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09/15/2023
Unified Risk Analysis for Weakly Supervised Learning
Among the flourishing research of weakly supervised learning (WSL), we r...
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08/20/2023
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
We study the real-valued combinatorial pure exploration of the multi-arm...
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08/12/2023
Multi-Label Knowledge Distillation
Existing knowledge distillation methods typically work by imparting the ...
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07/21/2023
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images
Knowledge distillation aims to learn a lightweight student network from ...
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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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06/15/2023
Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class
The combinatorial pure exploration (CPE) in the stochastic multi-armed b...
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06/12/2023
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
Training a classifier exploiting a huge amount of supervised data is exp...
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05/28/2023
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
Label-noise learning (LNL) aims to increase the model's generalization g...
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05/24/2023
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
Distribution shift (DS) may have two levels: the distribution itself cha...
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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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05/16/2023
Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model
The presentation of a moving tactile stimulus to a person's forearm evok...
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05/15/2023
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation
Complementary-label Learning (CLL) is a form of weakly supervised learni...
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05/11/2023
A Category-theoretical Meta-analysis of Definitions of Disentanglement
Disentangling the factors of variation in data is a fundamental concept ...
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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
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks
Adversarial training (AT) is a robust learning algorithm that can defend...
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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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02/28/2023
Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits
Thompson sampling (TS) for the parametric stochastic multi-armed bandits...
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02/08/2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Adversarial contrastive learning (ACL) does not require expensive data a...
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02/06/2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
While leveraging additional training data is well established to improve...
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02/06/2023
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
Dealing with distribution shifts is one of the central challenges for mo...
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02/03/2023
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
In the stochastic multi-armed bandit problem, a randomized probability m...
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12/26/2022
Robust computation of optimal transport by β-potential regularization
Optimal transport (OT) has become a widely used tool in the machine lear...
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11/23/2022
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning
Deep reinforcement learning (DRL) breaks through the bottlenecks of trad...
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11/01/2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Adversarial training (AT) with imperfect supervision is significant but ...
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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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08/03/2022
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
Machine learning systems may encounter unexpected problems when the data...
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07/05/2022
Adapting to Online Label Shift with Provable Guarantees
The standard supervised learning paradigm works effectively when trainin...
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07/04/2022
Learning from Multiple Unlabeled Datasets with Partial Risk Regularization
Recent years have witnessed a great success of supervised deep learning,...
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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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06/06/2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
In label-noise learning, estimating the transition matrix has attracted ...
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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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04/15/2022
Universal approximation property of invertible neural networks
Invertible neural networks (INNs) are neural network architectures with ...
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04/07/2022
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients
Supervised federated learning (FL) enables multiple clients to share the...
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02/22/2022
On the Effectiveness of Adversarial Training against Backdoor Attacks
DNNs' demand for massive data forces practitioners to collect data from ...
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02/07/2022
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests
Non-parametric two-sample tests (TSTs) that judge whether two sets of sa...
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02/01/2022
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
There is a fundamental limitation in the prediction performance that a m...
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01/12/2022
Towards Adversarially Robust Deep Image Denoising
This work systematically investigates the adversarial robustness of deep...
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12/23/2021
Learning with Proper Partial Labels
Partial-label learning is a kind of weakly-supervised learning with inex...
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12/19/2021
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data f...
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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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07/16/2021
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
Ordinary supervised learning is useful when we have paired training data...
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07/11/2021
Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation
Learning from positive and unlabeled (PU) data is an important problem i...
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06/17/2021
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations
In many real-world imitation learning tasks, the demonstrator and the le...
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06/16/2021
Multi-Class Classification from Single-Class Data with Confidences
Can we learn a multi-class classifier from only data of a single class? ...
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06/15/2021
Probabilistic Margins for Instance Reweighting in Adversarial Training
Reweighting adversarial data during training has been recently shown to ...
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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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06/09/2021
Loss function based second-order Jensen inequality and its application to particle variational inference
Bayesian model averaging, obtained as the expectation of a likelihood fu...
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06/01/2021
Instance Correction for Learning with Open-set Noisy Labels
The problem of open-set noisy labels denotes that part of training data ...
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06/01/2021
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
In learning with noisy labels, the sample selection approach is very pop...
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05/31/2021