We give relative error coresets for training linear classifiers with a broad class of loss functions, including the logistic loss and hinge loss. Our construction achieves $(1\pm \epsilon)$ relative error with $\tilde O(d \cdot \mu_y(X)^2/\epsilon^2)$ points, where $\mu_y(X)$ is a natural complexity measure of the data matrix $X \in \mathbb{R}^{n \times d}$ and label vector $y \in \{-1,1\}^n$, introduced in Munteanu et al. 2018. Our result is based on subsampling data points with probabilities proportional to their \textit{$\ell_1$ Lewis weights}. It significantly improves on existing theoretical bounds and performs well in practice, outperforming uniform subsampling along with other importance sampling methods. Our sampling distribution does not depend on the labels, so can be used for active learning. It also does not depend on the specific loss function, so a single coreset can be used in multiple training scenarios.
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Fair Classification with Instance-dependent Label Noise
With the widespread use of machine learning systems in our daily lives, it is important to consider fairness as a basic requirement when designing these systems, especially when the systems make life-changing decisions, e.g., \textit{COMPAS} algorithm helps judges decide whether to release an offender. For another thing, due to the cheap but imperfect data collection methods, such as crowdsourcing and web crawling, label noise is ubiquitous, which unfortunately makes fairness-aware algorithms even more prejudiced than fairness-unaware ones, and thereby harmful. To tackle these problems, we provide general frameworks for learning fair classifiers with \textit{instance-dependent label noise}. For statistical fairness notions, we rewrite the classification risk and the fairness metric in terms of noisy data and thereby build robust classifiers. For the causality-based fairness notion, we exploit the internal causal structure of data to model the label noise and \textit{counterfactual fairness} simultaneously. Experimental results demonstrate the effectiveness of the proposed methods on real-world datasets with controllable synthetic label noise.
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- NSF-PAR ID:
- 10391570
- Date Published:
- Journal Name:
- First Conference on Causal Learning and Reasoning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
