58 citations · 163 across the 32 of their papers we have counts for
39 papers
Fair Allocation with Dichotomous Preferences over Item Complements
Soumyajit Pyne, Vignesh Viswanathan, Wenhao Yang +1
We study the problem of fair allocation when agents have dichotomous submodular preferences over groups of, rather than individual, items. These preferences naturally extend existi…
STRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks
ELita Lobo, Xu Chen, Jingjing Meng +5
Existing LLM-based web agents struggle on complex, long-horizon tasks due to limited in-context memory, weak planning abilities, and greedy behaviors that lead to premature termina…
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…
Stable Matching under Matroid Rank Valuations
Alon Eden, Vignesh Viswanathan, Yair Zick
We study a two-sided matching model where one side of the market (hospitals) has combinatorial preferences over the other side (doctors). Specifically, we consider the setting wher…
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…
Deploying Fair and Efficient Course Allocation Mechanisms
George Bissias, Cyrus Cousins, Paula Navarrete Diaz +1
Universities regularly face the challenging task of assigning classes to thousands of students while considering their preferences, along with course schedules and capacities. Ensu…
Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty
Elita Lobo, Justin Payan, Cyrus Cousins +1
We study fair allocation of constrained resources, where a market designer optimizes overall welfare while maintaining group fairness. In many large-scale settings, utilities are n…
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
Tanya Chowdhury, Yair Zick, James Allan
Numerous works propose post-hoc, model-agnostic explanations for learning to rank, focusing on ordering entities by their relevance to a query through feature attribution methods.…
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…
On the Hardness of Fair Allocation under Ternary Valuations
Zack Fitzsimmons, Vignesh Viswanathan, Yair Zick
We study the problem of fair allocation of indivisible items when agents have ternary additive valuations -- each agent values each item at some fixed integer values , , or $…
Axiomatic Aggregations of Abductive Explanations
Gagan Biradar, Yacine Izza, Elita Lobo +2
The recent criticisms of the robustness of post hoc model approximation explanation methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations. For e…
The Good, the Bad and the Submodular: Fairly Allocating Mixed Manna Under Order-Neutral Submodular Preferences
Cyrus Cousins, Vignesh Viswanathan, Yair Zick
We study the problem of fairly allocating indivisible goods (positively valued items) and chores (negatively valued items) among agents with decreasing marginal utilities over item…
Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems
Warren Buckley, Adrian Byrne, Nicholas Perello +4
We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of…
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…
Weighted Notions of Fairness with Binary Supermodular Chores
Vignesh Viswanathan, Yair Zick
We study the problem of allocating indivisible chores among agents with binary supermodular cost functions. In other words, each chore has a marginal cost of or and chores…
For One and All: Individual and Group Fairness in the Allocation of Indivisible Goods
Jonathan Scarlett, Nicholas Teh, Yair Zick
Fair allocation of indivisible goods is a well-explored problem. Traditionally, research focused on individual fairness - are individual agents satisfied with their allotted share?…
Dividing Good and Better Items Among Agents with Bivalued Submodular Valuations
Cyrus Cousins, Vignesh Viswanathan, Yair Zick
We study the problem of fairly allocating a set of indivisible goods among agents with {\em bivalued submodular valuations} -- each good provides a marginal gain of either or $…
Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities
Cyrus Cousins, Justin Payan, Yair Zick
A successful peer review process requires that qualified and interested reviewers are assigned to each paper. Most automated reviewer assignment approaches estimate a real-valued a…
A General Framework for Fair Allocation under Matroid Rank Valuations
Vignesh Viswanathan, Yair Zick
We study the problem of fairly allocating a set of indivisible goods among agents with matroid rank valuations -- every good provides a marginal value of or when added to a…
Yankee Swap: a Fast and Simple Fair Allocation Mechanism for Matroid Rank Valuations
Vignesh Viswanathan, Yair Zick
We study fair allocation of indivisible goods when agents have matroid rank valuations. Our main contribution is a simple algorithm based on the colloquial Yankee Swap procedure th…
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…
I Will Have Order! Optimizing Orders for Fair Reviewer Assignment
Justin Payan, Yair Zick
We present fast, fair, flexible, and welfare efficient algorithms for assigning reviewers to submitted conference papers. Our approaches extend picking sequence mechanisms, standar…
The Price is (Probably) Right: Learning Market Equilibria from Samples
Vignesh Viswanathan, Omer Lev, Neel Patel +1
Equilibrium computation in markets usually considers settings where player valuation functions are known. We consider the setting where player valuations are unknown; using a PAC l…
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…