Tom Diethe - RankMe

70 papers A* 12A 4B 2Journal 37Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CoRefine: Confidence-Guided Self-Refinement for Adaptive Test-Time Compute. CoRR Chen Jin, Ryutaro Tanno, Tom Diethe, Philip Teare
2025	A*	`conf`	Balancing Act: Diversity and Consistency in Large Language Model Ensembles. ICLR 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
2025	J	`jnl`	Beyond Quantification: Navigating Uncertainty in Professional AI Systems. CoRR Sylvie Delacroix, Diana Robinson, Umang Bhatt, Jacopo Domenicucci, Jessica Montgomery, Gaël Varoquaux, Carl Henrik Ek, Vincent Fortuin, Yulan He, Tom Diethe, Neill W. Campbell, Mennatallah El-Assady, Søren Hauberg, Ivana Dusparic, Neil D. Lawrence
2025	J	`jnl`	Big Batch Bayesian Active Learning by Considering Predictive Probabilities. CoRR Sebastian W. Ober, Samuel Power, Tom Diethe, Henry B. Moss
2025	J	`jnl`	Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation. CoRR Lei Tong, Zhihua Liu, Chaochao Lu, Dino Oglic, Tom Diethe, Philip Teare, Sotirios A. Tsaftaris, Chen Jin
2025	—	`conf`	DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations. EMNLP (Findings) Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal, Tom Diethe, Philip Alexander Teare, Beatrice Alex, Pasquale Minervini, Amrutha Saseendran
2025	A*	`conf`	Diffusion Instruction Tuning. ICML Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Alexander Teare
2025	J	`jnl`	Diffusion Instruction Tuning. CoRR Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
2025	J	`jnl`	Exploring interpretable echo analysis using self-supervised parcels. Comput. Biol. Medicine Sylwia Majchrowska, Anders Hildeman, Ricardo Mokhtari, Tom Diethe, Philip Teare
2025	A*	`conf`	Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces. ICML Henry B. Moss, Sebastian W. Ober, Tom Diethe
2025	J	`jnl`	Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces. CoRR Henry B. Moss, Sebastian W. Ober, Tom Diethe
2025	A*	`conf`	Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation. ICML Zhihua Liu, Amrutha Saseendran, Lei Tong, Xilin He, Fariba Yousefi, Nikolay Burlutskiy, Dino Oglic, Tom Diethe, Philip Alexander Teare, Huiyu Zhou, Chen Jin
2025	J	`jnl`	Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation. CoRR Zhihua Liu, Amrutha Saseendran, Lei Tong, Xilin He, Fariba Yousefi, Nikolay Burlutskiy, Dino Oglic, Tom Diethe, Philip Teare, Huiyu Zhou, Chen Jin
2024	A*	`conf`	An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning. ICML Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
2024	J	`jnl`	DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations. CoRR Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal, Tom Diethe, Philip Teare, Beatrice Alex, Pasquale Minervini, Amrutha Saseendran
2024	A*	`conf`	Improving Antibody Humanness Prediction using Patent Data. ICML Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni
2024	J	`jnl`	Improving Antibody Humanness Prediction using Patent Data. CoRR Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni
2024	A*	`conf`	Measures of diversity and space-filling designs for categorical data. ICML Cédric Malherbe, Emilio Domínguez-Sánchez, Merwan Barlier, Igor Colin, Haitham Bou-Ammar, Tom Diethe
2024	—	`conf`	Tackling Structural Hallucination in Image Translation with Local Diffusion. ECCV (81) Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander
2024	J	`jnl`	Tackling Structural Hallucination in Image Translation with Local Diffusion. CoRR Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander
2023	J	`jnl`	An Image is Worth Multiple Words: Learning Object Level Concepts using Multi-Concept Prompt Learning. CoRR Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
2023	J	`jnl`	Unlocking the Heart Using Adaptive Locked Agnostic Networks. CoRR Sylwia Majchrowska, Anders Hildeman, Philip Teare, Tom Diethe
2021	J	`jnl`	Continual Density Ratio Estimation in an Online Setting. CoRR Yu Chen, Song Liu, Tom Diethe, Peter A. Flach
2020	J	`jnl`	Automatic Discovery of Privacy-Utility Pareto Fronts. Proc. Priv. Enhancing Technol. Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle
2020	J	`jnl`	Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. CoRR Yu Chen, Tom Diethe, Peter A. Flach
2020	—	`conf`	Calibrating Mechanisms for Privacy Preserving Text Analysis. PrivateNLP@WSDM Oluwaseyi Feyisetan, Borja Balle, Tom Diethe, Thomas Drake
2020	—	`conf`	Hyperbolic Embeddings for Preserving Privacy and Utility in Text. PrivateNLP@WSDM Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
2020	J	`jnl`	Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams. CoRR Charlie Dickens, Eric Meissner, Pablo G. Moreno, Tom Diethe
2020	A*	`conf`	Optimal Continual Learning has Perfect Memory and is NP-hard. ICML Jeremias Knoblauch, Hisham Husain, Tom Diethe
2020	J	`jnl`	Optimal Continual Learning has Perfect Memory and is NP-hard. CoRR Jeremias Knoblauch, Hisham Husain, Tom Diethe
2020	—	`conf`	Preserving Privacy in Analyses of Textual Data. PrivateNLP@WSDM Tom Diethe, Oluwaseyi Feyisetan
