Vignesh Viswanathan

I am a Computer Science PhD candidate at the University of Massachusetts, Amherst working primarily with Prof. Yair Zick. Prior to joining UMass Amherst, I received my undergraduate degree at IIT Kharagpur.

I like working on combinatorial problems in computational economics. My current focus is on problems in fair allocation and two sided matching. I have also worked on graph arrangements, market equilibria and explainable AI during my PhD.

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Publications

Unless otherwise mentioned, authors are alphabetically ordered. For papers where authors are ordered by contribution, * is used to denote the lead author(s). Representative papers are highlighted.

Journal Publications

A new method to generate counterfactual explanations for machine learning models.

How to assign a set of numbers to the vertices of a graph so as to minimize the sum of absolute differences along the edges?
Subsumes our AAMAS 2023 paper.

Conference Publications

Approximation algorithms for the graphical house allocation problem studied in our AAMAS 2023 paper. Also, Ramanujan graphs and a cool connection between graph cuts and bitstrings.

Cooperative game theory meets formal logic meets model explanations!

Fair allocation of indivisible items when agents valuations are submodular and each item has a marginal contribution of -1, 0 or some positive integer c.

Algorithms for fair allocation of indivisible items when agents valuations are submodular and each item has a marginal contribution of 1 or some positive integer c.

A framework that implies efficient algorithms for almost all fairness objectives when agents have binary submodular valuations.

A fast algorithm for leximin allocations when agents have binary submodular valuations.

How to assign a set of numbers to the vertices of a graph so as to minimize the sum of absolute differences along the edges?
Superseded by our JAAMAS 2024 paper.

Can we compute allocations that satisfy the envy-free up to any good (EFX) condition only for certain pairs of agents?

Can we hide the vulnerabilities of a web application by randomizing over multiple implementations?

Algorithms to compute approximate market equilibria when the only knowledge of agent preferences comes from samples.

Allocating scarce firefighting resources to different regions under uncertainty.

Other Ramblings

We can build a general Yankee Swap type framework for binary chores as well!

Thanks to Arpita, I got to be co-author of a book chapter!

Thanks and Thanks!