2 citations · 4 across the 15 of their papers we have counts for
18 papers
Linguistic Monoculture in LLM-Assisted Language Use
Suhas Thejaswi, Juhi Kulshreshta, Lutz Oettershagen
Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although such assistance can improve clari…
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…
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…
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…
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…
Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms
Ameet Gadekar, Suhas Thejaswi
Capacitated fair-range -clustering generalizes classical -clustering by incorporating both capacity constraints and demographic fairness. In this setting, each facility has a…
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…
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights
Ameet Gadekar, Aristides Gionis, Suhas Thejaswi
Data summarization tasks are often modeled as -clustering problems, where the goal is to choose data points, called cluster centers, that best represent the dataset by minim…
Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes
Seungeon Lee, Nina Corvelo Benz, Suhas Thejaswi +1
Resettlement agencies have started to adopt data-driven algorithmic matching to match refugees to locations using employment rate as a measure of utility. Given a pool of refugees,…
Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets
Eleni Straitouri, Suhas Thejaswi, Manuel Gomez Rodriguez
Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label values to a subset of them, namely…
Towards Human-AI Complementarity with Prediction Sets
Giovanni De Toni, Nastaran Okati, Suhas Thejaswi +2
Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. Rather than providing single-label predictions, t…
Prediction-Powered Ranking of Large Language Models
Ivi Chatzi, Eleni Straitouri, Suhas Thejaswi +1
Large language models are often ranked according to their level of alignment with human preferences -- a model is better than other models if its outputs are more frequently prefer…
Diversity-aware clustering: Computational Complexity and Approximation Algorithms
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti +1
In this work, we study diversity-aware clustering problems where the data points are associated with multiple attributes resulting in intersecting groups. A clustering solution nee…
Fair Column Subset Selection
Antonis Matakos, Bruno Ordozgoiti, Suhas Thejaswi
The problem of column subset selection asks for a subset of columns from an input matrix such that the matrix can be reconstructed as accurately as possible within the span of the…
Clustering with fair-center representation: parameterized approximation algorithms and heuristics
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti +1
We study a variant of classical clustering formulations in the context of algorithmic fairness, known as diversity-aware clustering. In this variant we are given a collection of fa…
Diversity-aware -median : Clustering with fair center representation
Suhas Thejaswi, Bruno Ordozgoiti, Aristides Gionis
We introduce a novel problem for diversity-aware clustering. We assume that the potential cluster centers belong to a set of groups defined by protected attributes, such as ethnici…
Restless reachability problems in temporal graphs
Suhas Thejaswi, Juho Lauri, Aristides Gionis
We study a family of reachability problems under waiting-time restrictions in temporal and vertex-colored temporal graphs. Given a temporal graph and a set of source vertices, we f…
Finding path motifs in large temporal graphs using algebraic fingerprints
Suhas Thejaswi, Aristides Gionis, Juho Lauri
We study a family of pattern-detection problems in vertex-colored temporal graphs. In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we sear…