Dejing Dou

Out-of-Town Recommendation with Travel Intention Modeling

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town …

Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios

2022 · arXiv (Cornell University)

Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and querying their labels from oracle/annotators. However, existing AL sampling strategies …

Federated Fingerprint Learning with Heterogeneous Architectures

2022 · 2022 IEEE International Conference on Data Mining (ICDM)

Recent studies on federated learning (FL) have sought to solve the system heterogeneity issue by designing customized local models for different clients. However, public dataset introduction, sensitive information exchange, non-trivial computational cost, or particular architecture …

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning

2024 · arXiv (Cornell University)

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, …

On Adversarial Examples for Character-Level Neural Machine Translation

2018 · arXiv (Cornell University)

Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of …