5 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
CLT and Edgeworth Expansion for m-out-of-n Bootstrap Estimators of The Studentized Median
Imon Banerjee, Sayak Chakrabarty
The m-out-of-n bootstrap, originally proposed by Bickel, Gotze, and Zwet (1992), approximates the distribution of a statistic by repeatedly drawing m subsamples (with m much smalle…