Study shows non-systematic bias in customer satisfaction surveys limits data value.
problem Non-systematic bias in customer satisfaction surveys limits data value.
method Used real customer satisfaction survey data of a large retail bank to show the irreducible error and suggest thoughtful survey design methods.
result A thoughtful survey design can reduce non-systematic error in customer satisfaction surveys.
Survey on why deep learning works despite having more parameters than data.
problem Understanding why deep learning algorithms generalize well despite having more parameters than training data.
method Explains the concept of implicit bias and reviews recent research findings.
result Implicit bias is a key factor in deep learning's ability to generalize.
Over the years, several studies have demonstrated that there exist significant disparities in health indicators in the United States population across various groups. Healthcare expense is used as a proxy for health in algorithms that drive healthcare systems and this exacerbates the existing bias. In this work, we foc…
Study measures gender bias in machine translation using multiple reference points.
problem Measuring and identifying gender bias in machine translation.
method Used an optimal non-biased translator, reference points from occupational statistics and survey.
result Found bias against both genders, but more against women, and found occupations have a greater effect than adjectives.
Balance corrects biased survey data for more accurate insights.
problem Bias in survey data leads to inaccurate insights and underperforming models.
method Three steps: bias understanding, weight adjustment, and evaluation.
result Corrected data leads to more accurate ML model training and insights.
Method fuses low and high-resolution data for better health estimates.
problem Improving high-resolution health estimates from mixed data sources.
method Fusion of unbiased low-resolution and potentially biased high-resolution data, learning a distribution consistent with sampling bias.
result Significant reduction in bias in high-resolution estimates.
This survey outlines methods to ensure fairness in machine learning.
problem Mitigating bias and promoting fairness in machine learning applications.
method Organizes approaches into pre-processing, in-processing, and post-processing methods.
result Summarizes open challenges and dilemmas for fairness research.
SaML guides ML models to avoid survey biases.
problem ML models trained on survey data often ignore survey design metadata.
method Nine-step guideline for incorporating survey design metadata in ML lifecycle.
result SaML provides valid population inference from survey data.
The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An…
The study examines methods to correct measurement error in nutritional epidemiology studies.
problem Measurement error in nutritional studies leads to biased and underconfident estimates.
method The article reviews various bias-correction models for exposure variables in nutritional epidemiology.
result Bias-correction methods are essential for accurate inference in nutritional studies.
In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyse general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, w…
The main aim of this paper is to inspect the properties of survey based on households inflation expectations, conducted by Reserve Bank of India. It is theorized that the respondents answers are exaggerated by extreme response bias. Latent class analysis has been hailed as a promising technique for studying measurement…
New model reduces bias in cosmic shear measurements.
problem Bias in cosmic shear measurements due to non-well-defined ellipticity.
method Hybrid physical and deep learning Hierarchical Bayesian Model.
result Unbiased estimate of shear on realistic galaxies.
User surveys for Quality of Experience (QoE) are a critical source of information. In addition to the common "star rating" used to estimate Mean Opinion Score (MOS), more detailed survey questions (problem tokens) about specific areas provide valuable insight into the factors impacting QoE. This paper explores two aspe…
PPI uses predictions and weighting to infer from partially labeled data.
problem Valid inference with partially labeled data.
method Combines model-based predictions with bias correction from labeled data, using Horvitz-Thompson and Hájek corrections.
result IPW-adjusted PPI with estimated propensities performs similarly to known-probability case.
The paper examines bias in ML models using the Adult dataset.
problem Understanding and mitigating bias in machine learning models.
method Mathematical framework for fair learning, Disparate Impact index, and evaluation of bias reduction methods.
result Some common bias reduction methods are ineffective.
Deep learning models outperform MICE in large survey imputation but with hyperparameter tuning.
problem Comparing deep learning and MICE for missing data imputation in large surveys.
method Extensive simulation studies comparing four machine learning-based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation networks, and multiple imputation using denoising autoencoders.
result MICE with classification trees consistently outperforms deep learning methods in terms of bias, mean squared error, and coverage.
New methods reduce bias in machine learning predictions for causal inference without extra data.
problem Machine learning predictions from satellite data shrink toward the mean, leading to biased causal estimates.
method Two post-hoc correction methods: Linear Calibration Correction (LCC) and Tweedie's approach, reduce shrinkage-induced bias.
result Tweedie's method yields nearly unbiased treatment-effect estimates, enabling multiple trials with a single map.
New method corrects selection bias in complex models.
problem Selection bias in statistical studies leading to systematic distortions.
method Amortized Bayesian inference with neural posterior estimation.
result Recover well-calibrated posterior distributions across diverse selection mechanisms.
Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2…
Fuzzy Forests reduces feature space in high-dimensional survey data.
problem High-dimensional and highly correlated datasets in social science.
method Fuzzy Forests algorithm, a variant of Random Forests.
result Partisan polarization was the strongest factor in the 2020 presidential election.
ResUNet-CMB neural network reconstructs CMB effects from noisy data.
problem Reconstructing CMB anisotropies from noisy data.
method Convolutional neural network (ResUNet-CMB) for simultaneous reconstruction of lensing and reionization.
result ResUNet-CMB outperforms quadratic estimators at low noise levels and avoids lensing-induced bias.
Study highlights fairness issues in travel behavior prediction models.
problem Ethical challenges in travel behavior analysis using machine learning.
method Operationalized computational fairness by equality of opportunity; compared DNN and DCM; introduced absolute correlation regularization.
result Both DNN and DCM predict disparities across social groups, with DNN outperforming DCM in prediction disparities.
