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1. Assessing the Benefits of Model Ensembles in Neural Re-ranking for Passage Retrieval

   https://doi.org/10.1007/978-3-030-72240-1_19  

   Borges, Luís; Martins, Bruno; Callan, Jamie (April 2021, Lecture notes in computer science)

   Hiemstra, D.; Moens, MF.; Mothe, J.; Perego, R.; Potthast, M.; Sebastiani, F. (Ed.)

   Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage re-ranking. Starting from relatively standard neural models, we use a previous technique named Fast Geometric Ensembling to generate multiple model instances from particular training schedules, then focusing or attention on different types of approaches for combining the results from the multiple model instances (e.g., averaging the ranking scores, using fusion methods from the IR literature, or using supervised learning-to-rank). Tests with the MS-MARCO dataset show that model ensembling can indeed benefit the ranking quality, particularly with supervised learning-to-rank although also with unsupervised rank aggregation. 

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2. Learning Hierarchical Polynomials with Three-Layer Neural Networks

   Wang, Zihao; Nichani, Eshaan; Lee, Jason D (May 2024, International Conference on Learning Representations)

   We study the problem of learning hierarchical polynomials over the standard Gaussian distribution with three-layer neural networks. We specifically consider target functions of the form where is a degree polynomial and is a degree polynomial. This function class generalizes the single-index model, which corresponds to , and is a natural class of functions possessing an underlying hierarchical structure. Our main result shows that for a large subclass of degree polynomials , a three-layer neural network trained via layerwise gradient descent on the square loss learns the target up to vanishing test error in samples and polynomial time. This is a strict improvement over kernel methods, which require samples, as well as existing guarantees for two-layer networks, which require the target function to be low-rank. Our result also generalizes prior works on three-layer neural networks, which were restricted to the case of being a quadratic. When is indeed a quadratic, we achieve the information-theoretically optimal sample complexity , which is an improvement over prior work (Nichani et al., 2023) requiring a sample size of . Our proof proceeds by showing that during the initial stage of training the network performs feature learning to recover the feature with samples. This work demonstrates the ability of three-layer neural networks to learn complex features and as a result, learn a broad class of hierarchical functions. 

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3. End-to-End Learning for Fair Ranking Systems

   https://doi.org/10.1145/3485447.3512247  

   Kotary, James; Fioretto, Ferdinando; Van Hentenryck, Pascal; Zhu, Ziwei (January 2022, WWW '22: Proceedings of the ACM Web Conference 2022)

   The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some notion of fairness among specified item groups. While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the predicted rankings. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints, while allowing for fine control of the fairness-utility tradeoff. SPOFR is shown to significantly improve on current state-of-the-art fair learning-to-rank systems with respect to established performance metrics. 

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4. Multi-Task Learning For Document Ranking And Query Suggestion

   Wasi Uddin Ahmad, Kai-Wei Chang (April 2018, Sixth International Conference on Learning Representations)

   We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommender tracks users’ query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users’ underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework. 

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5. Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search

   Ahmad, Wasi and (April 2018, Sixth International Conference on Learning Representations)

   We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommender tracks users’ query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users’ underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework. 

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