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07/12/2023
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Generating unlabeled data has been recently shown to help address the fe...
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05/19/2023
Enriching Disentanglement: Definitions to Metrics
Disentangled representation learning is a challenging task that involves...
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05/04/2023
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Pseudo labeling is a popular and effective method to leverage the inform...
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04/30/2023
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Adversarial contrastive learning (ACL), without requiring labels, incorp...
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10/27/2022
Audio Signal Enhancement with Learning from Positive and Unlabelled Data
Supervised learning is a mainstream approach to audio signal enhancement...
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06/07/2022
The Survival Bandit Problem
We study the survival bandit problem, a variant of the multi-armed bandi...
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06/02/2022
Excess risk analysis for epistemic uncertainty with application to variational inference
We analyze the epistemic uncertainty (EU) of supervised learning in Baye...
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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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11/23/2020
Stable Weight Decay Regularization
Weight decay is a popular regularization technique for training of deep ...
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10/22/2020
Maximum Mean Discrepancy is Aware of Adversarial Attacks
The maximum mean discrepancy (MMD) test, as a representative two-sample ...
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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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06/29/2020
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Ra...
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06/21/2020
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
In continual learning settings, deep neural networks are prone to catast...
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04/14/2020
Learning from Aggregate Observations
We study the problem of learning from aggregate observations where super...
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02/10/2020
Towards Mixture Proportion Estimation without Irreducibility
Mixture proportion estimation (MPE) is a fundamental problem of practica...
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01/11/2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Learning with noisy labels has drawn a lot of attention. In this area, m...
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10/20/2019
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
From two unlabeled (U) datasets with different class priors, we can trai...
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10/03/2019
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Many real world tasks require multiple agents to work together. Multi-ag...
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05/28/2019
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge
We propose an algorithm based on reinforcement learning for solving NP-h...
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02/04/2019
Online Multiclass Classification Based on Prediction Margin for Partial Feedback
We consider the problem of online multiclass classification with partial...
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01/31/2019
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization
We consider the semi-supervised ordinal regression problem, where unlabe...
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01/30/2019
On Possibility and Impossibility of Multiclass Classification with Rejection
We investigate the problem of multiclass classification with rejection, ...
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01/14/2019
How Does Disagreement Benefit Co-teaching?
Learning with noisy labels is one of the most important question in weak...
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05/21/2018
Frank-Wolfe Stein Sampling
In Bayesian inference, the posterior distributions are difficult to obta...
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03/14/2018
On the Ambiguity of Registration Uncertainty
Estimating the uncertainty in image registration is an area of current r...
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02/12/2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
High sensitivity of neural networks against malicious perturbations on i...
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10/19/2017
Binary Classification from Positive-Confidence Data
Reducing labeling costs in supervised learning is a critical issue in ma...
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05/19/2017
