Trevor Campbell

73 papers A* 16A 11C 2Journal 39Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space. CoRR Kyurae Kim, Qiang Fu, Yi-An Ma, Jacob R. Gardner, Trevor Campbell
2025	J	`jnl`	AutoGD: Automatic Learning Rate Selection for Gradient Descent. CoRR Nikola Surjanovic, Alexandre Bouchard-Côté, Trevor Campbell
2025	J	`jnl`	AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent. CoRR Nikola Surjanovic, Alexandre Bouchard-Côté, Trevor Campbell
2025	A*	`conf`	AutoStep: Locally adaptive involutive MCMC. ICML Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
2025	A	`conf`	Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? AISTATS Son Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Côté
2025	J	`jnl`	Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. CoRR Kyurae Kim, Yi-An Ma, Trevor Campbell, Jacob R. Gardner
2025	A*	`conf`	Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. ICML Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
2025	J	`jnl`	Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. CoRR Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
2025	A	`conf`	Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. UAI Naitong Chen, Jonathan H. Huggins, Trevor Campbell
2024	J	`jnl`	AutoStep: Locally adaptive involutive MCMC. CoRR Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
2024	A	`conf`	Coreset Markov chain Monte Carlo. AISTATS Naitong Chen, Trevor Campbell
2024	A*	`conf`	General bounds on the quality of Bayesian coresets. NeurIPS Trevor Campbell
2024	J	`jnl`	General bounds on the quality of Bayesian coresets. CoRR Trevor Campbell
2024	J	`jnl`	MCMC-driven learning. CoRR Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic
2024	A	`conf`	Mixed variational flows for discrete variables. AISTATS Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell
2024	J	`jnl`	Tuning-free coreset Markov chain Monte Carlo. CoRR Naitong Chen, Jonathan H. Huggins, Trevor Campbell
2024	A	`conf`	autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. AISTATS Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté
2023	J	`jnl`	Conditional Permutation Invariant Flows. Trans. Mach. Learn. Res. Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood
2023	A*	`conf`	Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. NeurIPS Zuheng Xu, Trevor Campbell
2023	J	`jnl`	Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. CoRR Zuheng Xu, Trevor Campbell
2023	J	`jnl`	Machine Learning and the Future of Bayesian Computation. CoRR Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson
2023	A*	`conf`	MixFlows: principled variational inference via mixed flows. ICML Zuheng Xu, Naitong Chen, Trevor Campbell
2023	J	`jnl`	Mixed Variational Flows for Discrete Variables. CoRR Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell
2023	J	`jnl`	Pigeons.jl: Distributed Sampling From Intractable Distributions. CoRR Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté
2023	J	`jnl`	Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations. J. Comput. Graph. Stat. Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
2022	A*	`conf`	Bayesian inference via sparse Hamiltonian flows. NeurIPS Naitong Chen, Zuheng Xu, Trevor Campbell
2022	J	`jnl`	Bayesian inference via sparse Hamiltonian flows. CoRR Naitong Chen, Zuheng Xu, Trevor Campbell
2022	J	`jnl`	Conditional Permutation Invariant Flows. CoRR Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood
2022	J	`jnl`	Ergodic variational flows. CoRR Zuheng Xu, Naitong Chen, Trevor Campbell
2022	A*	`conf`	Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. NeurIPS Cian Naik, Judith Rousseau, Trevor Campbell
2022	J	`jnl`	Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. CoRR Cian Naik, Judith Rousseau, Trevor Campbell
2022	A*	`conf`	Parallel Tempering With a Variational Reference. NeurIPS Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell
2022	J	`jnl`	The computational asymptotics of Gaussian variational inference and the Laplace approximation. Stat. Comput. Zuheng Xu, Trevor Campbell
2021	A*	`conf`	Finite mixture models do not reliably learn the number of components. ICML Diana Cai, Trevor Campbell, Tamara Broderick
2021	A*	`conf`	Parallel tempering on optimized paths. ICML Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté
2021	A	`conf`	Sequential core-set Monte Carlo. UAI Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell
