6 papers
The Burden of Interactive Alignment with Inconsistent Preferences
Ali Shirali
From media platforms to chatbots, algorithms shape how people interact, learn, and discover information. Such interactions between users and an algorithm often unfold over multiple…
Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
Ahmad-Reza Ehyaei, Ali Shirali, Samira Samadi
Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar…
The Hidden Cost of Waiting for Accurate Predictions
Ali Shirali, Ariel Procaccia, Rediet Abebe
Algorithmic predictions are increasingly informing societal resource allocations by identifying individuals for targeting. Policymakers often build these systems with the assumptio…
Direct Alignment with Heterogeneous Preferences
Ali Shirali, Arash Nasr-Esfahany, Abdullah Alomar +3
Alignment with human preferences is commonly framed using a universal reward function, even though human preferences are inherently heterogeneous. We formalize this heterogeneity b…
What Makes ImageNet Look Unlike LAION
Ali Shirali, Moritz Hardt
ImageNet was famously created from Flickr image search results. What if we recreated ImageNet instead by searching the massive LAION dataset based on image captions alone? In this…
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care
Ali Shirali, Alexander Schubert, Ahmed Alaa
Medical treatments often involve a sequence of decisions, each informed by previous outcomes. This process closely aligns with reinforcement learning (RL), a framework for optimizi…