Michael U. Gutmann
- Pen and Paper Exercises in Machine Learning
2022/06/27 by Michael U. Gutmann, Gutmann, Michael U. · 25 voices
Computer Science · #Neural Networks and Applications
- VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
2017/05/22 by Akash Srivastava, Srivastava, Akash, Lazar Valkov +7 · 16 citations
Computer Science · Physics and Astronomy · #Generative Adversarial Networks and Image Synthesis #Model Reduction and Neural Networks #Computational Physics and Python Applications
- Telescoping Density-Ratio Estimation
2020/06/22 by Benjamin Rhodes, Rhodes, Benjamin, Kai Xu +3 · 20 citations
Computer Science · Physics and Astronomy · #Generative Adversarial Networks and Image Synthesis #Neural Networks and Applications #Model Reduction and Neural Networks
- Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
2015/01/14 by Michael U. Gutmann, Gutmann, Michael U., Jukka Corander +1 · 7 citations
Computer Science · Decision Sciences · Physics and Astronomy · #Gaussian Processes and Bayesian Inference #Simulation Techniques and Applications #Model Reduction and Neural Networks
- Fundamentals and Recent Developments in Approximate Bayesian Computation
2016/10/19 by Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta +2 · 7 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods
- Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
2020/02/19 by Steven Kleinegesse, Kleinegesse, Steven, Michael U. Gutmann +1 · 6 citations
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Gaussian Processes and Bayesian Inference #Optimal Experimental Design Methods
- Efficient Bayesian Experimental Design for Implicit Models
2018/10/23 by Steven Kleinegesse, Michael U. Gutmann, Kleinegesse, Steven +1 · 5 citations
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Optimal Experimental Design Methods
- Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
2021/05/10 by Steven Kleinegesse, Michael U. Gutmann, Kleinegesse, Steven +1 · 5 citations
Computer Science · Decision Sciences · #62K05 #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Methodology (stat.ME) #Optimal Experimental Design Methods
- Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
2021/11/03 by Desi R. Ivanova, Adam S. Foster, Ivanova, Desi R. +8 · 6 citations
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimal Experimental Design Methods #Probabilistic and Robust Engineering Design
- Robust Optimisation Monte Carlo
2016/11/29 by Borislav Ikonomov, Ikonomov, Borislav, Michael U. Gutmann +1 · 2 citations
Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Gaussian Processes and Bayesian Inference #Machine Learning and Algorithms
- Sequential Bayesian Experimental Design for Implicit Models via Mutual\n Information
2020/03/20 by Steven Kleinegesse, Kleinegesse, Steven, Christopher Drovandi +3 · 2 citations
Computer Science · Decision Sciences · #62K05 #62L05 #Advanced Multi-Objective Optimization Algorithms #Advanced Statistical Process Monitoring #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Optimal Experimental Design Methods
- Simulation-based Bayesian inference under model misspecification
2025/03/16 by Ryan P. Kelly, Kelly, Ryan P., David J. Warne +9 · 1 voice · 4 citations
#stat.ME #cs.LG #stat.CO #stat.ML
- Enhanced gradient-based MCMC in discrete spaces
2022/07/29 by Benjamin Rhodes, Michael U. Gutmann, Rhodes, Benjamin +1 · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #NMR spectroscopy and applications
- Bregman divergence as general framework to estimate unnormalized statistical models
2012/02/14 by Michael U. Gutmann, Gutmann, Michael, Jun-ichiro Hirayama +1 · 1 citation
Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Mechanics and Entropy #Statistical Methods and Inference
- Likelihood-free inference via classification
2014/07/18 by Michael U. Gutmann, Gutmann, Michael U., Ritabrata Dutta +5 · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Methodology (stat.ME)
- Adaptive Gaussian Copula ABC
2019/02/27 by Yanzhi Chen, Chen, Yanzhi, Michael U. Gutmann +1 · 2 citations
Computer Science · #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning and Algorithms #Methodology (stat.ME) #Target Tracking and Data Fusion in Sensor Networks