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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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9 results for “XMC”

Extreme Multi-label Classification from Aggregated Labelscs.LG

New algorithm for XMC from aggregated labels.

problem Finding relevant labels for inputs from a large label universe.

method Developed a scalable algorithm to impute individual labels from group labels.

result Advantages over existing approaches in XMC and MIML tasks.

multi-label classification multi-instance learning aggregated labels XMC MIML

2020-04-01abs ↗pdf ↗

Label Disentanglement in Partition-based Extreme Multilabel Classificationstat.ML

This paper tackles multi-modal label disentanglement in partition-based XMC.

problem Existing partition-based XMC methods create mutually exclusive clusters, which is sub-optimal for multi-modal labels.

method Formulates label assignment as an optimization problem to maximize precision rates, creating flexible and overlapped label clusters.

result Successfully disentangles multi-modal labels, leading to state-of-the-art results on XMC benchmarks.

Label Disentanglement Partition-based XMC Multi-modal Labels Precision Rates Optimization

2021-06-24abs ↗pdf ↗

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classificationcs.LG

XR-Transformer accelerates XMC by recursively fine-tuning on multi-resolution objectives.

problem Efficiently classifying texts with large label sets.

method Recursive multi-resolution fine-tuning of transformers.

result XR-Transformer achieves 20x faster training time and 54% Precision@1 on Amazon-3M.

Transformer Multi-resolution Fine-tuning Extreme multi-label Text Classification

2021-10-01abs ↗pdf ↗

Taming Pretrained Transformers for Extreme Multi-label Text Classificationcs.LG

We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on Amazon.com and the labels could be product categories. XMC is an important yet challenging problem in t…

2019-05-07abs ↗pdf ↗

Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classificationcs.LG

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space…

2019-04-17abs ↗pdf ↗

CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classificationcs.LG

CascadeXML improves multi-resolution learning for XMC with transformer features.

problem Learning subset labels from millions of choices with trade-offs between performance and computation.

method End-to-end multi-resolution learning pipeline using transformer multi-layer architecture.

result Significantly outperforms existing approaches on benchmark datasets.

Transformer Multi-resolution Extreme Multi-label Classification Multi-layer Architecture Feature Representation

2022-10-29abs ↗pdf ↗

Unbiased Loss Functions for Extreme Classification With Missing Labelsstat.ML

New loss functions improve extreme classification with missing labels.

problem Large number of infrequent labels and missing labels in XMC.

method Derive unbiased loss functions for XMC, incorporating them into existing algorithms.

result Significant improvement in extreme classification performance (up to 20%) over existing methods.

Extreme classification Missing labels Unbiased loss functions Hinge-loss Squared-hinge-loss

2020-07-01abs ↗pdf ↗

Unbiased Loss Functions for Multilabel Classification with Missing Labelscs.LG

This paper tackles unbiased loss functions for multilabel classification with missing labels.

problem Missing labels in multilabel classification tasks, especially in extreme multi-label classification (XMC).

method Derives unbiased estimators for multilabel reductions, including non-decomposable ones, and addresses increased variance with convex upper-bounds.

result Switching to unbiased estimators can alter the bias-variance trade-off and may require stronger regularization.

multilabel classification missing labels unbiased loss functions XMC propensity-scored precision

2021-09-23abs ↗pdf ↗

Learning from eXtreme Bandit Feedbackstat.ML

New method reduces bias in learning from large action spaces using selective importance sampling.

problem Learning from large-scale recommendation systems with bandit feedback and supervised labels.

method Selective Importance Sampling (sIS) and Policy Optimization for eXtreme Models (POXM) algorithm.

result POXM method significantly outperforms existing methods in learning from bandit feedback on XMC tasks.

bandit feedback extreme models importance sampling policy optimization recommendation systems

2020-09-27abs ↗pdf ↗