Recommender models excel at providing domain-specific item recommendatio...
Although Transformer has achieved great success in natural language proc...
Big models, exemplified by Large Language Models (LLMs), are models typi...
Machine learning has demonstrated remarkable performance over finite
dat...
Sequential user modeling, a critical task in personalized recommender
sy...
Empirical risk minimization (ERM) is a fundamental machine learning para...
Time series remains one of the most challenging modalities in machine
le...
Large language models (LLMs) have achieved significant performance in ma...
Large language models (LLMs) are gaining increasing popularity in both
a...
Federated learning (FL) is an important technique for learning models fr...
Self-training (ST) has come to fruition in language understanding tasks ...
Instruction tuning large language models (LLMs) remains a challenging ta...
The increasing reliance on Large Language Models (LLMs) across academia ...
Embedding models have shown great power in knowledge graph completion (K...
Many real-world graph learning tasks require handling dynamic graphs whe...
Large language models (LLMs) have demonstrated powerful capabilities in ...
Collaborative Filtering (CF) is a widely used and effective technique fo...
Vision-Language models (VLMs) that use contrastive language-image
pre-tr...
While generative modeling has been ubiquitous in natural language proces...
Algorithmic fairness has become an important machine learning problem,
e...
Recent recommender systems have shown remarkable performance by using an...
Federated learning (FL) has emerged as a new paradigm for privacy-preser...
ChatGPT is a recent chatbot service released by OpenAI and is receiving
...
The critical challenge of Semi-Supervised Learning (SSL) is how to
effec...
Sentence summarization shortens given texts while maintaining core conte...
Self-training (ST) has prospered again in language understanding by
augm...
Deep semantic matching aims to discriminate the relationship between
doc...
In this paper, we move towards combining large parametric models with
no...
Semi-supervised learning (SSL) has shown great promise in leveraging
unl...
Pre-trained language models (PLMs) are known to improve the generalizati...
Recently, powerful Transformer architectures have proven superior in
gen...
Domain generalization (DG) aims to learn a generalizable model from mult...
This paper studies learning on text-attributed graphs (TAGs), where each...
Variational Auto-Encoder (VAE) has been widely adopted in text generatio...
Bilingual lexicon induction induces the word translations by aligning
in...
Graph Neural Networks (GNNs) have made tremendous progress in the graph
...
Query-aware webpage snippet extraction is widely used in search engines ...
We present SELOR, a framework for integrating self-explaining capabiliti...
Time series classification is an important problem in real world. Due to...
The distribution shifts between training and test data typically undermi...
Semi-supervised learning (SSL) improves model generalization by leveragi...
Machine learning systems may encounter unexpected problems when the data...
Graph-based collaborative filtering is capable of capturing the essentia...
Deep learning has achieved great success in the past few years. However,...
This paper presents FedX, an unsupervised federated learning framework. ...
The past several years have witnessed Variational Auto-Encoder's superio...
Deep recommender systems jointly leverage the retrieval and ranking
oper...
We consider the problem of personalised news recommendation where each u...
Session-based recommendation (SBR) aims to predict the user next action ...
Session-based recommendation (SBR) aims to predict the user next action ...