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RESEARCH

Built on peer-reviewed
machine learning research.

BIMLOGIQ is proud that its team's research has been published at leading machine learning and AI venues. Below is a selection of peer-reviewed work by co-founder Amir Dezfouli, spanning probabilistic inference, reinforcement learning, optimisation and automated reasoning.

  • NeurIPS
  • ICML
  • AAAI
  • AISTATS
  • JMLR
  • PNAS
[ MACHINE LEARNING & AI ] 13 papers
  1. NeurIPS 2026
    Free Lunch for Pass@k? Low Cost Diverse Sampling for Diffusion Language Models

    Sean Lamont , Christian Walder , Paul Montague , Amir Dezfouli , Michael Norrish

  2. NeurIPS 2025
    3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes

    Sean Lamont , Christian Walder , Amir Dezfouli , Paul Montague , Michael Norrish

  3. AAAI 2024
    BAIT: Benchmarking (Embedding) Architectures for Interactive Theorem-Proving

    Sean Lamont , Michael Norrish , Amir Dezfouli , Christian Walder , Paul Montague

  4. NeurIPS 2023
    The Contextual Lasso: Sparse Linear Models via Deep Neural Networks

    Ryan Thompson , Amir Dezfouli , Robert Kohn

  5. ICML 2023
    Transformed Distribution Matching for Missing Value Imputation

    He Zhao , Ke Sun , Amir Dezfouli , Edwin V. Bonilla

  6. AAAI 2023
    Mixed-Variable Black-Box Optimisation Using Value Proposal Trees

    Yan Zuo , Vu Nguyen , Amir Dezfouli , David Alexander , Benjamin Ward Muir , Iadine Chades

  7. ICML 2022
    Optimizing Sequential Experimental Design with Deep Reinforcement Learning

    Tom Blau , Edwin V. Bonilla , Iadine Chades , Amir Dezfouli

  8. 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

  9. NeurIPS 2021
    TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning

    Minchao Wu , Michael Norrish , Christian Walder , Amir Dezfouli

  10. JMLR 2019
    Generic Inference in Latent Gaussian Process Models

    Edwin V. Bonilla , Karl Krauth , Amir Dezfouli

  11. ICML 2018
    Variational Network Inference: Strong and Stable with Concrete Support

    Amir Dezfouli , Edwin V. Bonilla , Richard Nock

  12. AISTATS 2017
    Gray-box Inference for Structured Gaussian Process Models

    Pietro Galliani , Amir Dezfouli , Edwin V. Bonilla , Novi Quadrianto

  13. NeurIPS 2015
    Scalable Inference for Gaussian Process Models with Black-Box Likelihoods

    Amir Dezfouli , Edwin V. Bonilla

[ ML FOR HUMAN DECISION-MAKING ] 3 papers
  1. PNAS 2020
    Adversarial Vulnerabilities of Human Decision-Making

    Amir Dezfouli , Richard Nock , Peter Dayan

  2. NeurIPS 2019
    Disentangled Behavioural Representations

    Amir Dezfouli , Hassan Ashtiani , Omar Ghattas , Richard Nock , Peter Dayan , Cheng Soon Ong

  3. NeurIPS 2018 Oral
    Integrated Accounts of Behavioral and Neuroimaging Data Using Flexible Recurrent Neural Network Models

    Amir Dezfouli , Richard Morris , Fabio Ramos , Peter Dayan , Bernard W. Balleine

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