Second, we show the first core-set w.r.t. the sum-of-nearest-neighbor distances. Finally, we run several experiments showing the effectiveness of our core-set approach. In particular, we apply constrained diversity maximization to summarize a set of timed messages that takes into account the messages' recency.

However, the most common algorithm used for this problem in practical applications is a natural greedy algorithm.

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While the original algorithm was slow, efficient implementations have been provided since then (e.g., see

Ahmadian et al. [2020] established the study of fairness in hierarchical

We thank the reviewers for their comments. We will fix all minor issues and do not discuss them individually here. We appreciate your concern and will consider other datasets as well. However, particularly focusing on MIL, Krein-LSH has a better approximation factor. Krein-LSH is lossless, i.e., we do not lose anything on the approximation factor except for computing the integral We have a more detailed discussion in lines 68-75.

In this paper, we tackle a fundamental problem in the design of modern ad auctions. We adopt a "Myersonian" approach and study allocation rules that are monotone

In this paper, we study the role of machine-learned predictions in offline NP-hard problems.

The most popular heuristic for solving this problem is Lloyd's algorithm (Lloyd,