A Neurocomputational Model for Cocaine Addiction. - Amir Dezfouli; Payam Piray; Mohammad Mahdi Keramati; Hamed Ekhtiari; Caro Lucas; Azarakhsh Mokri (Neural Comput., 2009)

Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-Like Behavior: A Computational Approach. - Payam Piray; Mohammad Mahdi Keramati; Amir Dezfouli; Caro Lucas; Azarakhsh Mokri (Neural Comput., 2010)

Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes. - Mohammad Mehdi Keramati; Amir Dezfouli; Payam Piray (PLoS Comput. Biol., 2011)

Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized. - Amir Dezfouli; Bernard W. Balleine (PLoS Comput. Biol., 2013)

Scalable Inference for Gaussian Process Models with Black-Box Likelihoods. - Amir Dezfouli; Edwin V. Bonilla (NIPS, 2015)

Hierarchical models of goal-directed and automatic actions. - Amir Dezfouli (2015)

Gray-box Inference for Structured Gaussian Process Models. - Pietro Galliani; Amir Dezfouli; Edwin V. Bonilla; Novi Quadrianto (AISTATS, 2017)

Semi-parametric Network Structure Discovery Models. - Amir Dezfouli; Edwin V. Bonilla; Richard Nock (CoRR, 2017)

Variational Network Inference: Strong and Stable with Concrete Support. - Amir Dezfouli; Edwin V. Bonilla; Richard Nock (ICML, 2018)

Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. - Amir Dezfouli; Richard W. Morris; Fabio T. Ramos 001; Peter Dayan; Bernard W. Balleine (NeurIPS, 2018)

Disentangled behavioural representations. - Amir Dezfouli; Hassan Ashtiani; Omar Ghattas; Richard Nock; Peter Dayan; Cheng Soon Ong (NeurIPS, 2019)

Generic Inference in Latent Gaussian Process Models. - Edwin V. Bonilla; Karl Krauth; Amir Dezfouli (J. Mach. Learn. Res., 2019)

Learning the structure of the world: The adaptive nature of state-space and action representations in multi-stage decision-making. - Amir Dezfouli; Bernard W. Balleine (PLoS Comput. Biol., 2019)

Models that learn how humans learn: The case of decision-making and its disorders. - Amir Dezfouli; Kristi Griffiths; Fabio Ramos 001; Peter Dayan; Bernard W. Balleine (PLoS Comput. Biol., 2019)

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. - Payam Piray; Amir Dezfouli; Tom Heskes; Michael J. Frank; Nathaniel D. Daw (PLoS Comput. Biol., 2019)

Optimizing the depth and the direction of prospective planning using information values. - Can Eren Sezener; Amir Dezfouli; Mohammad Mehdi Keramati (PLoS Comput. Biol., 2019)

TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning. - Minchao Wu; Michael Norrish; Christian Walder; Amir Dezfouli (NeurIPS, 2021)

Optimizing Sequential Experimental Design with Deep Reinforcement Learning. - Tom Blau; Edwin V. Bonilla; Iadine Chades; Amir Dezfouli (ICML, 2022)

Neural Network Poisson Models for Behavioural and Neural Spike Train Data. - Moein Khajehnejad; Forough Habibollahi; Richard Nock; Ehsan Arabzadeh; Peter Dayan; Amir Dezfouli (ICML, 2022)

Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals. - Yan Zuo; Amir Dezfouli; Iadine Chades; David Alexander; Benjamin Ward Muir (CoRR, 2022)

EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations. - Jaykumar Sheth; Cyrus Miremadi; Amir Dezfouli; Behnam Dezfouli (IEEE Syst. J., 2022)

Mixed-Variable Black-Box Optimisation Using Value Proposal Trees. - Yan Zuo; Vu Nguyen; Amir Dezfouli; David Alexander; Benjamin Ward Muir; Iadine Chades (AAAI, 2023)

Transformed Distribution Matching for Missing Value Imputation. - He Zhao 001; Ke Sun 001; Amir Dezfouli; Edwin V. Bonilla (ICML, 2023)

The Contextual Lasso: Sparse Linear Models via Deep Neural Networks. - Ryan Thompson; Amir Dezfouli; Robert Kohn (NeurIPS, 2023)

Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning. - Tom Blau; Edwin V. Bonilla; Iadine Chades; Amir Dezfouli (CoRR, 2023)

BAIT: Benchmarking (Embedding) Architectures for Interactive Theorem-Proving. - Sean Lamont; Michael Norrish; Amir Dezfouli; Christian Walder; Paul Montague (AAAI, 2024)

Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation. - Ben Harwood; Amir Dezfouli; Iadine Chades; Conrad Sanderson (AI (2), 2024)

3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes. - Sean Lamont; Christian Walder; Amir Dezfouli; Paul Montague; Michael Norrish (CoRR, 2024)