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ORCIDMichael U. Gutmann
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2020 – today
- 2024
[j11]Vaidotas Simkus, Michael U. Gutmann:
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. Trans. Mach. Learn. Res. 2024 (2024)- [i26]Vaidotas Simkus, Michael U. Gutmann:
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. CoRR abs/2403.03069 (2024) - 2023
- [j10]Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann:
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data. J. Mach. Learn. Res. 24: 196:1-196:72 (2023) - [j9]Afonso Eduardo, Michael U. Gutmann:
Bayesian Optimization with Informative Covariance. Trans. Mach. Learn. Res. 2023 (2023) - [j8]Vaidotas Simkus, Michael U. Gutmann:
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. Trans. Mach. Learn. Res. 2023 (2023) - [j7]Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann:
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. Trans. Mach. Learn. Res. 2023 (2023) - [c16]Yanzhi Chen, Michael U. Gutmann, Adrian Weller:
Is Learning Summary Statistics Necessary for Likelihood-free Inference? ICML 2023: 4529-4544 - [i25]Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann:
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. CoRR abs/2305.00869 (2023) - [i24]Simon Valentin, Steven Kleinegesse, Neil R. Bramley
, Peggy Seriès, Michael U. Gutmann, Christopher G. Lucas:
Designing Optimal Behavioral Experiments Using Machine Learning. CoRR abs/2305.07721 (2023) - [i23]Vaidotas Simkus, Michael U. Gutmann:
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. CoRR abs/2308.09078 (2023) - 2022
- [j6]Bo Wang, Andy Law, Tim Regan, Nicholas Parkinson, Joby Cole, Clark D. Russell, David H. Dockrell, Michael U. Gutmann, J. Kenneth Baillie:
Systematic comparison of ranking aggregation methods for gene lists in experimental results. Bioinform. 38(21): 4927-4933 (2022) - [j5]Benjamin Rhodes, Michael U. Gutmann:
Enhanced gradient-based MCMC in discrete spaces. Trans. Mach. Learn. Res. 2022 (2022) - [i22]Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes:
Statistical applications of contrastive learning. CoRR abs/2204.13999 (2022) - [i21]Michael U. Gutmann:
Pen and Paper Exercises in Machine Learning. CoRR abs/2206.13446 (2022) - [i20]Benjamin Rhodes, Michael U. Gutmann:
Enhanced gradient-based MCMC in discrete spaces. CoRR abs/2208.00040 (2022) - [i19]Afonso Eduardo, Michael U. Gutmann:
Bayesian Optimization with Informative Covariance. CoRR abs/2208.02704 (2022) - 2021
- [c15]Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Chris Lucas:
Bayesian Experimental Design for Intractable Models of Cognition. CogSci 2021 - [c14]Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu:
Neural Approximate Sufficient Statistics for Implicit Models. ICLR 2021 - [c13]Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth:
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. NeurIPS 2021: 25785-25798 - [i18]Steven Kleinegesse, Michael U. Gutmann:
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds. CoRR abs/2105.04379 (2021) - [i17]Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas:
Bayesian Optimal Experimental Design for Simulator Models of Cognition. CoRR abs/2110.15632 (2021) - [i16]Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth:
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR abs/2111.02329 (2021) - [i15]Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann:
Variational Gibbs inference for statistical model estimation from incomplete data. CoRR abs/2111.13180 (2021) - 2020
- [c12]Borislav Ikonomov, Michael U. Gutmann:
Robust Optimisation Monte Carlo. AISTATS 2020: 2819-2829 - [c11]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Generative Ratio Matching Networks. ICLR 2020 - [c10]Steven Kleinegesse, Michael U. Gutmann:
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. ICML 2020: 5316-5326 - [c9]Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr:
Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions. IROS 2020: 5351-5357 - [c8]Benjamin Rhodes, Kai Xu, Michael U. Gutmann:
Telescoping Density-Ratio Estimation. NeurIPS 2020 - [i14]Steven Kleinegesse, Michael U. Gutmann:
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. CoRR abs/2002.08129 (2020) - [i13]Steven Kleinegesse, Christopher C. Drovandi, Michael U. Gutmann:
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information. CoRR abs/2003.09379 (2020) - [i12]Benjamin Rhodes, Kai Xu, Michael U. Gutmann:
Telescoping Density-Ratio Estimation. CoRR abs/2006.12204 (2020) - [i11]Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu:
Neural Approximate Sufficient Statistics for Implicit Models. CoRR abs/2010.10079 (2020)
2010 – 2019
- 2019
- [c7]Steven Kleinegesse, Michael U. Gutmann:
Efficient Bayesian Experimental Design for Implicit Models. AISTATS 2019: 476-485 - [c6]Yanzhi Chen, Michael U. Gutmann:
Adaptive Gaussian Copula ABC. AISTATS 2019: 1584-1592 - [c5]Benjamin Rhodes, Michael U. Gutmann:
Variational Noise-Contrastive Estimation. AISTATS 2019: 2741-2750 - [i10]Yanzhi Chen, Michael U. Gutmann:
Adaptive Gaussian Copula ABC. CoRR abs/1902.10704 (2019) - [i9]Borislav Ikonomov, Michael U. Gutmann:
Robust Optimisation Monte Carlo. CoRR abs/1904.00670 (2019) - [i8]Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr:
To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions. CoRR abs/1904.02431 (2019) - [i7]Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen:
Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR abs/1905.01252 (2019) - 2018
- [j4]Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski:
ELFI: Engine for Likelihood-Free Inference. J. Mach. Learn. Res. 19: 16:1-16:7 (2018) - [j3]Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander:
Likelihood-free inference via classification. Stat. Comput. 28(2): 411-425 (2018) - [c4]Ciwan Ceylan, Michael U. Gutmann:
Conditional Noise-Contrastive Estimation of Unnormalised Models. ICML 2018: 725-733 - [i6]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR abs/1806.00101 (2018) - [i5]Ciwan Ceylan, Michael U. Gutmann:
Conditional Noise-Contrastive Estimation of Unnormalised Models. CoRR abs/1806.03664 (2018) - [i4]Benjamin Rhodes, Michael U. Gutmann:
Variational Noise-Contrastive Estimation. CoRR abs/1810.08010 (2018) - [i3]Traiko Dinev, Michael U. Gutmann:
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE). CoRR abs/1810.09899 (2018) - [i2]Steven Kleinegesse, Michael U. Gutmann:
Efficient Bayesian Experimental Design for Implicit Models. CoRR abs/1810.09912 (2018) - 2017
- [c3]Tatiana Lopez-Guevara, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr:
Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation. CoRL 2017: 77-86 - [c2]Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton:
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS 2017: 3308-3318 - [i1]Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski:
ELFI: Engine for Likelihood Free Inference. CoRR abs/1708.00707 (2017) - 2016
- [j2]Michael U. Gutmann, Jukka Corander:
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models. J. Mach. Learn. Res. 17: 125:1-125:47 (2016) - 2014
- [j1]Song Liu, John A. Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi Sugiyama:
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. Neural Comput. 26(6): 1169-1197 (2014) - 2013
- [c1]Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama:
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. ECML/PKDD (2) 2013: 596-611
Coauthor Index
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