17 citations · 44 across the 21 of their papers we have counts for
8 papers · 1 filter
Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs
Tanya Chowdhury, Atharva Nijasure, Yair Zick +1
Fine-tuned Large Language Models (LLMs) encode rich task-specific features, but the form of these representations, especially within MLP layers, remains unclear. Empirical inspecti…
Heterogeneous Multi-Agent Bandits with Parsimonious Hints
Amirmahdi Mirfakhar, Xuchuang Wang, Jinhang Zuo +2
We study a hinted heterogeneous multi-agent multi-armed bandits problem (HMA2B), where agents can query low-cost observations (hints) in addition to pulling arms. In this framework…
Percentile Criterion Optimization in Offline Reinforcement Learning
Elita A. Lobo, Cyrus Cousins, Yair Zick +1
In reinforcement learning, robust policies for high-stakes decision-making problems with limited data are usually computed by optimizing the \emph{percentile criterion}. The percen…
Simple Steps to Success: A Method for Step-Based Counterfactual Explanations
Jenny Hamer, Nicholas Perello, Jake Valladares +2
Algorithmic recourse is a process that leverages counterfactual explanations, going beyond understanding why a system produced a given classification, to providing a user with acti…
Model Explanations via the Axiomatic Causal Lens
Gagan Biradar, Vignesh Viswanathan, Yair Zick
Explaining the decisions of black-box models is a central theme in the study of trustworthy ML. Numerous measures have been proposed in the literature; however, none of them take a…
Model Explanations with Differential Privacy
Neel Patel, Reza Shokri, Yair Zick
Black-box machine learning models are used in critical decision-making domains, giving rise to several calls for more algorithmic transparency. The drawback is that model explanati…