Machine Learning and Knowledge Discovery in Databases: Research Track

Table of Contents

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   Book Overview
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   Chapter 1 Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
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   Chapter 2 DCID: Deep Canonical Information Decomposition
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   Chapter 3 Negative Prototypes Guided Contrastive Learning for Weakly Supervised Object Detection
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   Chapter 4 Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
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   Chapter 5 Make a Long Image Short: Adaptive Token Length for Vision Transformers
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   Chapter 6 Graph Rebasing and Joint Similarity Reconstruction for Cross-Modal Hash Retrieval
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   Chapter 7 ARConvL: Adaptive Region-Based Convolutional Learning for Multi-class Imbalance Classification
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   Chapter 8 Binary Domain Generalization for Sparsifying Binary Neural Networks
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    Chapter 9 Efficient Hyperdimensional Computing
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    Chapter 10 Rényi Divergence Deep Mutual Learning
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    Chapter 11 Is My Neural Net Driven by the MDL Principle?
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    Chapter 12 Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression
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    Chapter 13 Learning Distinct Features Helps, Provably
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    Chapter 14 Continuous Depth Recurrent Neural Differential Equations
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    Chapter 15 Mitigating Algorithmic Bias with Limited Annotations
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    Chapter 16 $$\mathrm FG^2AN$$ : Fairness-Aware Graph Generative Adversarial Networks
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    Chapter 17 Targeting the Source: Selective Data Curation for Debiasing NLP Models
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    Chapter 18 Fairness in Multi-Task Learning via Wasserstein Barycenters
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    Chapter 19 REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training
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    Chapter 20 How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning
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    Chapter 21 Towards Practical Federated Causal Structure Learning
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    Chapter 22 Triplets Oversampling for Class Imbalanced Federated Datasets
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    Chapter 23 Learning Fast and Slow: Towards Inclusive Federated Learning
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    Chapter 24 Practical and General Backdoor Attacks Against Vertical Federated Learning
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    Chapter 25 Not All Tasks Are Equal: A Parameter-Efficient Task Reweighting Method for Few-Shot Learning
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    Chapter 26 Boosting Generalized Few-Shot Learning by Scattering Intra-class Distribution
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    Chapter 27 vMF Loss: Exploring a Scattered Intra-class Hypersphere for Few-Shot Learning
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    Chapter 28 Meta-HRNet: A High Resolution Network for Coarse-to-Fine Few-Shot Classification
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    Chapter 29 MuSE: A Multi-scale Emotional Flow Graph Model for Empathetic Dialogue Generation
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    Chapter 30 Posterior Consistency for Missing Data in Variational Autoencoders
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    Chapter 31 KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
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    Chapter 32 Learning Data Representations with Joint Diffusion Models
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    Chapter 33 MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
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    Chapter 34 Cold-Start Multi-hop Reasoning by Hierarchical Guidance and Self-verification
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    Chapter 35 Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response Planning
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    Chapter 36 Attentive Multi-Layer Perceptron for Non-autoregressive Generation
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    Chapter 37 MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders Are Better Dense Retrievers
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    Chapter 38 Duplicate Multi-modal Entities Detection with Graph Contrastive Self-training Network
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    Chapter 39 Graph Contrastive Representation Learning with Input-Aware and Cluster-Aware Regularization
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    Chapter 40 Temporal Graph Representation Learning with Adaptive Augmentation Contrastive
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    Chapter 41 Hierarchical Graph Contrastive Learning