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

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

#classifiers #supervised #quantification

A Comparative Evaluation of Quantification Methods

'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

An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification

'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

Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes

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 …

Dropsafe

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

A Billion Grains of Rice - Alec Muffett - Medium

A few days ago there was a tremendous kerfuffle regarding the Kim Phuc “Napalm Girl” photo, and Facebook “censorship”. Over on Twitter, Dan Hon posted a not-terrible tweetstorm, raging against…

Medium

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

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

#classifiers #replicability #hypothesis

Estimating the Replication Probability of Significant Classification Benchmark Experiments

'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

An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants

'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

Non-splitting Neyman-Pearson Classifiers

'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

Generalization and Stability of Interpolating Neural Networks with Minimal Width

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
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