1 citations · 1 across the 10 of their papers we have counts for
10 papers
From Bayes' Rule to Bayes Rules: Optimal Information Processing and Axiomatic Foundations Beyond Probability
Jeremie Houssineau, Badr-Eddine Chérief-Abdellatif
This paper develops principled updating rules for possibilistic inference, where uncertainty about a fixed parameter is represented by a possibility function, the maxitive analogue…
Possibilistic Predictive Uncertainty for Deep Learning
Yao Ni, Jeremie Houssineau, Yew-Soon Ong +1
Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling. Existin…
Action-Free Offline-to-Online RL via Discretised State Policies
Natinael Solomon Neggatu, Jeremie Houssineau, Giovanni Montana
Most existing offline RL methods presume the availability of action labels within the dataset, but in many practical scenarios, actions may be missing due to privacy, storage, or s…
Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
Jasraj Singh, Shelvia Wongso, Jeremie Houssineau +1
Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formulation depends on expectations and dive…
Possibilistic Instrumental Variable Regression with Potentially Invalid Instruments
Gregor Steiner, Jeremie Houssineau, Mark F. J. Steel
Instrumental variable regression is a common approach for causal inference in the presence of unobserved confounding. However, identifying valid instruments is often difficult in p…
Evaluation-Time Policy Switching for Offline Reinforcement Learning
Natinael Solomon Neggatu, Jeremie Houssineau, Giovanni Montana
Offline reinforcement learning (RL) looks at learning how to optimally solve tasks using a fixed dataset of interactions from the environment. Many off-policy algorithms developed…
Investigating Relational State Abstraction in Collaborative MARL
Sharlin Utke, Jeremie Houssineau, Giovanni Montana
This paper explores the impact of relational state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is…
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty
Jake Thomas, Jeremie Houssineau
A popular strategy for active learning is to specifically target a reduction in epistemic uncertainty, since aleatoric uncertainty is often considered as being intrinsic to the sys…
Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty
Chatchuea Kimchaiwong, Jeremie Houssineau, Adam M. Johansen
The problem of incorporating information from observations received serially in time is widespread in the field of uncertainty quantification. Within a probabilistic framework, suc…
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
Ting Zhu, Yue Jin, Jeremie Houssineau +1
In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favo…