2 citations · 4 across the 15 of their papers we have counts for
6 papers · 1 filter
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…