Modelling the Effects of Colony Age on the Foraging Behaviour of Harvester Ants. - Tom Diethe; Peter J. Bentley (ECAL, 2007)

Kernel Polytope Faces Pursuit. - Tom Diethe; Zakria Hussain (ECML/PKDD (1), 2009)

Matching Pursuit Kernel Fisher Discriminant Analysis. - Tom Diethe; Zakria Hussain; David R. Hardoon; John Shawe-Taylor (AISTATS, 2009)

Constructing Nonlinear Discriminants from Multiple Data Views. - Tom Diethe; David R. Hardoon; John Shawe-Taylor (ECML/PKDD (1), 2010)

Preface. - Tom Diethe; Nello Cristianini; John Shawe-Taylor (WAPA, 2010)

Sparse machine learning methods with applications in multivariate signal processing. - Thomas Robert Diethe (2010)

Preface. - Tom Diethe; José L. Balcázar; John Shawe-Taylor; Cristina Tîrnauca (WAPA, 2011)

Data dependent kernels in nearly-linear time. - Guy Lever; Tom Diethe; John Shawe-Taylor (AISTATS, 2012)

Online Learning with (Multiple) Kernels: A Review. - Tom Diethe; Mark A. Girolami (Neural Comput., 2013)

Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. - Tom Diethe; Niall Twomey; Peter A. Flach (ECML/PKDD (2), 2015)

An RSSI-based wall prediction model for residential floor map construction. - Xenofon Fafoutis; Evangelos Mellios; Niall Twomey; Tom Diethe; Geoffrey S. Hilton; Robert J. Piechocki (WF-IoT, 2015)

Bridging e-Health and the Internet of Things: The SPHERE Project. - Ni Zhu; Tom Diethe; Massimo Camplani; Lili Tao; Alison Burrows; Niall Twomey; Dritan Kaleshi; Majid Mirmehdi; Peter A. Flach; Ian Craddock (IEEE Intell. Syst., 2015)

Active transfer learning for activity recognition. - Tom Diethe; Niall Twomey; Peter A. Flach (ESANN, 2016)

ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. - Yu Chen; Tom Diethe; Peter A. Flach (IJCAI, 2016)

BDL.NET: Bayesian dictionary learning in Infer.NET. - Tom Diethe; Niall Twomey; Peter A. Flach (MLSP, 2016)

The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. - Niall Twomey; Tom Diethe; Meelis Kull; Hao Song 007; Massimo Camplani; Sion L. Hannuna; Xenofon Fafoutis; Ni Zhu; Pete Woznowski; Peter A. Flach; Ian Craddock (CoRR, 2016)

On the need for structure modelling in sequence prediction. - Niall Twomey; Tom Diethe; Peter A. Flach (Mach. Learn., 2016)

Probabilistic Sensor Fusion for Ambient Assisted Living. - Tom Diethe; Niall Twomey; Meelis Kull; Peter A. Flach; Ian Craddock (CoRR, 2017)

Unsupervised learning of sensor topologies for improving activity recognition in smart environments. - Niall Twomey; Tom Diethe; Ian Craddock; Peter A. Flach (Neurocomputing, 2017)

Anomaly detection in star light curves using hierarchical Gaussian processes. - Haoyan Chen; Tom Diethe; Niall Twomey; Peter A. Flach (ESANN, 2018)

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. - Tom Diethe; Mike Holmes; Meelis Kull; Miquel Perelló-Nieto; Kacper Sokol; Hao Song 007; Emma Tonkin; Niall Twomey; Peter A. Flach (KDD, 2018)

A Comprehensive Study of Activity Recognition Using Accelerometers. - Niall Twomey; Tom Diethe; Xenofon Fafoutis; Atis Elsts; Ryan McConville; Peter A. Flach; Ian Craddock (Informatics, 2018)

$β^3$-IRT: A New Item Response Model and its Applications. - Yu Chen; Telmo de Menezes e Silva Filho; Ricardo B. C. Prudêncio; Tom Diethe; Peter A. Flach (AISTATS, 2019)

Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. - Oluwaseyi Feyisetan; Tom Diethe; Thomas Drake (ICDM, 2019)

Distribution calibration for regression. - Hao Song 007; Tom Diethe; Meelis Kull; Peter A. Flach (ICML, 2019)

β3-IRT: A New Item Response Model and its Applications. - Yu Chen; Telmo de Menezes e Silva Filho; Ricardo B. C. Prudêncio; Tom Diethe; Peter A. Flach (CoRR, 2019)

Continual Learning in Practice. - Tom Diethe; Tom Borchert; Eno Thereska; Borja Balle; Neil Lawrence (CoRR, 2019)

Privacy-preserving Active Learning on Sensitive Data for User Intent Classification. - Oluwaseyi Feyisetan; Thomas Drake; Borja Balle; Tom Diethe (CoRR, 2019)

Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients. - Yu Chen; Tom Diethe; Neil Lawrence (CoRR, 2019)

