Evaluation and Performance Assessment

Últimos artículos

• DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects

  Shu Tamano · 3 de febrero de 2026

  Off-policy evaluation and learning in contextual bandits use logged interaction data to estimate and optimize the value of a target policy. Most existing methods require sufficient action overlap between the logging and target policies, and violations can bias value and policy gradient estimates. To…

• How Reliable are Causal Probing Interventions?

  Marc Canby, Adam Davies, Chirag Rastogi, Julia Hockenmaier · 23 de diciembre de 2025

  Causal probing aims to analyze foundation models by examining how intervening on their representation of various latent properties impacts their outputs. Recent works have cast doubt on the theoretical basis of several leading causal probing methods, but it has been unclear how to systematically eva…

• MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory

  Vasudha Varadarajan, Hui Xu, Rebecca Astrid Boehme, Mariam Marlan Mirstrom, Sverker Sikstrom, H. Andrew Schwartz · 21 de noviembre de 2025

  Recent advances in large language models (LLMs) offer new opportunities for scalable, interactive mental health assessment, but excessive querying by LLMs burdens users and is inefficient for real-world screening across transdiagnostic symptom profiles. We introduce MAQuA, an adaptive question-askin…

• COPA: Comparing the incomparable in multi-objective model evaluation

  Adri\'an Javaloy, Antonio Vergari, Isabel Valera · 12 de noviembre de 2025

  In machine learning (ML), we often need to choose one among hundreds of trained ML models at hand, based on various objectives such as accuracy, robustness, fairness or scalability. However, it is often unclear how to compare, aggregate and, ultimately, trade-off these objectives, making it a time-c…

• Program Evaluation with Remotely Sensed Outcomes

  Ashesh Rambachan, Rahul Singh, Davide Viviano · 29 de octubre de 2025

  Economists often estimate treatment effects in experiments using remotely sensed variables (RSVs), e.g., satellite images or mobile phone activity, in place of directly measured economic outcomes. A common practice is to use an observational sample to train a predictor of the economic outcome from t…