2020	A	`conf`	Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. WSDM Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, Tom Diethe
2020	J	`jnl`	Similarity of Neural Networks with Gradients. CoRR Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou
2019	A	`conf`	$β^3$-IRT: A New Item Response Model and its Applications. AISTATS Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach
2019	J	`jnl`	An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. Neurocomputing Niall Twomey, Haoyan Chen, Tom Diethe, Peter A. Flach
2019	J	`jnl`	Automatic Discovery of Privacy-Utility Pareto Fronts. CoRR Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle
2019	J	`jnl`	Continual Learning in Practice. CoRR Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle, Neil Lawrence
2019	J	`jnl`	Distribution Calibration for Regression. CoRR Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach
2019	A*	`conf`	Distribution calibration for regression. ICML Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach
2019	J	`jnl`	Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients. CoRR Yu Chen, Tom Diethe, Neil Lawrence
2019	J	`jnl`	HyperStream: a Workflow Engine for Streaming Data. CoRR Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach
2019	A*	`conf`	Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. ICDM Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
2019	J	`jnl`	Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. CoRR Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
2019	J	`jnl`	Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. CoRR Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, Tom Diethe
2019	J	`jnl`	Privacy-preserving Active Learning on Sensitive Data for User Intent Classification. CoRR Oluwaseyi Feyisetan, Thomas Drake, Borja Balle, Tom Diethe
2019	J	`jnl`	β CoRR Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach
2018	J	`jnl`	A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter A. Flach, Ian Craddock
2018	B	`conf`	Anomaly detection in star light curves using hierarchical Gaussian processes. ESANN Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach
2018	A*	`conf`	Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach
2017	J	`jnl`	Probabilistic Sensor Fusion for Ambient Assisted Living. CoRR Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock
2017	J	`jnl`	Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing Niall Twomey, Tom Diethe, Ian Craddock, Peter A. Flach
2016	A*	`conf`	ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. IJCAI Yu Chen, Tom Diethe, Peter A. Flach
2016	B	`conf`	Active transfer learning for activity recognition. ESANN Tom Diethe, Niall Twomey, Peter A. Flach
2016	—	`conf`	BDL.NET: Bayesian dictionary learning in Infer.NET. MLSP Tom Diethe, Niall Twomey, Peter A. Flach
2016	J	`jnl`	On the need for structure modelling in sequence prediction. Mach. Learn. Niall Twomey, Tom Diethe, Peter A. Flach
2016	J	`jnl`	The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. CoRR Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock
2015	—	`conf`	An RSSI-based wall prediction model for residential floor map construction. WF-IoT Xenofon Fafoutis, Evangelos Mellios, Niall Twomey, Tom Diethe, Geoffrey S. Hilton, Robert J. Piechocki
2015	—	`conf`	Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. ECML/PKDD (2) Tom Diethe, Niall Twomey, Peter A. Flach
2015	J	`jnl`	Bridging e-Health and the Internet of Things: The SPHERE Project. IEEE Intell. Syst. Ni Zhu, Tom Diethe, Massimo Camplani, Lili Tao, Alison Burrows, Niall Twomey, Dritan Kaleshi, Majid Mirmehdi, Peter A. Flach, Ian Craddock
2013	J	`jnl`	Online Learning with (Multiple) Kernels: A Review. Neural Comput. Tom Diethe, Mark A. Girolami
2012	A	`conf`	Data dependent kernels in nearly-linear time. AISTATS Guy Lever, Tom Diethe, John Shawe-Taylor
2011	J	`jnl`	Data-dependent kernels in nearly-linear time CoRR Guy Lever, Tom Diethe, John Shawe-Taylor
2011	—	`conf`	Preface. WAPA Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca
2011	—	`ed.`	Proceedings of the Second Workshop on Applications of Pattern Analysis, WAPA 2011, Castro Urdiales, Spain, October 19-21, 2011 WAPA Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca
2010	—	`conf`	Constructing Nonlinear Discriminants from Multiple Data Views. ECML/PKDD (1) Tom Diethe, David R. Hardoon, John Shawe-Taylor
2010	—	`conf`	Preface. WAPA Tom Diethe, Nello Cristianini, John Shawe-Taylor
2010	—	`ed.`	Proceedings of the First Workshop on Applications of Pattern Analysis, WAPA 2010, Cumberland Lodge, Windsor, UK, September 1-3, 2010 WAPA Tom Diethe, Nello Cristianini, John Shawe-Taylor
2009	—	`conf`	Kernel Polytope Faces Pursuit. ECML/PKDD (1) Tom Diethe, Zakria Hussain
2009	A	`conf`	Matching Pursuit Kernel Fisher Discriminant Analysis. AISTATS Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor
2007	—	`conf`	Modelling the Effects of Colony Age on the Foraging Behaviour of Harvester Ants. ECAL Tom Diethe, Peter J. Bentley