Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages

R Agrawal, A Gupta, Y Prabhu, M Varma - Proceedings of the 22nd …, 2013 - dl.acm.org
Recommending phrases from web pages for advertisers to bid on against search engine
queries is an important research problem with direct commercial impact. Most approaches …

Label embedding trees for large multi-class tasks

S Bengio, J Weston, D Grangier - Advances in neural …, 2010 - proceedings.neurips.cc
Multi-class classification becomes challenging at test time when the number of classes is
very large and testing against every possible class can become computationally infeasible …

Fast and balanced: Efficient label tree learning for large scale object recognition

J Deng, S Satheesh, A Berg… - Advances in Neural …, 2011 - proceedings.neurips.cc
We present a novel approach to efficiently learn a label tree for large scale classification with
many classes. The key contribution of the approach is a technique to simultaneously …

A no-regret generalization of hierarchical softmax to extreme multi-label classification

M Wydmuch, K Jasinska, M Kuznetsov… - Advances in neural …, 2018 - proceedings.neurips.cc
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small
subset of relevant labels chosen from an extremely large pool of possible labels. Large label …

Extreme f-measure maximization using sparse probability estimates

K Jasinska, K Dembczynski… - International …, 2016 - proceedings.mlr.press
We consider the problem of (macro) F-measure maximization in the context of extreme multi-
label classification (XMLC), ie, multi-label classification with extremely large label spaces …

An easy-to-hard learning paradigm for multiple classes and multiple labels

W Liu, IW Tsang, M Klaus-Robert - Journal of Machine Learning …, 2017 - jmlr.org
Many applications, such as human action recognition and object detection, can be
formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most …

Model-powered conditional independence test

R Sen, AT Suresh, K Shanmugam… - Advances in neural …, 2017 - proceedings.neurips.cc
We consider the problem of non-parametric Conditional Independence testing (CI testing)
for continuous random variables. Given iid samples from the joint distribution $ f (x, y, z) $ of …

On missing labels, long-tails and propensities in extreme multi-label classification

E Schultheis, M Wydmuch, R Babbar… - Proceedings of the 28th …, 2022 - dl.acm.org
The propensity model introduced by Jain et al has become a standard approach for dealing
with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper …

[HTML][HTML] Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: a large-scale benchmarking study

T Mortier, AD Wieme, P Vandamme… - Computational and …, 2021 - Elsevier
Today machine learning methods are commonly deployed for bacterial species identification
using MALDI-TOF mass spectrometry data. However, most of the studies reported in …

[PDF][PDF] Classifier cascades and trees for minimizing feature evaluation cost

Z Xu, MJ Kusner, KQ Weinberger, M Chen… - The Journal of Machine …, 2014 - jmlr.org
Abstract Machine learning algorithms have successfully entered industry through many real-
world applications (eg, search engines and product recommendations). In these …