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Alex Boyd
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Large scale multi-actor generative dialog modeling
A Boyd, R Puri, M Shoeybi, M Patwary, B Catanzaro
arXiv preprint arXiv:2005.06114, 2020
222020
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
172021
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
142020
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
102020
Structured stochastic gradient MCMC
A Alexos, AJ Boyd, S Mandt
International Conference on Machine Learning, 414-434, 2022
82022
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
32022
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
32021
Understanding pathologies of deep heteroskedastic regression
E Wong-Toi, A Boyd, V Fortuin, S Mandt
arXiv preprint arXiv:2306.16717, 2023
22023
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
22023
Inference for mark-censored temporal point processes
A Boyd, Y Chang, S Mandt, P Smyth
Uncertainty in Artificial Intelligence, 226-236, 2023
12023
Variational beam search for novelty detection
A Li, AJ Boyd, P Smyth, S Mandt
Third Symposium on Advances in Approximate Bayesian Inference, 2020
12020
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
12020
On the Efficient Marginalization of Probabilistic Sequence Models
A Boyd
arXiv preprint arXiv:2403.04005, 2024
2024
Probabilistic Modeling for Sequences of Sets in Continuous-Time
Y Chang, A Boyd, P Smyth
arXiv preprint arXiv:2312.15045, 2023
2023
Bayesian Online Learning for Consensus Prediction
S Showalter, A Boyd, P Smyth, M Steyvers
arXiv preprint arXiv:2312.07679, 2023
2023
Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach
A Alexos, AJ Boyd, S Mandt
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
2022
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
Personalizing Marked Temporal Point Process Models
A Boyd, P Smyth, R Bamler, S Mandt
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Articles 1–19