Publications (9)
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning
Yuhan Liu, Lin Ning, Neo Wu +5
User sequence modeling is crucial for modern large-scale recommendation systems, as it enables the extraction of informative representations of users and items from their historica…
Mixed Federated Learning: Joint Decentralized and Centralized Learning
Sean Augenstein, Andrew Hard, Lin Ning +4
Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixe…
Learning Federated Representations and Recommendations with Limited Negatives
Lin Ning, Karan Singhal, Ellie X. Zhou +1
Deep retrieval models are widely used for learning entity representations and recommendations. Federated learning provides a privacy-preserving way to train these models without re…
UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
Chao Wang, Neo Wu, Lin Ning +5
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user info…
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts
Lin Ning, Harsh Lara, Meiqi Guo +1
Parameter-efficient fine-tuning techniques like Low-Rank Adaptation (LoRA) have revolutionized the adaptation of large language models (LLMs) to diverse tasks. Recent efforts have…
What Do We Mean by Generalization in Federated Learning?
Honglin Yuan, Warren Morningstar, Lin Ning +1
Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus gene…