| Stan: a probabilistic programming language B Carpenter, A Gelman, M Hoffman, D Lee, B Goodrich, M Betancourt, ... Journal of Statistical Software, 2015 | 9760 | 2015 |
| The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. MD Hoffman, A Gelman J. Mach. Learn. Res. 15 (1), 1593-1623, 2014 | 7256 | 2014 |
| Stochastic variational inference MD Hoffman, DM Blei, C Wang, J Paisley Journal of Machine Learning Research, 2013 | 3599 | 2013 |
| Online learning for latent dirichlet allocation M Hoffman, DM Blei, F Bach Advances in Neural Information Processing Systems 23, 856-864, 2010 | 2450 | 2010 |
| Variational autoencoders for collaborative filtering D Liang, RG Krishnan, MD Hoffman, T Jebara Proceedings of the 2018 World Wide Web Conference, 689-698, 2018 | 1800 | 2018 |
| Music transformer CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, I Simon, C Hawthorne, ... Advances in Neural Processing Systems 3, 4, 2018 | 1282 | 2018 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... The Journal of Machine Learning Research 23 (1), 10237-10297, 2022 | 1027 | 2022 |
| Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei arXiv preprint arXiv:1704.04289, 2017 | 869 | 2017 |
| Learning Activation Functions to Improve Deep Neural Networks F Agostinelli, M Hoffman, P Sadowski, P Baldi arXiv preprint arXiv:1412.6830, 2014 | 791 | 2014 |
| Tensorflow distributions JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ... arXiv preprint arXiv:1711.10604, 2017 | 714 | 2017 |
| What are Bayesian neural network posteriors really like? P Izmailov, S Vikram, MD Hoffman, AGG Wilson International conference on machine learning, 4629-4640, 2021 | 615 | 2021 |
| ELBO surgery: yet another way to carve up the variational evidence lower bound MD Hoffman, MJ Johnson NIPS 2016 Workshop on Advances in Approximate Bayesian Inference, 2016 | 481 | 2016 |
| Deep Probabilistic Programming D Tran, MD Hoffman, RA Saurous, E Brevdo, K Murphy, DM Blei arXiv preprint arXiv:1701.03757, 2017 | 263 | 2017 |
| A Unified View of Static and Dynamic Source Separation Using Non-Negative Factorizations P Smaragdis, C Févotte, GJ Mysore, N Mohammadiha, M Hoffman IEEE Signal Processing Magazine, 2014 | 241* | 2014 |
| Structured stochastic variational inference MD Hoffman, DM Blei Artificial Intelligence and Statistics, 2015 | 232* | 2015 |
| Nonparametric variational inference S Gershman, M Hoffman, D Blei arXiv preprint arXiv:1206.4665, 2012 | 213 | 2012 |
| Bayesian nonparametric matrix factorization for recorded music M Hoffman, D Blei, P Cook Proc. ICML, 439-446, 2010 | 210 | 2010 |
| A variational analysis of stochastic gradient algorithms S Mandt, M Hoffman, D Blei International Conference on Machine Learning, 354-363, 2016 | 206 | 2016 |
| Sparse stochastic inference for latent dirichlet allocation D Mimno, M Hoffman, D Blei arXiv preprint arXiv:1206.6425, 2012 | 199 | 2012 |
| Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models J Engel, M Hoffman, A Roberts arXiv preprint arXiv:1711.05772, 2017 | 193 | 2017 |
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
