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Found 7 papers, 5 papers with code
Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets
In this paper, our goal is to control how frequently a decision support system based on prediction sets may cause harm, by design.
Towards Human-AI Complementarity with Predictions Sets
Then, we show that the problem of finding the optimal prediction sets under which the human experts achieve the highest average accuracy is NP-hard.
Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes
Then, we develop a post-processing algorithm that, given placement decisions made by a default policy on a pool of refugees and their employment outcomes, solves an inverse~matching problem to minimally modify the predictions made by a given classifier.
Prediction-Powered Ranking of Large Language Models
Using pairwise comparisons made by humans in the LMSYS Chatbot Arena platform and pairwise comparisons made by three strong large language models, we empirically demonstrate the effectivity of our framework and show that the rank-sets constructed using only pairwise comparisons by the strong large language models are often inconsistent with (the distribution of) human pairwise preferences.
Diversity-aware clustering: Computational Complexity and Approximation Algorithms
We present parameterized approximation algorithms with approximation ratios $1+ \frac{2}{e}$, $1+\frac{8}{e}$ and $3$ for diversity-aware $k$-median, diversity-aware $k$-means and diversity-aware $k$-supplier, respectively.
Fair Column Subset Selection
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 selected columns.
Finding path motifs in large temporal graphs using algebraic fingerprints
In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we search for temporal paths in the graph that contain the colors specified in the query.
