I have a background in data science and engineering with a focus on machine learning and I am currently pursuing a PhD at the University of Liège under the supervision of Professor Gilles Louppe .
I mainly work in the field of simulation-based inference, a subfield of Bayesian inference in which a simulator is leveraged to perform inference.
My current research is about designing simulation-based inference algorithms that can be reliably used for scientific purposes.
Beyond that, I have interests in approximate Bayesian inference, generative modeling, Bayesian deep learning and machine learning in general.
Publications
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability [PDF]
Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis
Advances in Neural Information Processing Systems, 2023
Bayesian inference allows expressing the uncertainty of posterior belief under a
probabilistic model given prior information and the likelihood of the evidence.
Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI). However, the existing
algorithms can yield overconfident posteriors ( Hermans et al., 2022 ) defeating the
whole purpose of credibility if the uncertainty quantification is inaccurate. We
propose to include a calibration term directly into the training objective of the
neural model in selected amortized SBI techniques. By introducing a relaxation of
the classical formulation of calibration error we enable end-to-end backpropagation.
The proposed method is not tied to any particular neural model and brings moderate computational overhead compared to the profits it introduces. It is directly
applicable to existing computational pipelines allowing reliable black-box posterior
inference. We empirically show on six benchmark problems that the proposed
method achieves competitive or better results in terms of coverage and expected
posterior density than the previously existing approaches.
Balancing Simulation-based Inference for Conservative Posteriors [PDF]
Arnaud Delaunoy*, Benjamin Kurt Miller*, Patrick Forré, Christoph Weniger, Gilles Louppe
5th Symposium on Advances in Approximate Bayesian Inference, 2023
Conservative inference is a major concern in simulation-based inference.
It has been shown that commonly used algorithms can produce overconfident posterior approximations.
Balancing has empirically proven to be an effective way to mitigate this issue.
However, its application remains limited to neural ratio estimation.
In this work, we extend balancing to any algorithm that provides a posterior density.
In particular, we introduce a balanced version of both neural posterior estimation and contrastive neural ratio estimation.
We show empirically that the balanced versions tend to produce conservative posterior approximations on a wide variety of benchmarks.
In addition, we provide an alternative interpretation of the balancing condition in terms of the χ2 divergence.
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation [PDF]
Arnaud Delaunoy*, Joeri Hermans*, François Rozet, Antoine Wehenkel, Gilles Louppe Advances in Neural Information Processing Systems, 2022
Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators. In practice,
the estimated posteriors’ computational faithfulness is, however, rarely guaranteed.
For example, Hermans et al. show that current simulation-based inference algorithms can produce posteriors that are overconfident, hence risking false inferences.
In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of
the NRE algorithm designed to produce posterior approximations that tend to be
more conservative, hence improving their reliability, while sharing the same Bayes
optimal solution. We achieve this by enforcing a balancing condition that increases
the quantified uncertainty in small simulation budget regimes while still converging
to the exact posterior as the budget increases. We provide theoretical arguments
showing that BNRE tends to produce posterior surrogates that are more conservative
than NRE’s. We evaluate BNRE on a wide variety of tasks and show that it produces
conservative posterior surrogates on all tested benchmarks and simulation budgets.
Finally, we emphasize that BNRE is straightforward to implement over NRE and
does not introduce any computational overhead.
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful [PDF]
Joeri Hermans*, Arnaud Delaunoy*, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe Transactions on Machine Learning Research, 2022
We present extensive empirical evidence showing that current Bayesian simulation-based
inference algorithms can produce computationally unfaithful posterior approximations. Our
results show that all benchmarked algorithms – (Sequential) Neural Posterior Estimation,
(Sequential) Neural Ratio Estimation, Sequential Neural Likelihood and variants of Approximate Bayesian Computation – can yield overconfident posterior approximations, which makes
them unreliable for scientific use cases and falsificationist inquiry. Failing to address this issue
may reduce the range of applicability of simulation-based inference. For this reason, we argue
that research efforts should be made towards theoretical and methodological developments of
conservative approximate inference algorithms and present research directions towards this
objective. In this regard, we show empirical evidence that ensembling posterior surrogates
provides more reliable approximations and mitigates the issue.
Computing the Bayesian posterior of a neural network is a challenging task due
to the high-dimensionality of the parameter space. Anchored ensembles approximate the posterior by training an ensemble of neural networks on anchored losses
designed for the optima to follow the Bayesian posterior. Training an ensemble,
however, becomes computationally expensive as its number of members grows
since the full training procedure is repeated for each member. In this note, we
present Sequential Anchored Ensembles (SAE), a lightweight alternative to anchored ensembles. Instead of training each member of the ensemble from scratch,
the members are trained sequentially on losses sampled with high auto-correlation,
hence enabling fast convergence of the neural networks and efficient approximation
of the Bayesian posterior. SAE outperform anchored ensembles, for a given computational budget, on some benchmarks while showing comparable performance
on the others and achieved 2nd and 3rd place in the light and extended tracks of the
NeurIPS 2021 Approximate Inference in Bayesian Deep Learning competition.
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization [PDF]
Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R Williamson, Gilles Louppe Machine Learning and the Physical Sciences Workshop, NeurIPS2020
Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms.
Because the evaluation of the likelihood function requires evaluating millions of
waveform models that link between signal shapes and the source parameters, running Markov chains until convergence is typically expensive and requires days of
computation. In this extended abstract, we provide a proof of concept that demonstrates how the latest advances in neural simulation-based inference can speed
up the inference time by up to three orders of magnitude – from days to minutes
– without impairing the performance. Our approach is based on a convolutional
neural network modeling the likelihood-to-evidence ratio and entirely amortizes the
computation of the posterior. We find that our model correctly estimates credible
intervals for the parameters of simulated gravitational waves.
Presentations
Orals
Simulation-Based Inference [slides]
One-day Symposium on statistics, data science and artificial intelligence (June 2023) [link]
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation [slides]
University of California Irvine Physics Astro/Particle-ML seminar (December 2022)
SAE: Sequential Anchored Ensembles [slides]
Approximate Inference in Bayesian Deep Learning competition, NeurIPS 2021 (December 2021)
Posters
Balancing Simulation-based Inference for Conservative Posteriors [poster]
5th Symposium on Advances in Approximate Bayesian Inference (July 2023)
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation [poster]
Advances in Neural Information Processing Systems (December 2022)
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful [poster]
Machine Learning and the Physical Sciences Workshop, NeurIPS2022 (December 2022)
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization [poster]
Machine Learning and the Physical Sciences Workshop, NeurIPS2020 (December 2020)
Videos
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful [video]
Transactions on Machine Learning Research, 2022
Teaching
INFO8006: Introduction to artificial intelligence (2020 - present) [link]
