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Found 27 papers, 7 papers with code
Complementary to Multiple Labels: A Correlation-Aware Correction Approach
Moreover, theoretical findings reveal that calculating a transition matrix from label correlations in \textit{multi-labeled CLL} (ML-CLL) needs multi-labeled data, while this is unavailable for ML-CLL.
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Key to our approach is a new interpretation of decision tree algorithms for positive and negative data as \emph{recursive greedy risk minimization algorithms}.
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer
Existing defenses are mainly built upon the observation that the backdoor trigger is usually of small size or affects the activation of only a few neurons.
A Boosting Algorithm for Positive-Unlabeled Learning
This is because AdaPU learns a weak classifier and its weight using a weighted positive-negative (PN) dataset with some negative data weights $-$ the dataset is derived from the original PU data, and the data weights are determined by the current weighted classifier combination, but some data weights are negative.
Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image
In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process.
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.
On the Robustness of Average Losses for Partial-Label Learning
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Particle-hole asymmetric superconducting coherence peaks in overdoped cuprates
To elucidate the superconductor to metal transition at the end of superconducting dome, the overdoped regime has stepped onto the center stage of cuprate research recently.
Superconductivity
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Collaborative filtering has been widely used in recommender systems.
Pointwise Binary Classification with Pairwise Confidence Comparisons
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision
To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones.
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
May there be a new sample selection method that can outperform the latest importance reweighting method in the deep learning age?
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations.
Clipped Matrix Completion: A Remedy for Ceiling Effects
On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-rank completion.
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries.
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.
Ranked #7 on
Learning with noisy labels
on CIFAR-10N-Random3
Active Feature Acquisition with Supervised Matrix Completion
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance.
CUR Algorithm for Partially Observed Matrices
In particular, the proposed algorithm computes the low rank approximation of the target matrix based on (i) the randomly sampled rows and columns, and (ii) a subset of observed entries that are randomly sampled from the matrix.
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning
In standard matrix completion theory, it is required to have at least $O(n\ln^2 n)$ observed entries to perfectly recover a low-rank matrix $M$ of size $n\times n$, leading to a large number of observations when $n$ is large.
