works on

From the 1 of 13 linked papers with an AI index.

activity
20242026
collaborators

13 papers

cs.AI2026

Linguistic Monoculture in LLM-Assisted Language Use

Suhas Thejaswi, Juhi Kulshreshta, Lutz Oettershagen

The paper examines how extensive reliance on large language models for drafting text can diminish linguistic variation, modeling authors and models as interacting distributions and…

cs.IT2026

Query-Limited Community Recovery in Stochastic Block Models

Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen +1

We study exact community recovery in the two-community stochastic block model on vertices under limited and noisy access to network data. The learner may query a noisy neighbor…

cs.CL2026

Evaluation of Large Language Models via Coupled Token Generation

Nina Corvelo Benz, Stratis Tsirtsis, Eleni Straitouri +4

State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multip…

cs.CC2026

On the Hardness of Approximation of the Fair k-Center Problem

Suhas Thejaswi

In this work, we study the hardness of approximation of the fair -center problem. In this problem, we are given a set of data points in a metric space that is partitioned into g…

cs.DS2025

Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information

Ameet Gadekar, Aristides Gionis, Suhas Thejaswi +1

We study the problem of fair -committee selection under an egalitarian objective. Given agents partitioned into groups (\eg, demographic quotas), the goal is to aggregat…

cs.LG2025

Towards Human-AI Complementarity in Matching Tasks

Adrian Arnaiz-Rodriguez, Nina Corvelo Benz, Suhas Thejaswi +2

Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthca…