181 research outputs found
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Semi-supervised learning (SSL) has demonstrated its potential to improve the
model accuracy for a variety of learning tasks when the high-quality supervised
data is severely limited. Although it is often established that the average
accuracy for the entire population of data is improved, it is unclear how SSL
fares with different sub-populations. Understanding the above question has
substantial fairness implications when different sub-populations are defined by
the demographic groups that we aim to treat fairly. In this paper, we reveal
the disparate impacts of deploying SSL: the sub-population who has a higher
baseline accuracy without using SSL (the "rich" one) tends to benefit more from
SSL; while the sub-population who suffers from a low baseline accuracy (the
"poor" one) might even observe a performance drop after adding the SSL module.
We theoretically and empirically establish the above observation for a broad
family of SSL algorithms, which either explicitly or implicitly use an
auxiliary "pseudo-label". Experiments on a set of image and text classification
tasks confirm our claims. We introduce a new metric, Benefit Ratio, and promote
the evaluation of the fairness of SSL (Equalized Benefit Ratio). We further
discuss how the disparate impact can be mitigated. We hope our paper will alarm
the potential pitfall of using SSL and encourage a multifaceted evaluation of
future SSL algorithms.Comment: Published as a conference paper at ICLR 202
Evaluating Fairness Without Sensitive Attributes: A Framework Using Only Auxiliary Models
Although the volume of literature and public attention on machine learning
fairness has been growing significantly, in practice some tasks as basic as
measuring fairness, which is the first step in studying and promoting fairness,
can be challenging. This is because sensitive attributes are often unavailable
due to privacy regulations. The straightforward solution is to use auxiliary
models to predict the missing sensitive attributes. However, our theoretical
analyses show that the estimation error of the directly measured fairness
metrics is proportional to the error rates of auxiliary models' predictions.
Existing works that attempt to reduce the estimation error often require strong
assumptions, e.g. access to the ground-truth sensitive attributes or some form
of conditional independence. In this paper, we drop those assumptions and
propose a framework that uses only off-the-shelf auxiliary models. The main
challenge is how to reduce the negative impact of imperfectly predicted
sensitive attributes on the fairness metrics without knowing the ground-truth
sensitive attributes. Inspired by the noisy label learning literature, we first
derive a closed-form relationship between the directly measured fairness
metrics and their corresponding ground-truth metrics. And then we estimate some
key statistics (most importantly transition matrix in the noisy label
literature), which we use, together with the derived relationship, to calibrate
the fairness metrics. In addition, we theoretically prove the upper bound of
the estimation error in our calibrated metrics and show our method can
substantially decrease the estimation error especially when auxiliary models
are inaccurate or the target model is highly biased. Experiments on COMPAS and
CelebA validate our theoretical analyses and show our method can measure
fairness significantly more accurately than baselines under favorable
circumstances
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
With the recent success of graph convolutional networks (GCNs), they have
been widely applied for recommendation, and achieved impressive performance
gains. The core of GCNs lies in its message passing mechanism to aggregate
neighborhood information. However, we observed that message passing largely
slows down the convergence of GCNs during training, especially for large-scale
recommender systems, which hinders their wide adoption. LightGCN makes an early
attempt to simplify GCNs for collaborative filtering by omitting feature
transformations and nonlinear activations. In this paper, we take one step
further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN),
which skips infinite layers of message passing for efficient recommendation.
Instead of explicit message passing, UltraGCN resorts to directly approximate
the limit of infinite-layer graph convolutions via a constraint loss.
Meanwhile, UltraGCN allows for more appropriate edge weight assignments and
flexible adjustment of the relative importances among different types of
relationships. This finally yields a simple yet effective UltraGCN model, which
is easy to implement and efficient to train. Experimental results on four
benchmark datasets show that UltraGCN not only outperforms the state-of-the-art
GCN models but also achieves more than 10x speedup over LightGCN.Comment: Paper accepted in CIKM'2021. Code available at:
https://github.com/xue-pai/UltraGC
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