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Noise Tolerance Under Risk Minimization

https://doi.org/10.1109/tsmcb.2012.2223460

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Noisy Label Learning With Provable Consistency for a Wider Family of Losses

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A Parametrical Model for Instance-Dependent Label Noise

inferred← citeshttps://doi.org/10.1109/tpami.2024.3360623openalex-snapshot

A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels

inferred← citeshttps://doi.org/10.1109/tps-isa56441.2022.00042openalex-snapshot

Understanding Model Extraction Games

inferred← citeshttps://doi.org/10.1109/tsmc.2015.2452894openalex-snapshot

Supply Chain Systems Coordination With Multiple Risk Sensitive Retail Buyers

inferred← citeshttps://doi.org/10.1109/wacv.2018.00138openalex-snapshot

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

inferred← citeshttps://doi.org/10.1109/wacv56688.2023.00392openalex-snapshot

Adaptive Sample Selection for Robust Learning under Label Noise

inferred← citeshttps://doi.org/10.1117/12.2624150openalex-snapshot

A method of adaptive filtering and learning based on label noise

inferred← citeshttps://doi.org/10.1117/12.2636819openalex-snapshot

Research on loss function for adaptive label thresholding algorithm

inferred← citeshttps://doi.org/10.1145/3341105.3374013openalex-snapshot

Analysis of label noise in graph-based semi-supervised learning

inferred← citeshttps://doi.org/10.1145/3371158.3371169openalex-snapshot

Robust Learning of Multi-Label Classifiers under Label Noise

inferred← citeshttps://doi.org/10.1145/3394171.3413952openalex-snapshot

Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph

inferred← citeshttps://doi.org/10.1145/3442188.3445887openalex-snapshot

Mitigating Bias in Set Selection with Noisy Protected Attributes

inferred← citeshttps://doi.org/10.1145/3442188.3445915openalex-snapshot

Fair Classification with Group-Dependent Label Noise

inferred← citeshttps://doi.org/10.1145/3531146.3533232openalex-snapshot

On the Existence of Simpler Machine Learning Models

inferred← citeshttps://doi.org/10.1145/3551349.3560418openalex-snapshot

MalWhiteout: Reducing Label Errors in Android Malware Detection

inferred← citeshttps://doi.org/10.1145/3605098.3635936openalex-snapshot

A Heterogeneous Ensemble Method for Handling Class Noise in Supervised Machine Learning

inferred← citeshttps://doi.org/10.1145/3631977openalex-snapshot

Attack as Detection: Using Adversarial Attack Methods to Detect Abnormal Examples

inferred← citeshttps://doi.org/10.1145/3648696openalex-snapshot

Loss Relaxation Strategy for Noisy Facial Video-based Automatic Depression Recognition

inferred← citeshttps://doi.org/10.1145/3652583.3658084openalex-snapshot

GB RAIN : Combating Textual Label Noise by Granular-ball based Robust Training

inferred← citeshttps://doi.org/10.1149/2754-2726/ac75f5openalex-snapshot

Review–A Survey of Learning from Noisy Labels

inferred← citeshttps://doi.org/10.1162/neco_a_01502openalex-snapshot

The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

inferred← citeshttps://doi.org/10.1186/s12544-021-00516-zopenalex-snapshot

Transport behavior-mining from smartphones: a review

inferred← citeshttps://doi.org/10.1214/16-ejs1193openalex-snapshot

Classification with asymmetric label noise: Consistency and maximal denoising

inferred← citeshttps://doi.org/10.1214/22-ejs1987openalex-snapshot

Binary classification with corrupted labels

inferred← citeshttps://doi.org/10.1371/journal.pone.0235502openalex-snapshot

Evolution and impact of bias in human and machine learning algorithm interaction

inferred← citeshttps://doi.org/10.14722/diss.2020.23003openalex-snapshot

Poisoning Attacks on Federated Learning-based IoT Intrusion Detection System

inferred← citeshttps://doi.org/10.1504/ijitca.2022.124372openalex-snapshot

Federated learning for intrusion detection in IoT security: a hybrid ensemble approach

inferred← citeshttps://doi.org/10.1587/transinf.2021edp7035openalex-snapshot

Learning from Noisy Complementary Labels with Robust Loss Functions

inferred← citeshttps://doi.org/10.1609/aaai.v30i1.10293openalex-snapshot

Risk Minimization in the Presence of Label Noise

inferred← citeshttps://doi.org/10.1609/aaai.v31i1.10894openalex-snapshot

Robust Loss Functions under Label Noise for Deep Neural Networks

inferred← citeshttps://doi.org/10.1609/aaai.v31i1.11177openalex-snapshot

Species Distribution Modeling of Citizen Science Data as a Classification Problem with Class-Conditional Noise

inferred← citeshttps://doi.org/10.1609/aaai.v32i1.11641openalex-snapshot

A General Formulation for Safely Exploiting Weakly Supervised Data

inferred← citeshttps://doi.org/10.1609/aaai.v33i01.33014870openalex-snapshot

Sparse Reject Option Classifier Using Successive Linear Programming

inferred← citeshttps://doi.org/10.1609/aaai.v35i11.17221openalex-snapshot

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

inferred← citeshttps://doi.org/10.1609/aaai.v35i11.17222openalex-snapshot

Learning with Group Noise

inferred← citeshttps://doi.org/10.1609/aaai.v35i13.17364openalex-snapshot

Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

inferred← citeshttps://doi.org/10.1609/aaai.v36i2.20085openalex-snapshot

Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation

inferred← citeshttps://doi.org/10.1609/aaai.v38i14.29516openalex-snapshot

Robust Loss Functions for Training Decision Trees with Noisy Labels

inferred← citeshttps://doi.org/10.1609/aaai.v38i17.29885openalex-snapshot

Mitigating the Impact of False Negative in Dense Retrieval with Contrastive Confidence Regularization

inferred← citeshttps://doi.org/10.1613/jair.1.15819openalex-snapshot

USN: A Robust Imitation Learning Method against Diverse Action Noise

inferred← citeshttps://doi.org/10.1631/fitee.2200514openalex-snapshot

Robust cross-modal retrieval with alignment refurbishment

inferred← citeshttps://doi.org/10.18297/etd/3241openalex-snapshot

Studying and handling iterated algorithmic biases in human and machine learning interaction.

inferred← citeshttps://doi.org/10.18653/v1/n19-1328openalex-snapshot

An Effective Label Noise Model for DNN Text Classification

inferred← citeshttps://doi.org/10.2139/ssrn.5139331openalex-snapshot

Uncertainty-Aware Cross Entropy for Robust Learning with Noisy Labels

inferred← citeshttps://doi.org/10.21528/cbic2021-161openalex-snapshot

How do loss functions impact the performance of graph neural networks?

inferred← citeshttps://doi.org/10.22215/etd/2020-14143openalex-snapshot

Text Classification with Noisy Class Labels

inferred← citeshttps://doi.org/10.23919/cje.2022.00.040openalex-snapshot

Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted Knowledge Distillation

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