More Like this

1. Fairness in Ranking, Part I: Score-based Ranking

   https://doi.org/10.1145/3533379  

   Zehlike, Meike; Yang, Ke; Stoyanovich, Julia (May 2022, ACM Computing Surveys)

   In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs. In this first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In the second part of this survey, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods. 

   more » « less
2. Memristive Data Ranking

   https://doi.org/10.1109/HPCA51647.2021.00045  

   Prasad, Ananth Krishna; Rezaalipour, Morteza; Dehyadegari, Masoud; Bojnordi, Mahdi Nazm (February 2021, IEEE International Symposium on High-Performance Computer Architecture (HPCA))
3. Ranking Neural Checkpoints

   Yandong Li, Xuhui Jia (January 2021, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   null (Ed.)
4. Robust Ranking Explanations

   Chen, Chao; Guo, Chenghua; Ma, Guixiang; Zeng, Ming; Zhang, Xi; Xie, Sihong (July 2023, Workshop on Interpretable Machine Learning in Healthcare at ICML 2023)

   Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using lp norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the R2ET algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy. 

   more » « less
5. Accelerated Spectral Ranking

   Agarwal, Arpit; Patil, Prathamesh; Agarwal, Shivani (January 2018, Proceedings of the 35th International Conference on Machine Learning)

   The problem of rank aggregation from pairwise and multiway comparisons has a wide range of implications, ranging from recommendation systems to sports rankings to social choice. Some of the most popular algorithms for this problem come from the class of spectral ranking algorithms; these include the rank centrality (RC) algorithm for pairwise comparisons, which returns consistent estimates under the Bradley-Terry-Luce (BTL) model for pairwise comparisons (Negahban et al., 2017), and its generalization, the Luce spectral ranking (LSR) algorithm, which returns consistent estimates under the more general multinomial logit (MNL) model for multiway comparisons (Maystre & Grossglauser, 2015). In this paper, we design a provably faster spectral ranking algorithm, which we call accelerated spectral ranking (ASR), that is also consistent under the MNL/BTL models. Our accelerated algorithm is achieved by designing a random walk that has a faster mixing time than the random walks associated with previous algorithms. In addition to a faster algorithm, our results yield improved sample complexity bounds for recovery of the MNL/BTL parameters: to the best of our knowledge, we give the first general sample complexity bounds for recovering the parameters of the MNL model from multiway comparisons under any (connected) comparison graph (and improve significantly over previous bounds for the BTL model for pairwise comparisons). We also give a message-passing interpretation of our algorithm, which suggests a decentralized distributed implementation. Our experiments on several real-world and synthetic datasets confirm that our new ASR algorithm is indeed orders of magnitude faster than existing algorithms. 

   more » « less