3 citations · 6 across the 8 of their papers we have counts for
11 papers
Robust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties
Jessica Lally, Milad Kazemi, Nicola Paoletti
Counterfactual reasoning (asking what would have happened, had a decision been different) is a powerful tool for identifying causes of failure and explaining the behaviour of AI sy…
Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
Jessica Lally, Milad Kazemi, Nicola Paoletti +2
Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables. However, Markov D…
Physics-Informed Neural Operators for Cardiac Electrophysiology
Hannah Lydon, Milad Kazemi, Martin Bishop +1
Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology (EP) modelling, remains a significant modelling challenge. Traditional numerical…
CONFEX: Uncertainty-Aware Counterfactual Explanations with Conformal Guarantees
Aman Bilkhoo, Mehran Hosseini, Milad Kazemi +1
Counterfactual explanations (CFXs) provide human-understandable justifications for model predictions, enabling actionable recourse and enhancing interpretability. To be reliable, C…
Abstract Counterfactuals for Language Model Agents
Edoardo Pona, Milad Kazemi, Yali Du +2
Counterfactual inference is a powerful tool for analysing and evaluating autonomous agents, but its application to language model (LM) agents remains challenging. Existing work on…
SimICD: A Closed-Loop Simulation Framework For ICD Therapy
Hannah Lydon, Milad Kazemi, Martin Bishop +1
Virtual studies of ICD behaviour are crucial for testing device functionality in a controlled environment prior to clinical application. Although previous works have shown the viab…
Robust Counterfactual Inference in Markov Decision Processes
Jessica Lally, Milad Kazemi, Nicola Paoletti
This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make c…
Verifiably Robust Conformal Prediction
Linus Jeary, Tom Kuipers, Mehran Hosseini +1
Conformal Prediction (CP) is a popular uncertainty quantification method that provides distribution-free, statistically valid prediction sets, assuming that training and test data…
Conformal Off-Policy Prediction for Multi-Agent Systems
Tom Kuipers, Renukanandan Tumu, Shuo Yang +3
Off-Policy Prediction (OPP), i.e., predicting the outcomes of a target policy using only data collected under a nominal (behavioural) policy, is a paramount problem in data-driven…
Counterfactual Influence in Markov Decision Processes
Milad Kazemi, Jessica Lally, Ekaterina Tishchenko +2
Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs). Given an MDP path , this kind of inference allows us to…
Synthesizing Stealthy Reprogramming Attacks on Cardiac Devices
Nicola Paoletti, Zhihao Jiang, Md Ariful Islam +5
An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmia and their treatment through the delivery of elect…