Marton Havasi

I just joined the Data to Actionable Knowledge Lab at Harvard under Finale Doshi-Velez. My research focuses on interpretable probabilistic modelling and model uncertainty in deep learning.

Quick links: CV, Google scholar, LinkedIn


My research focuses on two connected areas. First, I am interested in Bayesian inference and Bayesian deep learning [2,3,6]. I want to use Bayesian methods to understand model uncertainty in neural networks. Model uncertainty can then be used to build robust and deployable deep learning models that do not exhibit the typical failure modes, such as poor calibration and overconfident predictions of traditionally trained networks.

Second, model uncertainty from Bayesian methods can be used to estimate the information stored within the neural network. We can use this to derive an efficient model compression algorithm, that can reduce the size of the network by a factor of 100 [4]. A similar approach can be applied to images, where we exploit uncertainty in their latent representations for effective lossless and lossy image compression [1].

I also worked on Bayesian optimization with the goal of optimizing hardware accelerator design parameters [5].


  • October 2017 - April 2021, Cambridge, UK
    PhD in Probabilistic Machine Learning, University of Cambridge
    Working on model uncertainty, robustness, model compression and image compression with José Miguel Hernández-Lobato.

  • June 2020 - September 2020, Remote
    Research Intern, Google Health
    Worked on robust and efficient ensemble models with Dustin Tran, Andrew M. Dai and Balaji Lakshminarayanan.

  • June 2019 - September 2019, Cambridge, MA
    Research Intern, Google Brain
    Worked on Bayesian neural networks with Jasper Snoek and Dustin Tran.

  • October 2016 - August 2017, Cambridge, UK
    MPhil in Machine Learning, Speech and Language Technology, University of Cambridge
    Thesis: Designing neural network hardware accelerators using deep Gaussian processes.

  • June 2016 - September 2016, London, UK
    Software Engineer Intern, Facebook
    Working on locations on Facebook.

  • October 2013 - June 2016, Cambridge, UK
    BA in Computer Science with Mathematics, University of Cambridge
    Churchill College

  • June 2015 - September 2015, Menlo Park, CA
    Software Engineer Intern, Facebook
    Working on news feed ads.

  • June 2013, Colombia
    Participant at the International Mathematical Olympiad
    Earned a Bronze Medal

  • June 2012, Italy
    Participant at the International Olympiad in Informatics
    Earned a Silver Medal


Supervised courses: Machine Learning, Discrete Mathematics, Artificial Intelligence.


  • Zoltan Molnar-Saska, Undergraduate dissertation:
    Training robust agents in cooperative multi-agent reinforcement learning.
  • Gergely Flamich, MPhil dissertation:
    Compression without quanization.
  • Tudor Paraschivescu, Undergraduate dissertation:
    Library for MIRACLE compression.


[1] Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding, NeurIPS 2020

Gergely Flamich (joint first author), Marton Havasi (joint first author), José Miguel Hernández-Lobato
We prospose an image compression algorithm based on relative entropy coding. Arxiv

[2] Training independent subnetwork for robust predictions, Under review

Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran
We prospose an algorithm for training robust and efficient ensemble models. PDF

[3] Refining the variational posterior using through iterative optimization, Under review

Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon and José Miguel Hernández-Lobato
We prospose an algorithm for improving the expressivity of mean-field variational inference in Bayesian neural networks. PDF

[4] Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters, ICLR 2019

Marton Havasi, Robert Peharz and José Miguel Hernández-Lobato
We propose a non-deterministic compression method for neural networks that samples from a variational distribution. Arxiv

[5] Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization, IEEE 2019

Kshitij Bhardwaj, Marton Havasi, Yuan Yao, David M Brooks, José Miguel Hernández Lobato, Gu-Yeon Wei PDF

[6] Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo, NeurIPS 2018

Marton Havasi, José Miguel Hernández-Lobato and Juan José Murillo Fuentes
We applied an MCMC sampling method to do inference in deep Gaussian processes. Arxiv