty in Neural Processes S Naderiparizi, K Chiu, B Bloem-Reddy, F Wood arXiv preprint arXiv:2010.03753, 2020 | 5 | 2020 |
| Hypothesis Tests for Distributional Group Symmetry with Applications to Particle Physics K Chiu, B Bloem-Reddy NeurIPS 2023 AI for Science Workshop, 2023 | 4 | 2023 |
| Non-parametric Hypothesis Tests for Distributional Group Symmetry K Chiu, B Bloem-Reddy arXiv preprint arXiv:2307.15834, 2023 | 4 | 2023 |
| Sequential sampling of Gaussian process latent variable models M Tegnér, B Bloem-Reddy, S Roberts arXiv preprint arXiv:1807.04932, 2018 | 4 | 2018 |
| Sampling and inference for discrete random probability measures in probabilistic programs B Bloem-Reddy, E Mathieu, A Foster, T Rainforth, YW Teh, H Ge, ... NIPS 2017 Workshop on Advances in Approximate Bayesian Inference, 2017 | 4 | 2017 |
| Random Walk Models, Preferential Attachment, and Sequential Monte Carlo Methods for Analysis of Network Data B Bloem-Reddy Columbia University, 2017 | 3 | 2017 |
| Slice Sampling on Hamiltonian Trajectories B Bloem-Reddy, J Cunningham Proceedings of The 33rd International Conference on Machine Learning, 3050-3058, 2016 | 3 | 2016 |
| Triangular Monotonic Generative Models Can Perform Causal Discovery Q Xi, S Gonzalez, B Bloem-Reddy Causal Representation Learning Workshop at NeurIPS 2023, 2023 | 2 | 2023 |
| Neural network models of exchangeable sequences B Bloem-Reddy, YW Teh Third NeurIPS Workshop on Bayesian Deep Learning, 2018 | 2 | 2018 |
| Superfluid phase stability of 3He in axially anisotropic aerogel J Pollanen, JP Davis, B Reddy, KR Shirer, H Choi, WP Halperin Journal of Physics: Conference Series 150 (3), 032084, 2009 | 2 | 2009 |
| Causal Inference with Cocycles H Dance, B Bloem-Reddy arXiv preprint arXiv:2405.13844, 2024 | 1 | 2024 |
| Identifying metric structures of deep latent variable models S Syrota, Y Zainchkovskyy, J Xi, B Bloem-Reddy, S Hauberg arXiv preprint arXiv:2502.13757, 2025 | | 2025 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindstats.ox.ac.uk의 이메일 확인됨
