24 citations · 74 across the 7 of their papers we have counts for
7 papers
Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
In recommendation systems, users interact with only a small fraction of a vast item catalog, producing feedback that is both sparse and noisy. This challenges post-training generat…
Towards Generalizable and Efficient Large-Scale Generative Recommenders
Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya
Generative recommendation models can model user behavior as sequences of events and provide a shared backbone for multiple recommendation tasks. In production, however, pre-trainin…
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual recommendation quality. Existi…
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis
Ko-Jen Hsiao, Kevin S. Xu, Jeff Calder +1
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms…
Social Collaborative Retrieval
Ko-Jen Hsiao, Alex Kulesza, Alfred Hero
Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's p…
Pareto-depth for Multiple-query Image Retrieval
Ko-Jen Hsiao, Jeff Calder, Alfred O. Hero
Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this pa…
Multi-criteria Anomaly Detection using Pareto Depth Analysis
Ko-Jen Hsiao, Kevin S. Xu, Jeff Calder +1
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dis…