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2020 – today
- 2024
[j7]Ryan Giordano, Martin Ingram, Tamara Broderick:
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. J. Mach. Learn. Res. 25: 18:1-18:39 (2024)- [j6]Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick:
Are you using test log-likelihood correctly? Trans. Mach. Learn. Res. 2024 (2024) - [i34]David R. Burt, Yunyi Shen, Tamara Broderick:
Consistent Validation for Predictive Methods in Spatial Settings. CoRR abs/2402.03527 (2024) - [i33]Yunyi Shen, Renato Berlinghieri, Tamara Broderick:
Multi-marginal Schrödinger Bridges with Iterative Reference Refinement. CoRR abs/2408.06277 (2024) - [i32]Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick:
A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making. CoRR abs/2409.05866 (2024) - 2023
- [j5]Raj Agrawal, Tamara Broderick:
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. J. Mach. Learn. Res. 24: 27:1-27:60 (2023) - [c29]Nicholas Bonaker
, Emli-Mari Nel, Keith Vertanen, Tamara Broderick:
A Usability Study of Nomon: A Flexible Interface for Single-Switch Users. ASSETS 2023: 3:1-3:17 - [c28]Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola:
Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. ICLR 2023 - [c27]Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. ICML 2023: 2113-2163 - [i31]Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. CoRR abs/2302.10364 (2023) - [i30]Ryan Giordano, Martin Ingram, Tamara Broderick:
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. CoRR abs/2304.05527 (2023) - 2022
- [c26]William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick:
Measuring the robustness of Gaussian processes to kernel choice. AISTATS 2022: 3308-3331 - [c25]Tin D. Nguyen, Brian L. Trippe, Tamara Broderick:
Many processors, little time: MCMC for partitions via optimal transport couplings. AISTATS 2022: 3483-3514 - [c24]Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick:
Demonstrating Nomon: A Flexible Interface for Noisy Single-Switch Users. CHI Extended Abstracts 2022: 199:1-199:4 - [c23]Nicholas Ryan Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick:
A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. CHI 2022: 495:1-495:17 - [i29]Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick:
A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. CoRR abs/2204.01619 (2022) - [i28]Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola:
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. CoRR abs/2206.04119 (2022) - [i27]Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James R. Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean E. Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L. Weiss, Allan B. Wollaber, Sophia Yuditskaya, Jeremy Kepner:
Developing a Series of AI Challenges for the United States Department of the Air Force. CoRR abs/2207.07033 (2022) - [i26]Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick:
Are you using test log-likelihood correctly? CoRR abs/2212.00219 (2022) - 2021
- [c22]Diana Cai, Trevor Campbell, Tamara Broderick:
Finite mixture models do not reliably learn the number of components. ICML 2021: 1158-1169 - [c21]Brian L. Trippe, Hilary K. Finucane, Tamara Broderick:
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. NeurIPS 2021: 13471-13484 - [c20]William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick:
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. NeurIPS 2021: 24352-24364 - [i25]William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick:
Measuring the sensitivity of Gaussian processes to kernel choice. CoRR abs/2106.06510 (2021) - [i24]Brian L. Trippe, Hilary K. Finucane, Tamara Broderick:
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. CoRR abs/2107.06428 (2021) - [i23]William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick:
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. CoRR abs/2107.09194 (2021) - [i22]Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng:
Toward a Taxonomy of Trust for Probabilistic Machine Learning. CoRR abs/2112.03270 (2021) - 2020
- [c19]Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Validated Variational Inference via Practical Posterior Error Bounds. AISTATS 2020: 1792-1802 - [c18]William T. Stephenson, Tamara Broderick:
Approximate Cross-Validation in High Dimensions with Guarantees. AISTATS 2020: 2424-2434 - [c17]Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick:
Approximate Cross-Validation for Structured Models. NeurIPS 2020 - [c16]William T. Stephenson, Madeleine Udell, Tamara Broderick:
Approximate Cross-Validation with Low-Rank Data in High Dimensions. NeurIPS 2020 - [i21]Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick:
Approximate Cross-Validation for Structured Models. CoRR abs/2006.12669 (2020) - [i20]William T. Stephenson, Madeleine Udell, Tamara Broderick:
