5 papers
Set-Valued Policy Learning
Laura Fuentes-Vicente, Mathieu Even, Gaëlle Dormion +3
Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. Although a rich body of causal inferenc…
INSIGHTS: Demonstration-Based Summaries of Time Series Predictors
Bar Eini Porat, Rom Gutman, Uri Shalit +1
Explainability methods have progressed rapidly, but global explanations for time-series models remain underdeveloped, with most approaches focusing on local, instance-level attribu…
Controllable User Simulation
Guy Tennenholtz, Ofer Meshi, Amir Globerson +3
Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies. This has motivated the use of controllable user sim…
Set Valued Predictions For Robust Domain Generalization
Ron Tsibulsky, Daniel Nevo, Uri Shalit
Despite the impressive advancements in modern machine learning, achieving robustness in Domain Generalization (DG) tasks remains a significant challenge. In DG, models are expected…
BIG-Bench Extra Hard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal +17
Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LL…