Decision Boundary Analysis

Follow this topic Ask about this topic

Latest papers

8 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning
Oct 12, 2026Alireza Rahmansetayesh, Amirhossein Yousefiramandi, Maxwell Spiridonov +3Decision Boundary Analysis Forward-Forward Learning
Department of Computer Science, University of California, Irvine, Irvine, CA, USA · Independent Researcher
Dynamics to decision: A mathematical theory of Lyapunov spectra and decision boundaries in deep classifiers
Sep 30, 2026Shirin Panahi, Amirhossein Nazerian, Ali PezeshkiDynamical Systems Multilayer Perceptrons
Department of Electrical and Computer Engineering Colorado State University Fort Collins, Colorado, USA · Department of Mechanical Engineering Colorado State University Fort Collins, Colorado, USA
"Where Can I Trust You?": Boundary-Aware Evaluation of Surrogate Fidelity
Sep 26, 2026Jackson EshbaughDecision Boundary Analysis Surrogate Modeling
Department of Computer Science Lafayette College Easton, PA, USA
A Geometric Theory of Decision Boundaries in Structured Markov Decision Processes
Sep 16, 2026Fredy PokouMarkov Decision Processes Decision-Making under Uncertainty
MRE, INOCS
A Geometric View of Counterfactual Behavior: Interaction of Boundary Proximity and Local Support
Jun 2, 2026Ioanna Gemou, Matteo Gamba, Randall Balestriero +1Counterfactual Evaluation Counterfactual Explanations
Brown University
Empirical Evidence for Simply Connected Decision Regions in Image Classifiers
May 7, 2026Arjhun Swaminathan, Mete AkgünClassification Image Classification
Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML), University of Tübingen. · Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen.
ShapDBM: Exploring Decision Boundary Maps in Shapley Space
Mar 23, 2026Luke Watkin, Daniel Archambault, Alex TeleaDecision Boundary Analysis Interpretable ML
School of Computing, Newcastle University, UK · Department of Information and Computing Science, Utrecht University, Netherlands
Understanding the geometry of deep learning with decision boundary volume
Mar 16, 2026Matthew Burfitt, Jacek Brodzki, Pawel DłotkoDecision Boundary Analysis Neural Network Generalization
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications (BIMSA) · Mathematical Sciences, University of Southampton · Dioscuri Centre for Topological Data Analysis, Institute of Mathematics, Polish Academy of Sciences

Oct 12, 2026J/K move · Enter open · S save

Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning

Alireza Rahmansetayesh, Amirhossein Yousefiramandi, Maxwell Spiridonov, Sumit Raut+2

Department of Computer Science, University of California, Irvine, Irvine, CA, USA · Independent Researcher

Decision Boundary Analysis Forward-Forward Learning Local Learning Classification

Open paper AskPDF arXiv

Forward-Forward learning replaces end-to-end backpropagation with local, layer-wise training objectives. Its variants are evaluated primarily by classification accuracy, often measured with linear probes trained on their representations. We show that this evaluation can over-credit the learning method: a linear probe on the representations of an untrained convolutional neural network already reaches accuracy well above chance. Moreover, a single accuracy value does not reveal which datapoints become correctly classified after training and which remain misclassified. To examine what learning contributes beyond the random, untrained baseline, we present an analysis methodology built on the Boundary Proximity Index (BPI), a multiclass margin normalized by a class-centroid reference. We apply our analysis to seven Forward-Forward variants and related layer-local methods and find that initially misclassified points near the probe's decision boundary generally have higher rescue rates than those farther inside incorrect decision regions. Rescue rates vary substantially across methods, with some correcting a substantial fraction of these deeply embedded points. Together, these analyses offer a way to evaluate representation learning beyond accuracy and identify the rescue of points far inside incorrect decision regions as a target for improving local learning methods.