Published in arXiv, 2025
Presents NeuralSurv, the first deep survival model integrating Bayesian uncertainty quantification, using a two-stage data-augmentation framework.
Recommended citation: Monod, M., Micheli, A., & Bhatt, S. (2025). “NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification.” arXiv.
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Published in Finance and Stochastics, 2025
Models the strategic interaction between institutional and high-frequency traders in a multiperiod stochastic Stackelberg game.
Recommended citation: Cont, R., Micheli, A., & Neuman, E. (2025). “Fast and slow optimal trading with exogenous information.” Finance and Stochastics, 29, 553–607.
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Published in arXiv, 2025
Extends diffusion models to solve inverse problems under exponential family likelihoods via the ‘evidence trick’, with applications to malaria prevalence inference.
Recommended citation: Micheli, A., Monod, M., & Bhatt, S. (2025). “Diffusion Models for Inverse Problems in the Exponential Family.” arXiv.
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Published in arXiv, 2024
Introduces an actor-critic algorithm (DDPG) for learning non-Markovian optimal execution strategies under transient price impact.
Recommended citation: Micheli, A., & Monod, M. (2024). “Deep Reinforcement Learning for Online Optimal Execution Strategies.” arXiv.
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Published in Mathematical Finance, 2023
Examines a multiplayer stochastic differential game where agents interact through joint price impact in trading, proving existence of a closed-loop Nash equilibrium and its properties.
Published in JHEP, 2020
Measuring the Higgs trilinear self-coupling λₕₕₕ at the HL-LHC using di-Higgs (hh → 4b) events. Application of deep learning to suppress multijet background and evaluate boosted jet strategies.
Recommended citation: Amacker, J., Balunas, W., Beresford, L., Bortoletto, D., Frost, J., Issever, C., Liu, J., McKee, J., Micheli, A., Paredes Saenz, S., Spannowsky, M. & Stanislaus, B. (2020). “Higgs self-coupling measurements using deep learning in the \\(b\\bar{b}b\\bar{b}\\) final state.” JHEP, 2020(12), 115.
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