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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…
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…
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…
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…
Towards Automated Knowledge Integration From Human-Interpretable Representations
Katarzyna Kobalczyk, Mihaela van der Schaar
A significant challenge in machine learning, particularly in noisy and low-data environments, lies in effectively incorporating inductive biases to enhance data efficiency and robu…
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…