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ORCIDTrevor Campbell
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2020 – today
- 2024
[c29]Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell:
Mixed variational flows for discrete variables. AISTATS 2024: 2431-2439- [c28]Naitong Chen, Trevor Campbell:
Coreset Markov chain Monte Carlo. AISTATS 2024: 4438-4446 - [c27]Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté:
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. AISTATS 2024: 4600-4608 - [i27]Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic:
MCMC-driven learning. CoRR abs/2402.09598 (2024) - [i26]Trevor Campbell:
General bounds on the quality of Bayesian coresets. CoRR abs/2405.11780 (2024) - 2023
- [j5]Miguel Biron-Lattes
, Alexandre Bouchard-Côté, Trevor Campbell:
Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations. J. Comput. Graph. Stat. 32(2): 513-527 (2023) - [j4]Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood:
Conditional Permutation Invariant Flows. Trans. Mach. Learn. Res. 2023 (2023) - [c26]Zuheng Xu, Naitong Chen, Trevor Campbell:
MixFlows: principled variational inference via mixed flows. ICML 2023: 38342-38376 - [c25]Zuheng Xu, Trevor Campbell:
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. NeurIPS 2023 - [i25]Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson:
Machine Learning and the Future of Bayesian Computation. CoRR abs/2304.11251 (2023) - [i24]Zuheng Xu, Trevor Campbell:
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. CoRR abs/2307.06957 (2023) - [i23]Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté:
Pigeons.jl: Distributed Sampling From Intractable Distributions. CoRR abs/2308.09769 (2023) - [i22]Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell:
Mixed Variational Flows for Discrete Variables. CoRR abs/2308.15613 (2023) - 2022
- [j3]Zuheng Xu, Trevor Campbell:
The computational asymptotics of Gaussian variational inference and the Laplace approximation. Stat. Comput. 32(4): 63 (2022) - [c24]Naitong Chen, Zuheng Xu, Trevor Campbell:
Bayesian inference via sparse Hamiltonian flows. NeurIPS 2022 - [c23]Cian Naik, Judith Rousseau, Trevor Campbell:
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. NeurIPS 2022 - [c22]Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell:
Parallel Tempering With a Variational Reference. NeurIPS 2022 - [i21]Naitong Chen, Zuheng Xu, Trevor Campbell:
Bayesian inference via sparse Hamiltonian flows. CoRR abs/2203.05723 (2022) - [i20]Cian Naik, Judith Rousseau, Trevor Campbell:
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. CoRR abs/2203.09675 (2022) - [i19]Zuheng Xu, Naitong Chen, Trevor Campbell:
Ergodic variational flows. CoRR abs/2205.07475 (2022) - [i18]Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood:
Conditional Permutation Invariant Flows. CoRR abs/2206.09021 (2022) - 2021
- [c21]Diana Cai, Trevor Campbell, Tamara Broderick:
Finite mixture models do not reliably learn the number of components. ICML 2021: 1158-1169 - [c20]Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté:
Parallel tempering on optimized paths. ICML 2021: 10033-10042 - [c19]Thibaut Horel, Lorenzo Masoero, Raj Agrawal, Daria Roithmayr, Trevor Campbell:
The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. NeurIPS Datasets and Benchmarks 2021 - [c18]Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell:
Sequential core-set Monte Carlo. UAI 2021: 2165-2175 - 2020
- [c17]Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Validated Variational Inference via Practical Posterior Error Bounds. AISTATS 2020: 1792-1802 - [c16]Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell:
Bayesian Pseudocoresets. NeurIPS 2020 - [c15]Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell:
Slice Sampling for General Completely Random Measures. UAI 2020: 699-708 - [i17]Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell:
Slice Sampling for General Completely Random Measures. CoRR abs/2006.13925 (2020) - [i16]Sina Amini Niaki, Ehsan Haghighat, Xinglong Li, Trevor Campbell, Reza Vaziri:
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture. CoRR abs/2011.13511 (2020)
2010 – 2019
- 2019
- [j2]Trevor Campbell, Tamara Broderick:
Automated Scalable Bayesian Inference via Hilbert Coresets. J. Mach. Learn. Res. 20: 15:1-15:38 (2019) - [j1]Trevor Campbell, Brian Kulis, Jonathan P. How:
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 41(6): 1338-1352 (2019) - [c14]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS 2019: 796-805 - [c13]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. AISTATS 2019: 1822-1831 - [c12]Trevor Campbell, Xinglong Li:
Universal Boosting Variational Inference. NeurIPS 2019: 3479-3490 - [c11]Trevor Campbell, Boyan Beronov:
Sparse Variational Inference: Bayesian Coresets from Scratch. NeurIPS 2019: 11457-11468 - [i15]Trevor Campbell, Xinglong Li:
Universal Boosting Variational Inference. CoRR abs/1906.01235 (2019) - [i14]Trevor Campbell, Boyan Beronov:
Sparse Variational Inference: Bayesian Coresets from Scratch. CoRR abs/1906.03329 (2019) - [i13]Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Practical Posterior Error Bounds from Variational Objectives. CoRR abs/1910.04102 (2019) - 2018
- [c10]Trevor Campbell, Tamara Broderick:
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. ICML 2018: 697-705 - [i12]Trevor Campbell, Tamara Broderick:
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. CoRR abs/1802.01737 (2018) - [i11]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR abs/1806.10234 (2018) - [i10]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR abs/1809.09505 (2018) - [i9]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. CoRR abs/1810.04249 (2018) - [i8]Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick:
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR abs/1811.11790 (2018) - 2017
- [c9]Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III:
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures. CVPR 2017: 2403-2412 - [i7]Trevor Campbell, Tamara Broderick:
Automated Scalable Bayesian Inference via Hilbert Coresets. CoRR abs/1710.05053 (2017) - 2016
- [c8]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. NIPS 2016: 4080-4088 - [c7]Diana Cai, Trevor Campbell, Tamara Broderick:
Edge-exchangeable graphs and sparsity. NIPS 2016: 4242-4250 - [i6]Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III:
Efficient Globally Optimal Point Cloud Alignment using Bayesian Nonparametric Mixtures. CoRR abs/1603.04868 (2016) - [i5]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. CoRR abs/1605.06423 (2016) - [i4]Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III:
Small-Variance Nonparametric Clustering on the Hypersphere. CoRR abs/1607.06407 (2016) - 2015
- [c6]Trevor Campbell, Jonathan P. How:
Bayesian nonparametric set construction for robust optimization. ACC 2015: 4216-4221 - [c5]Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III:
Small-variance nonparametric clustering on the hypersphere. CVPR 2015: 334-342 - [c4]Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How:
Streaming, Distributed Variational Inference for Bayesian Nonparametrics. NIPS 2015: 280-288 - [i3]Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How:
Streaming, Distributed Variational Inference for Bayesian Nonparametrics. CoRR abs/1510.09161 (2015) - 2014
- [c3]Trevor Campbell, Jonathan P. How:
Approximate Decentralized Bayesian Inference. UAI 2014: 102-111 - [i2]Trevor Campbell, Jonathan P. How:
Decentralized Variational Bayesian Inference. CoRR abs/1403.7471 (2014) - 2013
- [c2]Trevor Campbell, Luke B. Johnson, Jonathan P. How:
Multiagent allocation of Markov decision process tasks. ACC 2013: 2356-2361 - [c1]Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. NIPS 2013: 449-457 - [i1]Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. CoRR abs/1305.6659 (2013)
Coauthor Index
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last updated on 2024-10-07 22:15 CEST by the dblp team
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