1.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Turing InstituteGB178 papers
- University of OxfordGB114 papers
- University of WarwickGB102 papers
- Queen Mary University of LondonGB91 papers
- University of CambridgeGB89 papers
- University College LondonGB81 papers
- Imperial College LondonGB60 papers
- University of EdinburghGB54 papers
- University of ManchesterGB30 papers
- University of ExeterGB25 papers
- University of SouthamptonGB20 papers
- Centre National de la Recherche ScientifiqueFR19 papers
61 papers · 1 filter
Conformal Adversarial Generative Ensemble
Ahmad Shahi, Mamehgol Yousefi, Brendon J. Woodford +2
Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influence of extreme forecasts. We intr…
Reliability of Probabilistic Emulation of Physical Systems
Sam F. Greenbury, Radka Jersakova, Paolo Conti +5
Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matching; and ensembles of determinist…
AI Generalisation Gap In Comorbid Sleep Disorder Staging
Saswata Bose, Suvadeep Maiti, Shivam Kumar Sharma +4
Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep lea…
Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
Qinyi Liu, Mohammad Khalil, Naman Goel
Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal…
Online Action-Stacking Improves Reinforcement Learning Performance for Air Traffic Control
Ben Carvell, George De Ath, Eseoghene Benjamin +1
We introduce online action-stacking, an inference-time wrapper for reinforcement learning policies that produces realistic air traffic control commands while allowing training on a…
Can Vibe Coding Beat Graduate CS Students? An LLM vs. Human Coding Tournament on Market-driven Strategic Planning
Panayiotis Danassis, Naman Goel
The rapid proliferation of Large Language Models (LLMs) has revolutionized AI-assisted code generation. This rapid development of LLMs has outpaced our ability to properly benchmar…
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia +4
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanism…
Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
Edward Henderson, Dewi Gould, Richard Everson +2
Real-time assessment of near-term Air Traffic Controller (ATCO) task demand is a critical challenge in an increasingly crowded airspace, as existing complexity metrics often fail t…
Online model learning with data-assimilated reservoir computers
Andrea Nóvoa, Luca Magri
We propose an online learning framework for forecasting nonlinear spatio-temporal signals (fields). The method integrates (i) dimensionality reduction, here, a simple proper orthog…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
AI for Explosive Ordnance Detection in Clearance Operations: The State of Research
Björn Kischelewski, Gregory Cathcart, David Wahl +1
The detection and clearance of explosive ordnance (EO) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its effi…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
What if? Causal Machine Learning in Supply Chain Risk Management
Mateusz Wyrembek, George Baryannis, Alexandra Brintrup
The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations…
Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
Sanyam Vyas, Chris Hicks, Vasilios Mavroudis
This paper investigates the threat of backdoors in Deep Reinforcement Learning (DRL) agent policies and proposes a novel method for their detection at runtime. Our study focuses on…
Private Collaborative Edge Inference via Over-the-Air Computation
Selim F. Yilmaz, Burak Hasircioglu, Li Qiao +1
We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accura…
Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
Jakob Benjamin Wessel, Christopher A. T. Ferro, Gavin R. Evans +1
Accurate forecasts of extreme wind speeds are of high importance for many applications. Such forecasts are usually generated by ensembles of numerical weather prediction (NWP) mode…
Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data
Yaxin Mo, Tullio Traverso, Luca Magri
Turbulent flows are chaotic and multi-scale dynamical systems, which have large numbers of degrees of freedom. Turbulent flows, however, can be modelled with a smaller number of de…
Scalable Data Assimilation with Message Passing
Oscar Key, So Takao, Daniel Giles +1
Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed a…
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
Filippo Corponi, Bryan M. Li, Gerard Anmella +13
Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood di…
Comparing Comparators in Generalization Bounds
Fredrik Hellström, Benjamin Guedj
We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the trainin…
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj +1
A fundamental question in theoretical machine learning is generalization. Over the past decades, the PAC-Bayesian approach has been established as a flexible framework to address t…
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause li…
Physics-Informed Long Short-Term Memory for Forecasting and Reconstruction of Chaos
Elise Özalp, Georgios Margazoglou, Luca Magri
We present the Physics-Informed Long Short-Term Memory (PI-LSTM) network to reconstruct and predict the evolution of unmeasured variables in a chaotic system. The training is const…
Towards Robust Metrics for Concept Representation Evaluation
Mateo Espinosa Zarlenga, Pietro Barbiero, Zohreh Shams +4
Recent work on interpretability has focused on concept-based explanations, where deep learning models are explained in terms of high-level units of information, referred to as conc…