Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms. - Iain Murray 001; Zoubin Ghahramani (UAI, 2004)

A Pragmatic Bayesian Approach to Predictive Uncertainty. - Iain Murray 001; Edward Lloyd Snelson (MLCW, 2005)

Nested sampling for Potts models. - Iain Murray 001; David J. C. MacKay; Zoubin Ghahramani; John Skilling (NIPS, 2005)

MCMC for Doubly-intractable Distributions. - Iain Murray 001; Zoubin Ghahramani; David J. C. MacKay (UAI, 2006)

On the quantitative analysis of deep belief networks. - Ruslan Salakhutdinov; Iain Murray 001 (ICML, 2008)

The Gaussian Process Density Sampler. - Ryan Prescott Adams; Iain Murray 001; David J. C. MacKay (NIPS, 2008)

Evaluating probabilities under high-dimensional latent variable models. - Iain Murray 001; Ruslan Salakhutdinov (NIPS, 2008)

Characterizing response behavior in multisensory perception with conflicting cues. - Rama Natarajan; Iain Murray 001; Ladan Shams; Richard S. Zemel (NIPS, 2008)

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. - Ryan Prescott Adams; Iain Murray 001; David J. C. MacKay (ICML, 2009)

Evaluation methods for topic models. - Hanna M. Wallach; Iain Murray 001; Ruslan Salakhutdinov; David M. Mimno (ICML, 2009)

Slice sampling covariance hyperparameters of latent Gaussian models. - Iain Murray 001; Ryan Prescott Adams (NIPS, 2010)

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. - Ryan Prescott Adams; George E. Dahl; Iain Murray 001 (UAI, 2010)

Elliptical slice sampling. - Iain Murray 001; Ryan Prescott Adams; David J. C. MacKay (AISTATS, 2010)

How biased are maximum entropy models? - Jakob H. Macke; Iain Murray 001; Peter E. Latham (NIPS, 2011)

The Neural Autoregressive Distribution Estimator. - Hugo Larochelle; Iain Murray 001 (AISTATS, 2011)

Deep Architectures for Articulatory Inversion. - Benigno Uria; Iain Murray 001; Steve Renals; Korin Richmond (INTERSPEECH, 2012)

A Composable Strategy for Shredded Document Reconstruction. - Razvan Ranca; Iain Murray 001 (CAIP (2), 2013)

RNADE: The real-valued neural autoregressive density-estimator. - Benigno Uria; Iain Murray 001; Hugo Larochelle (NIPS, 2013)

NADE: The real-valued neural autoregressive density-estimator. - Benigno Uria; Iain Murray 001; Hugo Larochelle (CoRR, 2013)

Estimation Bias in Maximum Entropy Models. - Jakob H. Macke; Iain Murray 001; Peter E. Latham (Entropy, 2013)

A framework for evaluating approximation methods for Gaussian process regression. - Krzysztof Chalupka; Christopher K. I. Williams; Iain Murray 001 (J. Mach. Learn. Res., 2013)

Attention as Reward-Driven Optimization of Sensory Processing. - Matthew Chalk; Iain Murray 001; Peggy Seriès (Neural Comput., 2013)

A Deep and Tractable Density Estimator. - Benigno Uria; Iain Murray 001; Hugo Larochelle (ICML, 2014)

Parallel MCMC with generalized elliptical slice sampling. - Robert Nishihara; Iain Murray 001; Ryan P. Adams (J. Mach. Learn. Res., 2014)

Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE. - Benigno Uria; Iain Murray 001; Steve Renals; Cassia Valentini-Botinhao; John Bridle (ICASSP, 2015)

MADE: Masked Autoencoder for Distribution Estimation. - Mathieu Germain; Karol Gregor; Iain Murray 001; Hugo Larochelle (ICML, 2015)

Pseudo-Marginal Slice Sampling. - Iain Murray 001; Matthew M. Graham (AISTATS, 2016)

Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation. - George Papamakarios; Iain Murray 001 (NIPS, 2016)

Differentiation of the Cholesky decomposition. - Iain Murray 001 (CoRR, 2016)

Neural Autoregressive Distribution Estimation. - Benigno Uria; Marc-Alexandre Côté; Karol Gregor; Iain Murray 001; Hugo Larochelle (J. Mach. Learn. Res., 2016)

Markov Chain Truncation for Doubly-Intractable Inference. - Colin Wei; Iain Murray 001 (AISTATS, 2017)

Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media. - Philippa Shoemark; Debnil Sur; Luke Shrimpton; Iain Murray 001; Sharon Goldwater (EACL (1), 2017)

Multiplicative LSTM for sequence modelling. - Ben Krause; Iain Murray 001; Steve Renals; Liang Lu 001 (ICLR (Workshop), 2017)

Masked Autoregressive Flow for Density Estimation. - George Papamakarios; Iain Murray 001; Theo Pavlakou (NIPS, 2017)

Dynamic Evaluation of Neural Sequence Models. - Ben Krause; Emmanuel Kahembwe; Iain Murray 001; Steve Renals (ICML, 2018)

Sequential Neural Methods for Likelihood-free Inference. - Conor Durkan; George Papamakarios; Iain Murray 001 (CoRR, 2018)

Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting. - Artur Bekasov; Iain Murray 001 (CoRR, 2018)

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows. - George Papamakarios; David C. Sterratt; Iain Murray 001 (AISTATS, 2019)

Mode Normalization. - Lucas Deecke; Iain Murray 001; Hakan Bilen (ICLR (Poster), 2019)

BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning. - Asa Cooper Stickland; Iain Murray 001 (ICML, 2019)

Neural Spline Flows. - Conor Durkan; Artur Bekasov; Iain Murray 001; George Papamakarios (NeurIPS, 2019)

Dynamic Evaluation of Transformer Language Models. - Ben Krause; Emmanuel Kahembwe; Iain Murray 001; Steve Renals (CoRR, 2019)

Cubic-Spline Flows. - Conor Durkan; Artur Bekasov; Iain Murray 001; George Papamakarios (CoRR, 2019)

Scalable Extreme Deconvolution. - James A. Ritchie; Iain Murray 001 (CoRR, 2019)

On Contrastive Learning for Likelihood-free Inference. - Conor Durkan; Iain Murray 001; George Papamakarios (ICML, 2020)

Ordering Dimensions with Nested Dropout Normalizing Flows. - Artur Bekasov; Iain Murray 001 (CoRR, 2020)

Density Deconvolution with Normalizing Flows. - Tim Dockhorn; James A. Ritchie; Yaoliang Yu; Iain Murray 001 (CoRR, 2020)

Diverse Ensembles Improve Calibration. - Asa Cooper Stickland; Iain Murray 001 (CoRR, 2020)

CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting. - Chaoyun Zhang; Marco Fiore 001; Iain Murray 001; Paul Patras (AAAI, 2021)

Maximum Likelihood Training of Score-Based Diffusion Models. - Yang Song 011; Conor Durkan; Iain Murray 001; Stefano Ermon (NeurIPS, 2021)

Lossless compression with state space models using bits back coding. - James Townsend; Iain Murray 001 (CoRR, 2021)

Don't recommend the obvious: estimate probability ratios. - Roberto Pellegrini; Wenjie Zhao; Iain Murray 001 (RecSys, 2022)

Nudging Neural Click Prediction Models to Pay Attention to Position. - Efi Karra Taniskidou; Wenjie Zhao; Iain Murray 001; Roberto Pellegrini (CIKM, 2023)