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Amir Dezfouli
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2020 – today
- 2024
- [c13]Sean Lamont, Michael Norrish, Amir Dezfouli, Christian Walder, Paul Montague:
BAIT: Benchmarking (Embedding) Architectures for Interactive Theorem-Proving. AAAI 2024: 10607-10615 - [c12]Ben Harwood, Amir Dezfouli, Iadine Chades, Conrad Sanderson:
Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation. AI (2) 2024: 95-106 - [i11]Sean Lamont, Michael Norrish, Amir Dezfouli, Christian Walder, Paul Montague:
BAIT: Benchmarking (Embedding) Architectures for Interactive Theorem-Proving. CoRR abs/2403.03401 (2024) - [i10]Ben Harwood, Amir Dezfouli, Iadine Chades, Conrad Sanderson:
Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation. CoRR abs/2404.19284 (2024) - [i9]Sean Lamont, Christian Walder, Amir Dezfouli, Paul Montague, Michael Norrish:
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes. CoRR abs/2410.11133 (2024) - 2023
- [c11]Yan Zuo, Vu Nguyen, Amir Dezfouli, David Alexander, Benjamin Ward Muir, Iadine Chades:
Mixed-Variable Black-Box Optimisation Using Value Proposal Trees. AAAI 2023: 11506-11514 - [c10]He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. ICML 2023: 42159-42186 - [c9]Ryan Thompson, Amir Dezfouli, Robert Kohn:
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks. NeurIPS 2023 - [i8]Ryan Thompson, Amir Dezfouli, Robert Kohn:
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks. CoRR abs/2302.00878 (2023) - [i7]He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla:
Transformed Distribution Matching for Missing Value Imputation. CoRR abs/2302.10363 (2023) - [i6]Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli:
Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning. CoRR abs/2305.18435 (2023) - 2022
- [j10]Jaykumar Sheth
, Cyrus Miremadi
EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations. IEEE Syst. J. 16(1): 591-602 (2022) - [c8]Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli:
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. ICML 2022: 2107-2128 - [c7]Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli:
Neural Network Poisson Models for Behavioural and Neural Spike Train Data. ICML 2022: 10974-10996 - [i5]Tom Blau, Edwin V. Bonilla, Amir Dezfouli, Iadine Chades:
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. CoRR abs/2202.00821 (2022) - [i4]Yan Zuo, Amir Dezfouli, Iadine Chades, David Alexander, Benjamin Ward Muir:
Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals. CoRR abs/2202.04832 (2022) - 2021
- [c6]Minchao Wu, Michael Norrish, Christian Walder, Amir Dezfouli:
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning. NeurIPS 2021: 9330-9342 - [i3]Minchao Wu, Michael Norrish, Christian Walder, Amir Dezfouli:
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning. CoRR abs/2102.09756 (2021) - 2020
- [i2]Jaykumar Sheth, Cyrus Miremadi, Amir Dezfouli, Behnam Dezfouli:
EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations. CoRR abs/2006.15514 (2020)
2010 – 2019
- 2019
- [j9]Edwin V. Bonilla, Karl Krauth, Amir Dezfouli:
Generic Inference in Latent Gaussian Process Models. J. Mach. Learn. Res. 20: 117:1-117:63 (2019) - [j8]Can Eren Sezener
Optimizing the depth and the direction of prospective planning using information values. PLoS Comput. Biol. 15(3) (2019) - [j7]Amir Dezfouli
Models that learn how humans learn: The case of decision-making and its disorders. PLoS Comput. Biol. 15(6) (2019) - [j6]Payam Piray
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput. Biol. 15(6) (2019) - [j5]Amir Dezfouli, Bernard W. Balleine
Learning the structure of the world: The adaptive nature of state-space and action representations in multi-stage decision-making. PLoS Comput. Biol. 15(9) (2019) - [c5]Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. NeurIPS 2019: 2251-2260 - 2018
- [c4]Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Variational Network Inference: Strong and Stable with Concrete Support. ICML 2018: 1212-1221 - [c3]Amir Dezfouli, Richard W. Morris, Fabio T. Ramos, Peter Dayan, Bernard W. Balleine:
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. NeurIPS 2018: 4233-4242 - 2017
- [c2]Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla, Novi Quadrianto:
Gray-box Inference for Structured Gaussian Process Models. AISTATS 2017: 353-361 - [i1]Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Semi-parametric Network Structure Discovery Models. CoRR abs/1702.08530 (2017) - 2015
- [b1]Amir Dezfouli:
Hierarchical models of goal-directed and automatic actions. University of Sydney, Australia, 2015 - [c1]Amir Dezfouli, Edwin V. Bonilla:
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods. NIPS 2015: 1414-1422 - 2013
- [j4]Amir Dezfouli, Bernard W. Balleine
Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized. PLoS Comput. Biol. 9(12) (2013) - 2011
- [j3]Mohammad Mehdi Keramati, Amir Dezfouli, Payam Piray
Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes. PLoS Comput. Biol. 7(5) (2011) - 2010
- [j2]Payam Piray
Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-Like Behavior: A Computational Approach. Neural Comput. 22(9): 2334-2368 (2010)
2000 – 2009
- 2009
- [j1]Amir Dezfouli, Payam Piray
A Neurocomputational Model for Cocaine Addiction. Neural Comput. 21(10): 2869-2893 (2009)
Coauthor Index
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