| Large scale multi-actor generative dialog modeling A Boyd, R Puri, M Shoeybi, M Patwary, B Catanzaro arXiv preprint arXiv:2005.06114, 2020 | 27 | 2020 |
| Detecting and adapting to irregular distribution shifts in bayesian online learning A Li, A Boyd, P Smyth, S Mandt Advances in neural information processing systems 34, 6816-6828, 2021 | 26 | 2021 |
| User-dependent neural sequence models for continuous-time event data A Boyd, R Bamler, S Mandt, P Smyth Advances in Neural Information Processing Systems 33, 21488-21499, 2020 | 23 | 2020 |
| Structured stochastic gradient MCMC A Alexos, AJ Boyd, S Mandt International Conference on Machine Learning, 414-434, 2022 | 15 | 2022 |
| Predictive model-based intelligent system for automatically scaling and managing provisioned computing resources MX LaBute, T Qu, JM Stratton, XJ Duan, AJ Boyd US Patent 10,761,897, 2020 | 13 | 2020 |
| Predictive querying for autoregressive neural sequence models A Boyd, S Showalter, S Mandt, P Smyth Advances in Neural Information Processing Systems 35, 23751-23764, 2022 | 6 | 2022 |
| Probabilistic querying of continuous-time event sequences A Boyd, Y Chang, S Mandt, P Smyth International Conference on Artificial Intelligence and Statistics, 10235-10251, 2023 | 5 | 2023 |
| Dynamic survival analysis for ehr data with personalized parametric distributions P Putzel, H Do, A Boyd, H Zhong, P Smyth Machine Learning for Healthcare Conference, 648-673, 2021 | 5 | 2021 |
| Understanding pathologies of deep heteroskedastic regression E Wong-Toi, A Boyd, V Fortuin, S Mandt arXiv preprint arXiv:2306.16717, 2023 | 3 | 2023 |
| Inference for mark-censored temporal point processes A Boyd, Y Chang, S Mandt, P Smyth Uncertainty in Artificial Intelligence, 226-236, 2023 | 2 | 2023 |
| Bayesian Online Learning for Consensus Prediction S Showalter, AJ Boyd, P Smyth, M Steyvers International Conference on Artificial Intelligence and Statistics, 2539-2547, 2024 | 1 | 2024 |
| Variational beam search for novelty detection A Li, AJ Boyd, P Smyth, S Mandt Third Symposium on Advances in Approximate Bayesian Inference, 2021 | 1 | 2021 |
| Neural transformation learning for deep anomaly detection beyond images A Boyd, R Bamler, S Mandt, P Smyth 34th Conference on Neural Information Processing Systems, 2020 | 1 | 2020 |
| On the Efficient Marginalization of Probabilistic Sequence Models A Boyd UC Irvine, 2024 | | 2024 |
| Probabilistic Modeling for Sequences of Sets in Continuous-Time Y Chang, A Boyd, P Smyth arXiv preprint arXiv:2312.15045, 2023 | | 2023 |
| Variational Beam Search for Online Learning with Distribution Shifts. A Li, A Boyd, P Smyth, S Mandt CoRR, 2020 | | 2020 |
| salmon: A Symbolic Linear Regression Package for Python A Boyd, DL Sun arXiv preprint arXiv:1911.00648, 2019 | | 2019 |
| Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach A Alexos, AJ Boyd, S Mandt Fourth Symposium on Advances in Approximate Bayesian Inference, 0 | | |
| Personalizing Marked Temporal Point Process Models A Boyd, P Smyth, R Bamler, S Mandt | | |
