4 citations · 7 across the 8 of their papers we have counts for
9 papers
Discovery of Hidden Miscalibration Regimes
Katarzyna Kobalczyk, Mihaela van der Schaar
Calibration is commonly evaluated by comparing model confidence with its empirical correctness, implicitly treating reliability as a function of the confidence score alone. However…
Eliciting Numerical Predictive Distributions of LLMs Without Autoregression
Julianna Piskorz, Katarzyna Kobalczyk, Mihaela van der Schaar
Large Language Models (LLMs) have recently been successfully applied to regression tasks -- such as time series forecasting and tabular prediction -- by leveraging their in-context…
LILO: Bayesian Optimization with Natural Language Feedback
Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham +3
Many real-world optimization problems are guided by complex, subjective preferences that are difficult to express as explicit closed-form objectives. In response, we introduce Lang…
Interpretable Reward Modeling with Active Concept Bottlenecks
Sonia Laguna, Katarzyna Kobalczyk, Julia E. Vogt +1
We introduce Concept Bottleneck Reward Models (CB-RM), a reward modeling framework that enables interpretable preference learning through selective concept annotation. Unlike stand…
Preference Learning for AI Alignment: a Causal Perspective
Katarzyna Kobalczyk, Mihaela van der Schaar
Reward modelling from preference data is a crucial step in aligning large language models (LLMs) with human values, requiring robust generalisation to novel prompt-response pairs.…
Active Task Disambiguation with LLMs
Katarzyna Kobalczyk, Nicolas Astorga, Tennison Liu +1
Despite the impressive performance of large language models (LLMs) across various benchmarks, their ability to address ambiguously specified problems--frequent in real-world intera…
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun +1
As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critic…
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces
Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic +4
Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain. Existing few-shot methods are not directly applicable to tabular datasets d…
cegpy: Modelling with Chain Event Graphs in Python
Gareth Walley, Aditi Shenvi, Peter Strong +1
Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to…