HyperStream: a Workflow Engine for Streaming Data. - Tom Diethe; Meelis Kull; Niall Twomey; Kacper Sokol; Hao Song 007; Miquel Perelló-Nieto; Emma Tonkin; Peter A. Flach (CoRR, 2019)

An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. - Niall Twomey; Haoyan Chen; Tom Diethe; Peter A. Flach (Neurocomputing, 2019)

Optimal Continual Learning has Perfect Memory and is NP-hard. - Jeremias Knoblauch; Hisham Husain; Tom Diethe (ICML, 2020)

Preserving Privacy in Analyses of Textual Data. - Tom Diethe; Oluwaseyi Feyisetan (PrivateNLP@WSDM, 2020)

Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. - Oluwaseyi Feyisetan; Borja Balle; Thomas Drake; Tom Diethe (WSDM, 2020)

Calibrating Mechanisms for Privacy Preserving Text Analysis. - Oluwaseyi Feyisetan; Borja Balle; Tom Diethe; Thomas Drake (PrivateNLP@WSDM, 2020)

Hyperbolic Embeddings for Preserving Privacy and Utility in Text. - Oluwaseyi Feyisetan; Tom Diethe; Thomas Drake (PrivateNLP@WSDM, 2020)

Similarity of Neural Networks with Gradients. - Shuai Tang; Wesley J. Maddox; Charlie Dickens; Tom Diethe; Andreas C. Damianou (CoRR, 2020)

Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. - Yu Chen; Tom Diethe; Peter A. Flach (CoRR, 2020)

Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams. - Charlie Dickens; Eric Meissner; Pablo G. Moreno; Tom Diethe (CoRR, 2020)

Automatic Discovery of Privacy-Utility Pareto Fronts. - Brendan Avent; Javier González 002; Tom Diethe; Andrei Paleyes; Borja Balle (Proc. Priv. Enhancing Technol., 2020)

Continual Density Ratio Estimation in an Online Setting. - Yu Chen; Song Liu; Tom Diethe; Peter A. Flach (CoRR, 2021)

Unlocking the Heart Using Adaptive Locked Agnostic Networks. - Sylwia Majchrowska; Anders Hildeman; Philip Teare; Tom Diethe (CoRR, 2023)

An Image is Worth Multiple Words: Learning Object Level Concepts using Multi-Concept Prompt Learning. - Chen Jin; Ryutaro Tanno; Amrutha Saseendran; Tom Diethe; Philip Teare (CoRR, 2023)

Tackling Structural Hallucination in Image Translation with Local Diffusion. - Seunghoi Kim; Chen Jin; Tom Diethe; Matteo Figini; Henry F. J. Tregidgo; Asher Mullokandov; Philip Teare; Daniel C. Alexander (ECCV (81), 2024)

An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning. - Chen Jin; Ryutaro Tanno; Amrutha Saseendran; Tom Diethe; Philip Teare (ICML, 2024)

Measures of diversity and space-filling designs for categorical data. - Cédric Malherbe; Emilio Domínguez-Sánchez; Merwan Barlier; Igor Colin; Haitham Bou-Ammar; Tom Diethe (ICML, 2024)

Improving Antibody Humanness Prediction using Patent Data. - Talip Ucar; Aubin Ramon; Dino Oglic; Rebecca Croasdale-Wood; Tom Diethe; Pietro Sormanni (ICML, 2024)

DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations. - Aryo Pradipta Gema; Chen Jin; Ahmed Abdulaal; Tom Diethe; Philip Teare; Beatrice Alex; Pasquale Minervini; Amrutha Saseendran (CoRR, 2024)

Balancing Act: Diversity and Consistency in Large Language Model Ensembles. - Ahmed Abdulaal; Chen Jin; Nina Montaña Brown; Aryo Pradipta Gema; Daniel C. Castro; Daniel C. Alexander; Philip Alexander Teare; Tom Diethe; Dino Oglic; Amrutha Saseendran (ICLR, 2025)

Exploring interpretable echo analysis using self-supervised parcels. - Sylwia Majchrowska; Anders Hildeman; Ricardo Mokhtari; Tom Diethe; Philip Teare (Comput. Biol. Medicine, 2025)

Big Batch Bayesian Active Learning by Considering Predictive Probabilities. - Sebastian W. Ober; Samuel Power; Tom Diethe; Henry B. Moss (CoRR, 2025)

Diffusion Instruction Tuning. - Chen Jin; Ryutaro Tanno; Amrutha Saseendran; Tom Diethe; Philip Teare (CoRR, 2025)

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation. - Zhihua Liu; Amrutha Saseendran; Lei Tong; Xilin He; Fariba Yousefi; Nikolay Burlutskiy; Dino Oglic; Tom Diethe; Philip Teare; Huiyu Zhou 001; Chen Jin (CoRR, 2025)

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces. - Henry B. Moss; Sebastian W. Ober; Tom Diethe (CoRR, 2025)