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02/25/2023
Complementary to Multiple Labels: A Correlation-Aware Correction Approach
Complementary label learning (CLL) requires annotators to give irrelevan...
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10/16/2022
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
The need to learn from positive and unlabeled data, or PU learning, aris...
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08/13/2022
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer
Backdoor attacks have been shown to be a serious security threat against...
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05/19/2022
A Boosting Algorithm for Positive-Unlabeled Learning
Positive-unlabeled (PU) learning deals with binary classification proble...
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05/09/2022
Single-Image 3D Face Reconstruction under Perspective Projection
In 3D face reconstruction, orthogonal projection has been widely employe...
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12/17/2021
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Actuated by the growing attention to personal healthcare and the pandemi...
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11/10/2021
On Designing a Two-stage Auction for Online Advertising
For the scalability of industrial online advertising systems, a two-stag...
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09/29/2021
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
The goal of multi-label learning (MLL) is to associate a given instance ...
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06/11/2021
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
In online advertising, auto-bidding has become an essential tool for adv...
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06/11/2021
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classificatio...
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12/05/2020
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mec...
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10/05/2020
Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classi...
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09/03/2020
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great...
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07/17/2020
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, wh...
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06/29/2020
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their tar...
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02/19/2020
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning is one of the important weakly supervised learnin...
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01/29/2019
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
In the early history of positive-unlabeled (PU) learning, the sample sel...
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09/28/2018
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
It is challenging to train deep neural networks robustly on the industri...
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09/13/2018
Clipped Matrix Completion: a Remedy for Ceiling Effects
We consider the recovery of a low-rank matrix from its clipped observati...
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05/23/2018
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
We consider a challenging multi-label classification problem where both ...
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04/18/2018
Co-sampling: Training Robust Networks for Extremely Noisy Supervision
Training robust deep networks is challenging under noisy labels. Current...
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02/15/2018