16 papers
Integrating Local and Global Entropy for Uncertainty Quantification in LLMs
Johanne Medina, Tianyi Zhou, Keivin Isufaj +2
Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment. Existing methods rely predominantly on token-level signals,…
Efficient Banzhaf-Based Data Valuation for -Nearest Neighbors Classification
Guangyi Zhang, Lutz Oettershagen, Lixu Wang +1
Data valuation, the task of quantifying the contribution of individual data points to model performance, has emerged as a fundamental challenge in machine learning. Game-theoretic…
Khatri-Rao Clustering for Data Summarization
Martino Ciaperoni, Collin Leiber, Aristides Gionis +1
As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to add…
Sequential Diversification with Provable Guarantees
Honglian Wang, Sijing Tu, Aristides Gionis
Diversification is a useful tool for exploring large collections of information items. It has been used to reduce redundancy and cover multiple perspectives in information-search s…
Streaming Stochastic Submodular Maximization with On-Demand User Requests
Honglian Wang, Sijing Tu, Lutz Oettershagen +1
We explore a novel problem in streaming submodular maximization, inspired by the dynamics of news-recommendation platforms. We consider a setting where users can visit a news websi…
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…