Cover Image

ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Borchers, Conrad, Baker, Ryan S.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning G.3
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA
by: Borchers, Conrad
Published: (2025)

Replicable Distribution Testing
by: Diakonikolas, Ilias, et al.
Published: (2025)

Random Models for Fuzzy Clustering Similarity Measures
by: DeWolfe, Ryan, et al.
Published: (2023)

FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
by: Yasuno, Takato
Published: (2026)

Conditioning Diffusions Using Malliavin Calculus
by: Pidstrigach, Jakiw, et al.
Published: (2025)

Beyond Point Estimates: Distributional Uncertainty in Machine Learning Performance Evaluation
by: Lehmann, Christoph, et al.
Published: (2025)

Variational Search Distributions
by: Steinberg, Daniel M., et al.
Published: (2024)

Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities
by: Bialokozowicz, Lilian W.
Published: (2022)

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study
by: Gabriel, Franck, et al.
Published: (2025)

Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
by: Švábenský, Valdemar, et al.
Published: (2024)

A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
by: Liu, Kara, et al.
Published: (2026)

Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem
by: Popov, Roumen Nikolaev
Published: (2024)

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
by: Das, Abhinav, et al.
Published: (2025)

Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks
by: Papagiannouli, Katerina, et al.
Published: (2026)

Identifying Information from Observations with Uncertainty and Novelty
by: Prijatelj, Derek S., et al.
Published: (2025)

How Does the Pretraining Distribution Shape In-Context Learning? Task Selection, Generalization, and Robustness
by: Azizian, Waïss, et al.
Published: (2025)

Variational Autoencoder Kernel Interpretation and Selection for Classification
by: Mendonça, Fábio, et al.
Published: (2022)

Reweighting Improves Conditional Risk Bounds
by: Zhang, Yikai, et al.
Published: (2025)

Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology
by: Lam, Ka Chun, et al.
Published: (2023)

Unity Forests: Improving Interaction Modelling and Interpretability in Random Forests
by: Hornung, Roman, et al.
Published: (2026)

Fairmetrics: An R package for group fairness evaluation
by: Smith, Benjamin, et al.
Published: (2025)

The MAPS Algorithm: Fast model-agnostic and distribution-free prediction intervals for supervised learning
by: Salnikov, Daniel, et al.
Published: (2024)

Evaluating the Impact of Data Augmentation on Predictive Model Performance
by: Švábenský, Valdemar, et al.
Published: (2024)

Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
by: Yasuno, Takato
Published: (2026)

Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting
by: Lade, Ankit, et al.
Published: (2026)

Prediction-Augmented Trees for Reliable Statistical Inference
by: Kher, Vikram, et al.
Published: (2025)

Active Learning for Manifold Gaussian Process Regression
by: Cheng, Yuanxing, et al.
Published: (2025)

Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
by: Kang, Lulu, et al.
Published: (2025)

ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias
by: Zhang, Zhibin, et al.
Published: (2026)

Predictive Hotspot Mapping for Data-driven Crime Prediction
by: Sriram, Karthik, et al.
Published: (2026)

List-Decodable Regression via Expander Sketching
by: Pourali, Herbod, et al.
Published: (2025)

Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach
by: Bodik, Juraj
Published: (2026)

Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
by: Jullien, Sami, et al.
Published: (2023)

A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
by: Cohen-Addad, Vincent, et al.
Published: (2024)

Scalable Gaussian process inference via neural feature maps
by: Stephenson, Anthony
Published: (2026)

$whittlehurst$: A Python package implementing Whittle's likelihood estimation of the Hurst exponent
by: Csanády, Bálint, et al.
Published: (2025)

Neural parameter calibration and uncertainty quantification for epidemic forecasting
by: Gaskin, Thomas, et al.
Published: (2023)

Causal Discovery in Semi-Stationary Time Series
by: Gao, Shanyun, et al.
Published: (2024)

Causal Discovery-Driven Change Point Detection in Time Series
by: Gao, Shanyun, et al.
Published: (2024)

Byzantine-tolerant distributed learning of finite mixture models
by: Zhang, Qiong, et al.
Published: (2024)