83 citations · 175 across the 8 of their papers we have counts for
9 papers
Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity
Anjun Hu, Hanting Xie, Saranya Govindan +2
Multi-agent collaborative filtering (CF) systems coordinate autonomous LLM-powered user and item agents through natural-language interaction to refine preferences and generate reco…
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Joseph Early, Gavin KC Cheung, Kurt Cutajar +3
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multi…
Low-count Time Series Anomaly Detection
Philipp Renz, Kurt Cutajar, Niall Twomey +2
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct chal…
Deep Gaussian Processes for Multi-fidelity Modeling
Kurt Cutajar, Mark Pullin, Andreas Damianou +2
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in o…
Entropic Trace Estimates for Log Determinants
Jack Fitzsimons, Diego Granziol, Kurt Cutajar +3
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian…
Bayesian Inference of Log Determinants
Jack Fitzsimons, Kurt Cutajar, Michael Osborne +2
The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through t…
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
Karl Krauth, Edwin V. Bonilla, Kurt Cutajar +1
We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference;…
Random Feature Expansions for Deep Gaussian Processes
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi +1
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning prob…
Preconditioning Kernel Matrices
Kurt Cutajar, Michael A. Osborne, John P. Cunningham +1
The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue i…