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Author Information
Yang Song (Stanford University)
Conor Durkan (University of Edinburgh)
Iain Murray (University of Edinburgh)
Iain Murray is a SICSA Lecturer in Machine Learning at the University of Edinburgh. Iain was introduced to machine learning by David MacKay and Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His thesis on Monte Carlo methods received an honourable mention for the ISBA Savage Award. He was a commonwealth fellow in Machine Learning at the University of Toronto, before moving to Edinburgh in 2010. Iain's research interests include building flexible probabilistic models of data, and probabilistic inference from indirect and uncertain observations. Iain is passionate about teaching. He has lectured at several Summer schools, is listed in the top 15 authors on videolectures.net, and was awarded the EUSA Van Heyningen Award for Teaching in Science and Engineering in 2015.
Stefano Ermon (Stanford)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Poster: Maximum Likelihood Training of Score-Based Diffusion Models »
Thu. Dec 9th 12:30 -- 02:00 AM Room
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2017 : Generative Adversarial Imitation Learning, Stefano Ermon, Stanford »
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2017 : Panel session »
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2017 : Stefano Ermon (Stanford): Measuring Progress Towards Sustainable Development Goals with Machine Learning »
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2017 : Invited talk 3: Learning priors, likelihoods, or posteriors »
Iain Murray -
2017 : Poster Spotlights I »
Taesik Na · Yang Song · Aman Sinha · Richard Shin · Qiuyuan Huang · Nina Narodytska · Matt Staib · Kexin Pei · Fnu Suya · Amirata Ghorbani · Jacob Buckman · Matthias Hein · Huan Zhang · Yanjun Qi · Yuan Tian · Min Du · Dimitris Tsipras -
2017 : Invited talk: Iain Murray (TBA) »
Iain Murray -
2017 Oral: Masked Autoregressive Flow for Density Estimation »
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2017 Poster: A-NICE-MC: Adversarial Training for MCMC »
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2017 Poster: InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations »
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2017 Poster: Neural Variational Inference and Learning in Undirected Graphical Models »
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2016 Poster: Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation »
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2016 Poster: Solving Marginal MAP Problems with NP Oracles and Parity Constraints »
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2016 Poster: Variational Bayes on Monte Carlo Steroids »
Aditya Grover · Stefano Ermon -
2016 Poster: Adaptive Concentration Inequalities for Sequential Decision Problems »
Shengjia Zhao · Enze Zhou · Ashish Sabharwal · Stefano Ermon -
2015 Tutorial: Monte Carlo Inference Methods »
Iain Murray -
2013 Poster: RNADE: The real-valued neural autoregressive density-estimator »
Benigno Uria · Iain Murray · Hugo Larochelle -
2013 Poster: Embed and Project: Discrete Sampling with Universal Hashing »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2012 Poster: Density Propagation and Improved Bounds on the Partition Function »
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2011 Poster: Accelerated Adaptive Markov Chain for Partition Function Computation »
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2011 Spotlight: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Poster: How biased are maximum entropy models? »
Jakob H Macke · Iain Murray · Peter E Latham -
2010 Workshop: Monte Carlo Methods for Bayesian Inference in Modern Day Applications »
Ryan Adams · Mark A Girolami · Iain Murray -
2010 Oral: Slice sampling covariance hyperparameters of latent Gaussian models »
Iain Murray · Ryan Adams -
2010 Poster: Slice sampling covariance hyperparameters of latent Gaussian models »
Iain Murray · Ryan Adams -
2010 Session: Spotlights Session 5 »
Iain Murray -
2010 Session: Oral Session 5 »
Iain Murray -
2008 Poster: Comparing model predictions of response bias and variance in cue combination »
Rama Natarajan · Iain Murray · Ladan Shams · Richard Zemel -
2008 Poster: The Gaussian Process Density Sampler »
Ryan Adams · Iain Murray · David MacKay -
2008 Spotlight: The Gaussian Process Density Sampler »
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Iain Murray · Russ Salakhutdinov -
2008 Spotlight: Evaluating probabilities under high-dimensional latent variable models »
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