9 papers
CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
Randeep Bhatia, Nikos Papadis, Murali Kodialam +2
We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL). In CFL, clients are naturally grouped into clusters based on thei…
Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
Riddhiman Bhattacharyya, Sayak Chakrabarty, Imon Banerjee
Contextual MDPs are powerful tools with wide applicability in areas from biostatistics to machine learning. However, specializing them to offline datasets has been challenging due…
Is Sliding Window All You Need? An Open Framework for Long-Sequence Recommendation
Sayak Chakrabarty, Souradip Pal
Long interaction histories are central to modern recommender systems, yet training with long sequences is often dismissed as impractical under realistic memory and latency budgets.…
Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study
Charlotte S. Alexander, Shane Storks, Souradip Pal +4
Letters of recommendation (LoRs) can carry patterns of implicitly gendered language that can inadvertently influence downstream decisions, e.g. in hiring and admissions. In this wo…
PixRec: Leveraging Visual Context for Next-Item Prediction in Sequential Recommendation
Sayak Chakrabarty, Souradip Pal
Large Language Models (LLMs) have recently shown strong potential for usage in sequential recommendation tasks through text-only models, which combine advanced prompt design, contr…
Time-Constrained Recommendations: Reinforcement Learning Strategies for E-Commerce
Sayak Chakrabarty, Souradip Pal
Unlike traditional recommendation tasks, finite user time budgets introduce a critical resource constraint, requiring the recommender system to balance item relevance and evaluatio…