'A Comparative Evaluation of Quantification Methods', by Tobias Schumacher, Markus Strohmaier, Florian Lemmerich.

http://jmlr.org/papers/v26/21-0241.html

#classifiers #supervised #quantification

'An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification', by Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski.

http://jmlr.org/papers/v25/24-0268.html

#adversarial #regularization #classifiers

'Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes', by Su Jia, Fatemeh Navidi, Viswanath Nagarajan, R. Ravi.

http://jmlr.org/papers/v25/23-1484.html

#adaptive #classifiers #optimal

Cost of false positives | Kellan Elliott-McCrea: Blog
https://alecmuffett.com/article/110781
#OnlineHarms #OnlineSafetyAct #classifiers #ofcom

Cost of false positives | Kellan Elliott-McCrea: Blog

Kevin Marks (q.v.) introduced me to Kellan’s Paradox of False Positives in Social Media, which predates the themes I explored in Billion Grains of Rice by 5+ years:

Imagine you’ve got a near perfect model for detecting spammers on Twitter. Say [that] Joe is (presumably hyperbolically) claiming 99% accuracy for his model. And for the moment we’ll imagine he is right. Even at 99% accuracy, that means this algorithm is going to be incorrectly flagging roughly 2 million tweets per day as spam that are actually perfectly legitimate.

https://laughingmeme.org//2011/07/23/cost-of-false-positives/

Via: https://bsky.app/profile/kevinmarks.com/post/3lefwdts3n225

⊞

#classifiers #ofcom #onlineHarms #onlineSafetyAct

'Estimating the Replication Probability of Significant Classification Benchmark Experiments', by Daniel Berrar.

http://jmlr.org/papers/v25/24-0158.html

#classifiers #replicability #hypothesis

'An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants', by Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri.

http://jmlr.org/papers/v25/22-1367.html

#classifiers #ensembles #en

'Non-splitting Neyman-Pearson Classifiers', by Jingming Wang, Lucy Xia, Zhigang Bao, Xin Tong.

http://jmlr.org/papers/v25/22-0795.html

#classifiers #classifier #classification

'Generalization and Stability of Interpolating Neural Networks with Minimal Width', by Hossein Taheri, Christos Thrampoulidis.

http://jmlr.org/papers/v25/23-0422.html

#classifiers #generalization #minimization

Then came Severi et al.'s "Poisoning Network Flow #Classifiers", investigating the challenging scenario of clean-label #poisoning where the adversary's capabilities are constrained to tampering only with the #TrainingData. (https://www.acsac.org/2023/program/final/s156.html) 3/4

'Fairness guarantees in multi-class classification with demographic parity', by Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu.

http://jmlr.org/papers/v25/23-0322.html

#fairness #classifiers #classification

'Margin-Based Active Learning of Classifiers', by Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice.

http://jmlr.org/papers/v25/22-1127.html

#classifiers #classes #algorithms

'Classification with Deep Neural Networks and Logistic Loss', by Zihan Zhang, Lei Shi, Ding-Xuan Zhou.

http://jmlr.org/papers/v25/22-0049.html

#classifiers #deepen #classification

'Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data', by Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini.

http://jmlr.org/papers/v25/23-0121.html

#classifiers #classifier #labeling

'A Multilabel Classification Framework for Approximate Nearest Neighbor Search', by Ville Hyvönen, Elias Jääsaari, Teemu Roos.

http://jmlr.org/papers/v25/23-0286.html

#classification #classifiers #classifier

'Random Feature Amplification: Feature Learning and Generalization in Neural Networks', by Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett.

http://jmlr.org/papers/v24/22-1132.html

#classifiers #neurons #relu

'Lifted Bregman Training of Neural Networks', by Xiaoyu Wang, Martin Benning.

http://jmlr.org/papers/v24/22-0934.html

#autoencoders #classifiers #denoising

'Statistical Comparisons of Classifiers by Generalized Stochastic Dominance', by Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin.

http://jmlr.org/papers/v24/22-0902.html

#classifiers #comparisons #randomization

'Interpretable and Fair Boolean Rule Sets via Column Generation', by Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei.

http://jmlr.org/papers/v24/22-0880.html

#boolean #classifiers #fairness

'Random Forests for Change Point Detection', by Malte Londschien, Peter Bühlmann, Solt Kovács.

http://jmlr.org/papers/v24/22-0512.html

#changeforest #classifier #classifiers