# MICO: Selective Search with Mutual Information Co-training

arXiv: https://arxiv.org/abs/2209.04378 · HTML: https://mlsanity.com/paper/2209.04378/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sparse, Dense, and Attentional Representations for Text Retrieval](https://mlsanity.com/paper/2005.00181/) (2020)
- [Formal Limitations on the Measurement of Mutual Information](https://mlsanity.com/paper/1811.04251/) (2018)
- [Learning Discrete Representations via Information Maximizing Self-Augmented Training](https://mlsanity.com/paper/1702.08720/) (2017)
- [Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction](https://mlsanity.com/paper/1804.07849/) (2018)
- [To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers](https://mlsanity.com/paper/2205.00235/) (2022)
- [Information Theoretic Co-Training](https://mlsanity.com/paper/1802.07572/) (2018)
- [Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information](https://mlsanity.com/paper/2004.03991/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Dual-View Approach to Classifying Radiology Reports by Co-Training](https://mlsanity.com/paper/2406.05995/) (2024)

## Abstract (arXiv)

In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a Mutual Information CO-training framework for selective search with minimal supervision using the search logs. After training, MICO does not only cluster the documents, but also routes unseen queries to the relevant clusters for efficient retrieval. In our empirical experiments, MICO significantly improves the performance on multiple metrics of selective search and outperforms a number of existing competitive baselines.