2021	—	`conf`	The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. NeurIPS Datasets and Benchmarks Thibaut Horel, Lorenzo Masoero, Raj Agrawal, Daria Roithmayr, Trevor Campbell
2020	A*	`conf`	Bayesian Pseudocoresets. NeurIPS Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell
2020	J	`jnl`	Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture. CoRR Sina Amini Niaki, Ehsan Haghighat, Xinglong Li, Trevor Campbell, Reza Vaziri
2020	A	`conf`	Slice Sampling for General Completely Random Measures. UAI Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
2020	J	`jnl`	Slice Sampling for General Completely Random Measures. CoRR Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
2020	A	`conf`	Validated Variational Inference via Practical Posterior Error Bounds. AISTATS Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019	J	`jnl`	Automated Scalable Bayesian Inference via Hilbert Coresets. J. Mach. Learn. Res. Trevor Campbell, Tamara Broderick
2019	A	`conf`	Data-dependent compression of random features for large-scale kernel approximation. AISTATS Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2019	J	`jnl`	Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. Trevor Campbell, Brian Kulis, Jonathan P. How
2019	J	`jnl`	Practical Posterior Error Bounds from Variational Objectives. CoRR Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019	A	`conf`	Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2019	A*	`conf`	Sparse Variational Inference: Bayesian Coresets from Scratch. NeurIPS Trevor Campbell, Boyan Beronov
2019	J	`jnl`	Sparse Variational Inference: Bayesian Coresets from Scratch. CoRR Trevor Campbell, Boyan Beronov
2019	A*	`conf`	Universal Boosting Variational Inference. NeurIPS Trevor Campbell, Xinglong Li
2019	J	`jnl`	Universal Boosting Variational Inference. CoRR Trevor Campbell, Xinglong Li
2018	A*	`conf`	Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. ICML Trevor Campbell, Tamara Broderick
2018	J	`jnl`	Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. CoRR Trevor Campbell, Tamara Broderick
2018	J	`jnl`	Data-dependent compression of random features for large-scale kernel approximation. CoRR Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2018	J	`jnl`	Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2018	J	`jnl`	Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick
2018	J	`jnl`	Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2017	J	`jnl`	Automated Scalable Bayesian Inference via Hilbert Coresets. CoRR Trevor Campbell, Tamara Broderick
2017	A*	`conf`	Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures. CVPR Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
2016	—	`conf`	Coresets for Scalable Bayesian Logistic Regression. NIPS Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
2016	J	`jnl`	Coresets for Scalable Bayesian Logistic Regression. CoRR Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
2016	—	`conf`	Edge-exchangeable graphs and sparsity. NIPS Diana Cai, Trevor Campbell, Tamara Broderick
2016	J	`jnl`	Efficient Globally Optimal Point Cloud Alignment using Bayesian Nonparametric Mixtures. CoRR Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
2016	J	`jnl`	Small-Variance Nonparametric Clustering on the Hypersphere. CoRR Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
2015	C	`conf`	Bayesian nonparametric set construction for robust optimization. ACC Trevor Campbell, Jonathan P. How
2015	A*	`conf`	Small-variance nonparametric clustering on the hypersphere. CVPR Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
2015	—	`conf`	Streaming, Distributed Variational Inference for Bayesian Nonparametrics. NIPS Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
2015	J	`jnl`	Streaming, Distributed Variational Inference for Bayesian Nonparametrics. CoRR Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
2014	A	`conf`	Approximate Decentralized Bayesian Inference. UAI Trevor Campbell, Jonathan P. How
2014	J	`jnl`	Decentralized Variational Bayesian Inference. CoRR Trevor Campbell, Jonathan P. How
2013	J	`jnl`	Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture CoRR Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How
2013	—	`conf`	Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. NIPS Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
2013	C	`conf`	Multiagent allocation of Markov decision process tasks. ACC Trevor Campbell, Luke B. Johnson, Jonathan P. How