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Found 20 papers, 11 papers with code
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches.
Fair Classifiers Without Fair Training: An Influence-Guided Data Sampling Approach
A fair classifier should ensure the benefit of people from different groups, while the group information is often sensitive and unsuitable for model training.
Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Given the cost and difficulty of cleaning these datasets by humans, we introduce a systematic framework for evaluating the credibility of datasets, identifying label errors, and evaluating the influence of noisy labels in the curated language data, specifically focusing on unsafe comments and conversation classification.
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning.
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
Our theoretical analyses show that directly using proxy models can give a false sense of (un)fairness.
To Aggregate or Not? Learning with Separate Noisy Labels
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e. g., via crowdsourcing).
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
We observe that tasks with lower-quality features fail to meet the anchor-point or clusterability condition, due to the coexistence of both uninformative and informative representations.
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
These observations require us to rethink the treatment of noisy labels, and we hope the availability of these two datasets would facilitate the development and evaluation of future learning with noisy label solutions.
Mitigating Memorization of Noisy Labels via Regularization between Representations
Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs).
Detecting Corrupted Labels Without Training a Model to Predict
In this paper, from a more data-centric perspective, we propose a training-free solution to detect corrupted labels.
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited.
A Good Representation Detects Noisy Labels
In this paper, given good representations, we propose a universally applicable and training-free solution to detect noisy labels.
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points.
Image Classification with Human Noise
Image Classification with Label Noise
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A Second-Order Approach to Learning with Instance-Dependent Label Noise
We first provide evidences that the heterogeneous instance-dependent label noise is effectively down-weighting the examples with higher noise rates in a non-uniform way and thus causes imbalances, rendering the strategy of directly applying methods for class-dependent label noise questionable.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
Federated Bandit: A Gossiping Approach
Motivated by the proposal of federated learning, we aim for a solution with which agents will never share their local observations with a central entity, and will be allowed to only share a private copy of his/her own information with their neighbors.
Policy Learning Using Weak Supervision
Most existing policy learning solutions require the learning agents to receive high-quality supervision signals such as well-designed rewards in reinforcement learning (RL) or high-quality expert demonstrations in behavioral cloning (BC).
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
This high-quality sample sieve allows us to treat clean examples and the corrupted ones separately in training a DNN solution, and such a separation is shown to be advantageous in the instance-dependent noise setting.
Image Classification with Label Noise
Learning with noisy labels
Online optimal task offloading with one-bit feedback
In this paper, we investigate a stochastic task offloading model and propose a multi-armed bandit framework to formulate this model.
Learn and Pick Right Nodes to Offload
An effective task offloading strategy is needed to utilize the computational resources efficiently.
