# Loss Functions for Multiset Prediction

arXiv: https://arxiv.org/abs/1711.05246 · HTML: https://mlsanity.com/paper/1711.05246/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2017.

## Built on by (most cited first, 6 of 6)

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- [Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence](https://mlsanity.com/paper/2305.03010/) (2023)
- [Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications](https://mlsanity.com/paper/2103.15330/) (2021)
- [Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction](https://mlsanity.com/paper/2603.24562/) (2026)
- [NEST: Nested Event Stream Transformer for Sequences of Multisets](https://mlsanity.com/paper/2602.00520/) (2026)
- [Jointly Predicting Courses and Grades Using a Transformer-Based Model](https://mlsanity.com/paper/2608.13409/) (2026)

## Abstract (arXiv)

We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging. In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making. The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions. The experiments reveal the effectiveness of the proposed loss function over the others.