Survey on biases in image analysis for industrial safety.
problem Bias in machine learning algorithms affects industrial safety-critical applications.
method Survey and analysis of recent advances in bias detection and mitigation.
result Need for new methods to detect and mitigate biases in image analysis for safety-critical applications.
The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced Deep Echo State Network (DeepESN) model opened the way to an extremely efficient approach for designing deep neura…
Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.
problem Understanding and quantifying uncertainty in ML and DL for risk-sensitive applications.
method Structured review of literature, categorizing uncertainty, assessing uncertainty quantification techniques.
result Broadened scope of uncertainty discussion and updated DL uncertainty quantification methods.
This text is a survey on cross-validation. We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family. For the risk estimation problem, we compute the bias (which can also be …
This paper reviews and introduces measures for data representativity in AI systems.
problem Ensuring appropriate inference from data in AI systems.
method Defined and evaluated three measurable concepts of representativity.
result Contrasts between coverage and distribution representativity are crucial for AI system building.
This paper reviews various sampling methods from statistics and machine learning.
problem Addressing sampling methods in statistics and machine learning.
method Explains and reviews simple random sampling, bootstrapping, stratified sampling, cluster sampling, multistage sampling, network sampling, snowball sampling, and sampling from cumulative distribution function.
result Summarizes characteristics, pros, and cons of different sampling methods.
Bias in training data affects diagnostic algorithms' performance.
problem Bias in training data leads to biased predictions in diagnostic algorithms.
method Survey of MICCAI 2018 proceedings, analysis of a skin lesions dataset, adversarial training setup.
result Classifier performance varies significantly between subgroups based on age and sex.
Algorithm improves binary classification of biased grouped data.
problem Improving binary classification for biased, grouped data.
method Assumes partition-projected class-conditional invariance across groups and derives a semi-supervised algorithm to learn a group-aware classifier.
result Demonstrates improved area under the ROC curve compared to baselines.
As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms herein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms' intrinsic variance, …
Machine learning detects survey validity from user behavior.
problem Detecting valid responses in web surveys.
method Uses mouse activity and machine learning models (LSTM, HMM).
result Predicts survey validity without analyzing specific answers.
Survey updates knowledge on homogeneous Einstein spaces.
problem Understanding homogeneous Einstein spaces.
method Building on previous surveys by Wang and Lauret.
result Current state of homogeneous Einstein spaces.
This paper explores neural models to improve modeling of Hawkes process intensity functions.
problem Traditional Hawkes process intensity function's parametrized kernel function biases future event predictions.
method Uses neural models to model the kernel function of Hawkes process intensity function.
result Neural models can better capture future event characteristics using past events data.
StatEcoNet models species distribution using neural networks to correct observation errors.
problem Correcting observation errors in wildlife surveys for accurate species distribution modeling.
method StatEcoNet integrates a graphical generative model with neural networks to address SDM challenges.
result StatEcoNet outperforms traditional methods on simulated and real datasets.
Survey on submanifolds in nearly Kähler spaces.
problem Understanding submanifolds in nearly Kähler spaces.
method No specific method mentioned, just a survey of existing results.
result Summarizes existing results on submanifolds in nearly Kähler spaces.
Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…
Survey of minimal genus problem progress.
problem Finding the smallest genus surface in 3-manifolds.
method Survey and review of existing research.
result Summarizes recent advancements in the minimal genus problem.
The paper improves Lasso inference methods for survey data.
problem Improving inference methods for survey data.
method Extends Lasso inferential methods to survey data.
result Establishes asymptotic validity of inference procedures in survey environments.
Survey on L^2-invariants for 3-manifolds.
problem Understanding L^2-invariants for 3-manifolds.
method Survey and focus on L^2-invariants for 3-manifolds.
result Survey results on L^2-invariants for 3-manifolds.
Study uses neural fields to improve geophysical inversions by reducing artifacts.
problem Improving geophysical inversions by reducing artifacts and improving model recovery.
method Employing neural fields for test-time learning in geophysical inversions.
result Test-time learning with neural fields eliminates unwanted artifacts in recovered models.
Proposes methods to learn from biased samples, ensuring robust decision rules.
problem Learning from biased samples can lead to poor performance in real-world applications.
method Modeling sampling bias, using distributionally robust optimization and deep learning.
result Proposes a method to minimize worst-case risk under various test distributions.
Survey on DDVV-type inequalities, their history, and recent developments.
problem None explicitly stated; focuses on surveying existing work.
method None explicitly stated; focuses on surveying existing work.
result Survey of DDVV-type inequalities and their history and recent developments.
Survey of recent measures of association, including a new coefficient.
problem Exploring new measures of association in statistics.
method Survey and introduction of a new correlation coefficient.
result Proposed a new extension of the correlation coefficient to standard Borel spaces.
The paper tests the credibility of public and private surveys using linear regression and differential privacy.
problem Ensuring the validity of data analysis results from sample surveys using linear regression.
method Designing an algorithm to test the credibility of surveys and extending it to handle LDP.
result The algorithm achieves optimal estimation error bound for ℓ1 linear regression and reduces sample complexity. Survey on stated skein algebras and their representations.
problem None explicitly stated in the abstract.
method None explicitly stated in the abstract.
result None explicitly stated in the abstract.
Using AI predictions as data can mislead inference, study shows.
problem Misleading inference when using AI predictions instead of real data.
method Characterized statistical challenges and reviewed methods for IPD.
result High predictive accuracy doesn't ensure valid inference.