Francesco Leofante
Assistant Professor in Computing
Leads the CRUMBS lab and co-directs the Centre for Explainable AI, combining learning and reasoning for robust counterfactual explanations and safe decision making.
Francesco Leofante's lab at Imperial works on trustworthy & safe ML, reinforcement learning and human-AI collaboration & trust. 29 papers since 2023, 15 at top venues, mostly IJCAI and KR. 4 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 52%
- PhD students
- 4
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 5 · collecting
- 8 papers2025 · T1 4
- 7 papers2024 · T1 4
- 8 papers2023 · T1 2
T1 venuesOtherVenues: IJCAI, KR, AAAI, AAMAS, Artificial Intelligence
Research focuspaper titles that use each topic's words
- explanations15 papers
- Trustworthy & safe ML3 papers
- Human-AI collaboration & trust2 papers
- Reinforcement learning1 paper
- centre0 papers
Paper list
From the lab homepage.
20266 papers
- IJCAI (T1)Attribution-based Explanations for Markov Decision Processes
- NeurIPS (T1)Efficient bias mitigation in T2I diffusion models using Concept Graphs
- Proc. of SAIVEnhancing the Robustness of Counterfactual Explanations via Adversarial Training
- Artificial Intelligence (T1)Probabilistically Robust Counterfactual Explanations under Model Changes
- ICLR (T1)Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
- IJCAI (T1)What if...Counterfactual Explanations Were to Be Deployed?
20258 papers
- IJCAI (T1)Counterfactual Explanations under Model Multiplicity and their Use in Computational Argumentation
- KRCounterfactual Scenarios for Automated Planning
- IJCAI (T1)Counterfactual Strategies for Markov Decision Processes
- AI MagazineExplainable AI, Energy and Critical Infrastructure Systems
- AI EthicsExplainable AI: definition and attributes of a good explanation for health AI
- NeurIPS (T1)Representation Consistency for Accurate and Coherent LLM Answer Aggregation
- SpringerRobust Explainable AI
- IJCAI (T1)RobustX: Robust Counterfactual Explanations Made Easy
20247 papers
- KRContestable AI needs Computational Argumentation
- Proc. of AIFin@ECAIExplaining Deep Time Series Models in Finance: Are We There Yet?
- Artificial Intelligence (T1)Interval Abstractions for Robust Counterfactual Explanations
- AAAI (T1)Promoting Counterfactual Robustness through Diversity
- AAMAS (T1)Recourse under Model Multiplicity via Argumentative Ensembling
- Proc. of ECAIRigorous Probabilistic Guarantees for Robust Counterfactual Explanations
- IJCAI (T1)Robust Counterfactual Explanations in Machine Learning: A Survey
20238 papers
- KRCounterfactual Explanations and Model Multiplicity: a Relational Verification View
- AAAI (T1)Formalising the Robustness of Counterfactual Explanations for Neural Networks
- Journal on Satisfiability, Boolean Modeling and ComputationOMTPlan: a Tool for Optimal Planning Modulo Theories
- ACMLProvably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
- Proc. of EUMASRobust Explanations for Human-Neural Multi-agent Systems with Formal Verification
- AAMAS (T1)Towards Robust Contrastive Explanations for Human-Neural Multi-agent Systems
- KRVerification of Semantic Key Point Detection for Aircraft Pose Estimation
- IJCNNVerification-friendly Networks: The Case for Parametric ReLUs
Where alumni went
Alumni are not listed on the lab homepage.
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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Data
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- The alumni list could not be found on the lab site.
- Collected
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- Members
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