Tom Diethe

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 38

Top Topics

Gaussian Process

Anomaly Detection

Activity Recognition

Preserving Privacy

Top Venues

PrivateNLP@WSDM

Publications

Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni
Improving Antibody Humanness Prediction using Patent Data. CoRR (2024)
Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander
Tackling Structural Hallucination in Image Translation with Local Diffusion. CoRR (2024)
Sylwia Majchrowska, Anders Hildeman, Philip Teare, Tom Diethe
Unlocking the Heart Using Adaptive Locked Agnostic Networks. CoRR (2023)
Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
An Image is Worth Multiple Words: Learning Object Level Concepts using Multi-Concept Prompt Learning. CoRR (2023)
Yu Chen, Song Liu, Tom Diethe, Peter A. Flach
Continual Density Ratio Estimation in an Online Setting. CoRR (2021)
Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou
Similarity of Neural Networks with Gradients. CoRR (2020)
Tom Diethe, Oluwaseyi Feyisetan
Preserving Privacy in Analyses of Textual Data. PrivateNLP@WSDM (2020)
Yu Chen, Tom Diethe, Peter A. Flach
Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. CoRR (2020)
Jeremias Knoblauch, Hisham Husain, Tom Diethe
Optimal Continual Learning has Perfect Memory and is NP-hard. CoRR (2020)
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, Tom Diethe
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. WSDM (2020)
Charlie Dickens, Eric Meissner, Pablo G. Moreno, Tom Diethe
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams. CoRR (2020)
Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
Hyperbolic Embeddings for Preserving Privacy and Utility in Text. PrivateNLP@WSDM (2020)
Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle
Automatic Discovery of Privacy-Utility Pareto Fronts. Proc. Priv. Enhancing Technol. 2020 (4) (2020)
Oluwaseyi Feyisetan, Borja Balle, Tom Diethe, Thomas Drake
Calibrating Mechanisms for Privacy Preserving Text Analysis. PrivateNLP@WSDM (2020)
Jeremias Knoblauch, Hisham Husain, Tom Diethe
Optimal Continual Learning has Perfect Memory and is NP-hard. ICML (2020)
Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach
Distribution Calibration for Regression. CoRR (2019)
Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. ICDM (2019)
Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach
$β^3$-IRT: A New Item Response Model and its Applications. AISTATS (2019)
Niall Twomey, Haoyan Chen, Tom Diethe, Peter A. Flach
An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. Neurocomputing 342 (2019)
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, Tom Diethe
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. CoRR (2019)
Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach
HyperStream: a Workflow Engine for Streaming Data. CoRR (2019)
Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle, Neil Lawrence
Continual Learning in Practice. CoRR (2019)
Oluwaseyi Feyisetan, Thomas Drake, Borja Balle, Tom Diethe
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification. CoRR (2019)
Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. CoRR (2019)
Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach
Distribution calibration for regression. ICML (2019)
Brendan Avent, Javier Gonzalez, Tom Diethe, Andrei Paleyes, Borja Balle
Automatic Discovery of Privacy-Utility Pareto Fronts. CoRR (2019)
Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach
-IRT: A New Item Response Model and its Applications. CoRR (2019)
Yu Chen, Tom Diethe, Neil Lawrence
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients. CoRR (2019)
Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach
Anomaly detection in star light curves using hierarchical Gaussian processes. ESANN (2018)
Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD (2018)
Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter A. Flach, Ian Craddock
A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics 5 (2) (2018)
Niall Twomey, Tom Diethe, Ian Craddock, Peter A. Flach
Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing 234 (2017)
Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock
Probabilistic Sensor Fusion for Ambient Assisted Living. CoRR (2017)
Yu Chen, Tom Diethe, Peter A. Flach
ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. IJCAI (2016)
Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. CoRR (2016)
Tom Diethe, Niall Twomey, Peter A. Flach
BDL.NET: Bayesian dictionary learning in Infer.NET. MLSP (2016)
Niall Twomey, Tom Diethe, Peter A. Flach
On the need for structure modelling in sequence prediction. Mach. Learn. 104 (2-3) (2016)
Tom Diethe, Niall Twomey, Peter A. Flach
Active transfer learning for activity recognition. ESANN (2016)
Ni Zhu, Tom Diethe, Massimo Camplani, Lili Tao, Alison Burrows, Niall Twomey, Dritan Kaleshi, Majid Mirmehdi, Peter A. Flach, Ian Craddock
Bridging e-Health and the Internet of Things: The SPHERE Project. IEEE Intell. Syst. 30 (4) (2015)
Xenofon Fafoutis, Evangelos Mellios, Niall Twomey, Tom Diethe, Geoffrey S. Hilton, Robert J. Piechocki
An RSSI-based wall prediction model for residential floor map construction. WF-IoT (2015)
Tom Diethe, Niall Twomey, Peter A. Flach
Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. ECML/PKDD (2) (2015)
Tom Diethe, Mark A. Girolami
Online Learning with (Multiple) Kernels: A Review. Neural Comput. 25 (3) (2013)
Guy Lever, Tom Diethe, John Shawe-Taylor
Data dependent kernels in nearly-linear time. AISTATS (2012)
Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca
Preface. WAPA (2011)
Guy Lever, Tom Diethe, John Shawe-Taylor
Data-dependent kernels in nearly-linear time CoRR (2011)
Tom Diethe, Nello Cristianini, John Shawe-Taylor
Preface. WAPA (2010)
Tom Diethe, David R. Hardoon, John Shawe-Taylor
Constructing Nonlinear Discriminants from Multiple Data Views. ECML/PKDD (1) (2010)
Tom Diethe, Zakria Hussain
Kernel Polytope Faces Pursuit. ECML/PKDD (1) (2009)
Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor
Matching Pursuit Kernel Fisher Discriminant Analysis. AISTATS (2009)
Tom Diethe, Peter J. Bentley
Modelling the Effects of Colony Age on the Foraging Behaviour of Harvester Ants. ECAL (2007)