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Observational Multiplicity
Jul 30, 2025Erin George, Deanna Needell, Berk UstunBinary Classification Classification
Department of Mathematics University of California, San Diego La Jolla, CA 92093, USA · Department of Mathematics University of California, Los Angeles Los Angeles, CA 90095, USA · Department of Computer Science & Halıcıoğlu Data Science Institute University of California, San Diego La Jolla, CA 92093, USA
When majority rules, minority loses: bias amplification of gradient descent
May 19, 2025François Bachoc, Jérôme Bolte, Ryan Boustany +1Class-Imbalanced Learning Gradient Descent
LPP · University of Lille · Institut Universitaire de France (IUF) +6
Focus on Likely Classes for Test-Time Prediction
May 2, 2025Johannes SchneiderTest-Time Adaptation Multiclass Classification
Department of Computer Science and Information Systems University of Liechtenstein Vaduz, Liechtenstein
Universality of Benign Overfitting in Binary Linear Classification
Jan 17, 2025Ichiro Hashimoto, Stanislav Volgushev, Piotr ZwiernikNeural Network Generalization Benign Overfitting
Department of Statistical Sciences, University of Toronto
Probabilistic Truly Unordered Rule Sets
Jan 18, 2024Lincen Yang, Matthijs van LeeuwenInterpretable ML Multiclass Classification
LIACS, Leiden University Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
Geometry-Aware Adaptation for Pretrained Models
Jul 23, 2023Nicholas Roberts, Xintong Li, Dyah Adila +4Zero-Shot Learning VLM Adaptation
University of Wisconsin-Madison
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration
Nov 20, 2022Zongyao Lyu, Nolan B. Gutierrez, William J. BeksiProbability Calibration Open-Set Recognition
The University of Texas at Arlington Arlington, TX, USA
Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?
Aug 4, 2021Yang Bai, Kuntara PukthuanthongQuantitative Finance Classification
College of Business and Economics, California State University, Fullerton · Trulaske Sr. College of Business, University of Missouri
Ensemble Learning Based Classification Algorithm Recommendation
Jan 15, 2021Guangtao Wang, Qinbao Song, Xiaoyan Zhu +1Algorithm Selection Ensemble Learning
Department of Computer Science and Technology, Xi’an Jiaotong University, China
Methods to integrate multinormals and compute classification measures
Dec 23, 2020Abhranil Das, Wilson S GeislerMulticlass Classification Classification
Department of Physics, The University of Texas at Austin · Center for Perceptual Systems, The University of Texas at Austin · Center for Theoretical and Computational Neuroscience, The University of Texas at Austin +1
Random Polytope Descriptors
Sep 29, 2020Michael Joswig, Marek Kaluba, Lukas RuffClassification
Technische Universität Berlin, Chair of Discrete Mathematics/Geometry · MPI MiS Leipzig · Adam Mickiewicz University, Poznań, Poland +1
Linear and Quadratic Discriminant Analysis: Tutorial
Jun 1, 2019Benyamin Ghojogh, Mark CrowleyClassification
Department of Electrical and Computer Engineering, Machine Learning Laboratory, University of Waterloo, Waterloo, ON, Canada
Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation
Date pendingAndreas Spilz, Heiko Oppel, Michael MunzHuman Activity Recognition Label Distribution Learning
AI for Sensor Data Analytics Research Group, Ulm University of Applied Sciences, 89081 Ulm, Germany
Video2Reaction: Training Foundation Video Models to Predict Audience Reaction
Date pendingSidong Zhang, Trang Nguyen, Shiv Shankar +4VLM Adaptation Video-Language Models
UMass Amherst 130 Governors Drive, Amherst · Dolby Laboratories 1275 Market Street, San Francisco

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Observational Multiplicity

Erin George, Deanna Needell, Berk Ustun

Department of Mathematics University of California, San Diego La Jolla, CA 92093, USA · Department of Mathematics University of California, Los Angeles Los Angeles, CA 90095, USA · Department of Computer Science & Halıcıoğlu Data Science Institute University of California, San Diego La Jolla, CA 92093, USA

Binary Classification Classification

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Many prediction tasks can admit multiple models that can perform almost equally well. This phenomenon can undermine interpretability and safety when competing models assign conflicting predictions to individuals. In this work, we study how arbitrariness can arise in probabilistic classification tasks as a result of an effect that we call \emph{observational multiplicity}. We discuss how this effect arises in a broad class of practical applications where we learn a classifier to predict probabilities

p_i \in [0,1]

but are given a dataset of observations

y_i \in \{0,1\}

. We propose to evaluate the arbitrariness of individual probability predictions through the lens of \emph{regret}. We introduce a measure of regret for probabilistic classification tasks, which measures how the predictions of a model could change as a result of different training labels. We present a general-purpose method to estimate the regret in a probabilistic classification task. We use our measure to show that regret is often higher for certain groups in the dataset and discuss potential applications of regret. We demonstrate how estimating regret can be used to promote safety in real-world applications by abstention and data collection.

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