8 citations · 17 across the 13 of their papers we have counts for
16 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…
Calibrate-Then-Delegate: Safety Monitoring with Risk and Budget Guarantees via Model Cascades
Edoardo Pona, Milad Kazemi, Mehran Hosseini +4
Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalated to a more expensive exper…
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…
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives
Milad Kazemi, Mateo Perez, Fabio Somenzi +3
Recent advances in reinforcement learning (RL) have renewed interest in reward design for shaping agent behavior, but manually crafting reward functions is tedious and error-prone.…
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…
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…
Assume-Guarantee Reinforcement Learning
Milad Kazemi, Mateo Perez, Fabio Somenzi +3
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel. A monolithic view of such modular environ…
Causal Temporal Reasoning for Markov Decision Processes
Milad Kazemi, Nicola Paoletti
We introduce , a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP). PCFTL is th…
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems
Abolfazl Lavaei, Mateo Perez, Milad Kazemi +4
We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning…
Data-Driven Abstraction-Based Control Synthesis
Milad Kazemi, Rupak Majumdar, Mahmoud Salamati +2
This paper studies formal synthesis of controllers for continuous-space systems with unknown dynamics to satisfy requirements expressed as linear temporal logic formulas. Formal ab…
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning
Milad Kazemi, Sadegh Soudjani
This paper studies satisfaction of temporal properties on unknown stochastic processes that have continuous state spaces. We show how reinforcement learning (RL) can be applied for…