Approximate Cross-Validation with Low-Rank Data in High Dimensions. CoRR abs/2008.10547 (2020)
2010 – 2019
- 2019
- [j4]Trevor Campbell, Tamara Broderick:
Automated Scalable Bayesian Inference via Hilbert Coresets. J. Mach. Learn. Res. 20: 15:1-15:38 (2019) - [c15]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS 2019: 796-805 - [c14]Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick:
A Swiss Army Infinitesimal Jackknife. AISTATS 2019: 1139-1147 - [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]Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML 2019: 141-150 - [c11]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML 2019: 6315-6324 - [i19]Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR abs/1905.06501 (2019) - [i18]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR abs/1905.07499 (2019) - [i17]William T. Stephenson, Tamara Broderick:
Sparse Approximate Cross-Validation for High-Dimensional GLMs. CoRR abs/1905.13657 (2019) - [i16]Ryan Giordano, Michael I. Jordan, Tamara Broderick:
A Higher-Order Swiss Army Infinitesimal Jackknife. CoRR abs/1907.12116 (2019) - [i15]Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Practical Posterior Error Bounds from Variational Objectives. CoRR abs/1910.04102 (2019) - 2018
- [j3]Ryan Giordano, Tamara Broderick, Michael I. Jordan:
Covariances, Robustness, and Variational Bayes. J. Mach. Learn. Res. 19: 51:1-51:49 (2018) - [c10]Raj Agrawal, Caroline Uhler, Tamara Broderick:
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. ICML 2018: 89-98 - [c9]Trevor Campbell, Tamara Broderick:
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. ICML 2018: 697-705 - [i14]Trevor Campbell, Tamara Broderick:
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. CoRR abs/1802.01737 (2018) - [i13]Raj Agrawal, Tamara Broderick, Caroline Uhler:
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. CoRR abs/1803.05554 (2018) - [i12]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) - [i11]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) - [i10]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) - [i9]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
- [c8]Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick:
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS 2017: 3611-3621 - [i8]Trevor Campbell, Tamara Broderick:
Automated Scalable Bayesian Inference via Hilbert Coresets. CoRR abs/1710.05053 (2017) - 2016
- [c7]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. NIPS 2016: 4080-4088 - [c6]Diana Cai, Trevor Campbell, Tamara Broderick:
Edge-exchangeable graphs and sparsity. NIPS 2016: 4242-4250 - [i7]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. CoRR abs/1605.06423 (2016) - [i6]Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson:
Boosting Variational Inference. CoRR abs/1611.05559 (2016) - 2015
- [j2]Tamara Broderick, Lester W. Mackey, John W. Paisley, Michael I. Jordan:
Combinatorial Clustering and the Beta Negative Binomial Process. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 290-306 (2015) - [c5]Ryan Giordano, Tamara Broderick, Michael I. Jordan:
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. NIPS 2015: 1441-1449 - 2014
- [b1]Tamara Broderick:
Clusters and Features from Combinatorial Stochastic Processes. University of California, Berkeley, USA, 2014 - [i5]Ryan Giordano, Tamara Broderick:
Covariance Matrices for Mean Field Variational Bayes. CoRR abs/1410.6853 (2014) - 2013
- [c4]Tamara Broderick, Brian Kulis, Michael I. Jordan:
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) 2013: 226-234 - [c3]Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. NIPS 2013: 1403-1411 - [c2]Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan:
Streaming Variational Bayes. NIPS 2013: 1727-1735 - [i4]Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan:
Streaming Variational Bayes. CoRR abs/1307.6769 (2013) - [i3]Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. CoRR abs/1307.8049 (2013) - 2012
- [i2]Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick:
Combining Spatial and Telemetric Features for Learning Animal Movement Models. CoRR abs/1203.3486 (2012) - 2011
- [j1]Tamara Broderick, Robert B. Gramacy:
Classification and Categorical Inputs with Treed Gaussian Process Models. J. Classif. 28(2): 244-270 (2011) - 2010
- [c1]Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick:
Combining Spatial and Telemetric Features for Learning Animal Movement Models. UAI 2010: 260-267
2000 – 2009
- 2009
- [i1]Tamara Broderick, David John Cameron MacKay:
Fast and flexible selection with a single switch. CoRR abs/0909.2450 (2009)
Coauthor Index
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last updated on 2024-10-16 21:23 CEST by the dblp team
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