arXiv research

Distillation affects some classes more than others, impacting fairness and bias.

ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.

New algorithm offers costless model selection in contextual bandits.

Unified framework suppresses model bias in semi-supervised learning with decoupled sampling control.

Mitigates confirmation bias in SSL by adjusting pseudo labels dynamically.

New proof links initial class bias to DNN trainability, challenging traditional understanding.

Contrastive learning struggles with class collapse and feature suppression, revealing bias towards simpler solutions.

New method quantifies inductive bias for machine learning tasks.

Decision trees can be biased towards minority class, contrary to belief.

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to …

We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the…

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…

Matrix SMD converges to unique solution minimizing Bregman divergence.

In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning…

New findings show DNC is not optimal for deep models, revealing a low-rank bias.

Untrained neural networks can unfairly assign predictions to the same class.

Study reveals class disparities in balanced datasets through spectral imbalance.

This report examines the Pinned AUC metric introduced and highlights some of its limitations. Pinned AUC provides a threshold-agnostic measure of unintended bias in a classification model, inspired by the ROC-AUC metric. However, as we highlight in this report, there are ways that the metric can obscure different kinds…

Gradient descent learns ReLU functions with non-zero bias efficiently.

Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. Adaptation is done on a per-task basis, and retraining is needed when the system configuration changes. The resulting inefficiency in terms of da…

Machine learning algorithms can misrepresent training data, study finds.

Improved sampling from complex distributions with reduced bias.

Bayesian adaptive designs can be biased by active learning, especially with misspecified models.

Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "…

Mirror flow in shallow neural networks shows similar implicit bias to gradient flow, with key differences in curvature penalties.

New method reduces bias in estimating causal effects from discretized variables.

Algorithm corrects bias in classification data.

Active learning method balances bias and variance under class imbalance.

In this paper, we study the accuracy of values aggregated over classes predicted by a classification algorithm. The problem is that the resulting aggregates (e.g., sums of a variable) are known to be biased. The bias can be large even for highly accurate classification algorithms, in particular when dealing with class-…

This work analyzes the maximum-margin bias in quasi-homogeneous neural networks.

Study reveals how neural network architectures bias their learning based on feature directions.

Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks today. Known results show that, in the worst-case, revealing the accuracy of $k$ adaptively chosen classifiers on a data set of size $n$ al…

Paper debiases multiple word embedding biases simultaneously.

Study improves statistical power for detecting algorithmic bias in educational data.

Paper connects sampling and labeling biases in large-output spaces.

Understanding the inductive bias of neural networks is critical to explaining their ability to generalise. Here, for one of the simplest neural networks -- a single-layer perceptron with n input neurons, one output neuron, and no threshold bias term -- we prove that upon random initialisation of weights, the a priori p…

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk, based on theories of decision making under uncertainty which won the 2002 Nobel prize in economics. First, the paper documents inaccuracy…

This work extends implicit bias analysis to multiclass classification using a new loss framework.

New method removes pseudo-label bias for unsupervised domain adaptation.

Proposes a method to learn policies from offline data with reduced bias.

Meta-learning improves performance in stochastic linear bandits.

Visualizes classification results with class maps.

Theory and methods to mitigate omitted variable bias in causal machine learning.

The paper analyzes optimal implicit bias in linear regression for over-parameterized models.

Exactly solvable model reveals how data geometry influences ML bias.

Study shows imbalanced labels can be beneficial but not always in class-imbalanced learning.

Visualizes classification accuracy and label bias in neural nets and trees.

A number of applications (e.g., AI bot tournaments, sports, peer grading, crowdsourcing) use pairwise comparison data and the Bradley-Terry-Luce (BTL) model to evaluate a given collection of items (e.g., bots, teams, students, search results). Past work has shown that under the BTL model, the widely-used maximum-likeli…