1 citations · 4 across the 16 of their papers we have counts for
16 papers
A Low-Rank Subspace Analysis of LLM Interventions
Angira Sharma, Christian Schroeder de Witt, Philip Torr +2
Interventions designed to modify a particular behavior in LLMs, such as refusal or sycophancy, often produce unintended changes in other behaviors. This lack of targeted control ma…
When Language Representations Interact: Separability and Cross-Lingual Effects in LLMs
Boris Marinov, Angira Sharma, Christian Schroeder de Witt +3
Large language models exhibit strong multilingual capabilities, however, their internal representations are difficult to interpret. Understanding these interactions is important fo…
Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
M Lopes Alves, Joel Dyer, Doyne Farmer +2
Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilitated the adoption of ABMs across…
Learning to Manage Investment Portfolios beyond Simple Utility Functions
Maarten P. Scholl, Mahmoud Mahfouz, Anisoara Calinescu +1
While investment funds publicly disclose their objectives in broad terms, their managers optimize for complex combinations of competing goals that go beyond simple risk-return trad…
Bayesian Decision Making around Experts
Daniel Jarne Ornia, Joel Dyer, Nicholas Bishop +2
Complex learning agents are increasingly deployed alongside existing experts, such as human operators or previously trained agents. However, it remains unclear how should learners…
Sandbagging in a Simple Survival Bandit Problem
Joel Dyer, Daniel Jarne Ornia, Nicholas Bishop +2
Evaluating the safety of frontier AI systems is an increasingly important concern, helping to measure the capabilities of such models and identify risks before deployment. However,…
Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu +2
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the inf…
Automatic Differentiation of Agent-Based Models
Arnau Quera-Bofarull, Nicholas Bishop, Joel Dyer +4
Agent-based models (ABMs) simulate complex systems by capturing the bottom-up interactions of individual agents comprising the system. Many complex systems of interest, such as epi…
IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)
Siyi Sun, David Antony Selby, Yunchuan Huang +3
Missing data imputation in tabular datasets remains a pivotal challenge in data science and machine learning, particularly within socioeconomic research. However, real-world socioe…
Emergent Risk Awareness in Rational Agents under Resource Constraints
Daniel Jarne Ornia, Nicholas Bishop, Joel Dyer +4
Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision-making problems under (approximate) utility fu…
SAGE: Scalable Ground Truth Evaluations for Large Sparse Autoencoders
Constantin Venhoff, Anisoara Calinescu, Philip Torr +1
A key challenge in interpretability is to decompose model activations into meaningful features. Sparse autoencoders (SAEs) have emerged as a promising tool for this task. However,…
Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model
Samuel Wiese, Jagoda Kaszowska-Mojsa, Joel Dyer +6
In the last few years, economic agent-based models have made the transition from qualitative models calibrated to match stylised facts to quantitative models for time series foreca…
A multi-objective combinatorial optimisation framework for large scale hierarchical population synthesis
Imran Mahmood, Nicholas Bishop, Anisoara Calinescu +2
In agent-based simulations, synthetic populations of agents are commonly used to represent the structure, behaviour, and interactions of individuals. However, generating a syntheti…
Causally Abstracted Multi-armed Bandits
Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer +4
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems. The majority of prior work typically studies and solves individual MAB and…
Interventionally Consistent Surrogates for Agent-based Simulators
Joel Dyer, Nicholas Bishop, Yorgos Felekis +4
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents. Their high-fidelit…
Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network
Peer Nagy, Sascha Frey, Silvia Sapora +4
Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we pr…