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Martin Jankowiak
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2020 – today
- 2024
- [c14]Martin Jankowiak, Du Phan:
Reparameterized Variational Rejection Sampling. AISTATS 2024: 739-747 - 2023
- [c13]Martin Jankowiak:
Bayesian Variable Selection in a Million Dimensions. AISTATS 2023: 253-282 - [i19]Martin Jankowiak, Du Phan:
Reparameterized Variational Rejection Sampling. CoRR abs/2309.14612 (2023) - 2022
- [c12]Martin Jankowiak, Du Phan:
Surrogate Likelihoods for Variational Annealed Importance Sampling. ICML 2022: 9881-9901 - [i18]Martin Jankowiak:
Bayesian Variable Selection in a Million Dimensions. CoRR abs/2208.01180 (2022) - 2021
- [c11]David Eriksson, Martin Jankowiak:
High-dimensional Bayesian optimization with sparse axis-aligned subspaces. UAI 2021: 493-503 - [i17]David Eriksson, Martin Jankowiak:
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces. CoRR abs/2103.00349 (2021) - [i16]Martin Jankowiak, Geoff Pleiss:
Scalable Cross Validation Losses for Gaussian Process Models. CoRR abs/2105.11535 (2021) - [i15]Martin Jankowiak, Du Phan:
Surrogate Likelihoods for Variational Annealed Importance Sampling. CoRR abs/2112.12194 (2021) - 2020
- [c10]Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS 2020: 2959-2969 - [c9]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Parametric Gaussian Process Regressors. ICML 2020: 4702-4712 - [c8]Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner:
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS 2020 - [c7]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. UAI 2020: 789-798 - [i14]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. CoRR abs/2002.09112 (2020) - [i13]Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner:
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR abs/2006.11267 (2020)
2010 – 2019
- 2019
- [j1]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. J. Mach. Learn. Res. 20: 28:1-28:6 (2019) - [c6]Martin Jankowiak, Theofanis Karaletsos:
Pathwise Derivatives for Multivariate Distributions. AISTATS 2019: 333-342 - [c5]Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin T. Chiu, Alexander M. Rush, Noah D. Goodman:
Tensor Variable Elimination for Plated Factor Graphs. ICML 2019: 4871-4880 - [c4]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Bayesian Optimal Experimental Design. NeurIPS 2019: 14036-14047 - [i12]Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Justin T. Chiu, Neeraj Pradhan, Alexander M. Rush, Noah D. Goodman:
Tensor Variable Elimination for Plated Factor Graphs. CoRR abs/1902.03210 (2019) - [i11]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Estimators for Bayesian Optimal Experimental Design. CoRR abs/1903.05480 (2019) - [i10]Martin Jankowiak, Jacob R. Gardner:
Neural Likelihoods for Multi-Output Gaussian Processes. CoRR abs/1905.13697 (2019) - [i9]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Sparse Gaussian Process Regression Beyond Variational Inference. CoRR abs/1910.07123 (2019) - [i8]Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Du Phan, Jonathan P. Chen:
Functional Tensors for Probabilistic Programming. CoRR abs/1910.10775 (2019) - [i7]Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR abs/1911.00294 (2019) - [i6]Du Phan, Neeraj Pradhan, Martin Jankowiak:
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro. CoRR abs/1912.11554 (2019) - 2018
- [c3]Martin Jankowiak, Fritz Obermeyer:
Pathwise Derivatives Beyond the Reparameterization Trick. ICML 2018: 2240-2249 - [i5]Martin Jankowiak, Fritz Obermeyer:
Pathwise Derivatives Beyond the Reparameterization Trick. CoRR abs/1806.01851 (2018) - [i4]Martin Jankowiak, Theofanis Karaletsos:
Pathwise Derivatives for Multivariate Distributions. CoRR abs/1806.01856 (2018) - [i3]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. CoRR abs/1810.09538 (2018) - [i2]Martin Jankowiak:
Closed Form Variational Objectives For Bayesian Neural Networks with a Single Hidden Layer. CoRR abs/1811.00686 (2018) - 2017
- [c2]Martin Jankowiak, Manuel Gomez-Rodriguez:
Uncovering the Spatiotemporal Patterns of Collective Social Activity. SDM 2017: 822-830 - [i1]Martin Jankowiak, Manuel Gomez-Rodriguez:
Uncovering the Spatiotemporal Patterns of Collective Social Activity. CoRR abs/1701.02740 (2017) - 2014
- [c1]Kevin Reschke, Martin Jankowiak, Mihai Surdeanu, Christopher D. Manning, Daniel Jurafsky:
Event Extraction Using Distant Supervision. LREC 2014: 4527-4531
Coauthor Index
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last updated on 2024-09-13 01:41 CEST by the dblp team
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