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Found 27 papers, 0 papers with code
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
Multi-dimensional Fair Federated Learning
Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy.
Federated Causality Learning with Explainable Adaptive Optimization
We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data.
Multi-granularity Causal Structure Learning
Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
Long-tail Cross Modal Hashing
LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities.
Reinforcement Causal Structure Learning on Order Graph
In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size.
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks.
Crowdsourcing with Meta-Workers: A New Way to Save the Budget
Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited.
Meta Cross-Modal Hashing on Long-Tailed Data
Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and then introduces an associative memory module to learn the memory features of samples of the tail classes.
MetaMIML: Meta Multi-Instance Multi-Label Learning
Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances.
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search.
Few-Shot Partial-Label Learning
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label.
Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.
Deep Incomplete Multi-View Multiple Clusterings
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.
Partial Multi-label Learning with Label and Feature Collaboration
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant.
Attention-Aware Answers of the Crowd
However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks).
Multi-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results.
Prototypical Networks for Multi-Label Learning
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.
Active Multi-Label Crowd Consensus
AMCC accounts for the commonality and individuality of workers, and assumes that workers can be organized into different groups.
Cross-modal Zero-shot Hashing
It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.
Weakly-paired Cross-Modal Hashing
FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities.
ActiveHNE: Active Heterogeneous Network Embedding
To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices.
Multi-View Multiple Clustering
It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality.
Ranking-based Deep Cross-modal Hashing
Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions.
Multiple Independent Subspace Clusterings
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.
