59 citations · 140 across the 14 of their papers we have counts for
14 papers
Fast Adversarial Attacks with Gradient Prediction
Kamil Ciosek, Aleksandr V. Petrov, Nicolò Felicioni +1
Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even "fast" attacks such as FGSM remain throughpu…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
Mapping Faithful Reasoning in Language Models
Jiazheng Li, Andreas Damianou, J Rosser +2
Chain-of-thought (CoT) traces promise transparency for reasoning language models, but prior work shows they are not always faithful reflections of internal computation. This raises…
Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models
Konstantina Palla, José Luis Redondo García, Claudia Hauff +5
Content moderation plays a critical role in shaping safe and inclusive online environments, balancing platform standards, user expectations, and regulatory frameworks. Traditionall…
A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks
Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna +4
Performing gradient descent in a wide neural network is equivalent to computing the posterior mean of a Gaussian Process with the Neural Tangent Kernel (NTK-GP), for a specific pri…
Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
Sergio Calvo-Ordoñez, Konstantina Palla, Kamil Ciosek
Recent work has shown that training wide neural networks with gradient descent is formally equivalent to computing the mean of the posterior distribution in a Gaussian Process (GP)…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…
Enhancing Content Moderation with Culturally-Aware Models
Alex J. Chan, José Luis Redondo García, Fabrizio Silvestri +2
Content moderation on a global scale must navigate a complex array of local cultural distinctions, which can hinder effective enforcement. While global policies aim for consistency…
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla +3
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may deci…
Bayesian Nonparametrics for Sparse Dynamic Networks
Cian Naik, Francois Caron, Judith Rousseau +2
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the…
A reversible infinite HMM using normalised random measures
Konstantina Palla, David A. Knowles, Zoubin Ghahramani
We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weight…
A dependent partition-valued process for multitask clustering and time evolving network modelling
Konstantina Palla, David A. Knowles, Zoubin Ghahramani
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or t…
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
Simon Lacoste-Julien, Konstantina Palla, Alex Davies +3
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically align…
An Infinite Latent Attribute Model for Network Data
Konstantina Palla, David Knowles, Zoubin Ghahramani
Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network in…