10 citations · 26 across the 9 of their papers we have counts for
11 papers
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
Venkat Srinivas, Chenzhang He, Sam Woodmansee +64
The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and language…
Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise
Bo Long, Deepak Agarwal, Jelena Markovic-Voronov +2
The Transformer is the foundational building block of modern AI, yet offers no principled handling of \emph{uncertainty}, which is prevalent in real applications: cold-start tokens…
Sampling for Quality: Training-Free Reward-Guided LLM Decoding via Sequential Monte Carlo
Jelena Markovic-Voronov, Wenhui Zhu, Bo Long +5
We introduce a principled probabilistic framework for reward-guided decoding in large language models, addressing the limitations of standard decoding methods that optimize token-l…
DeText: A Deep Text Ranking Framework with BERT
Weiwei Guo, Xiaowei Liu, Sida Wang +8
Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep un…
Evaluating Fairness Using Permutation Tests
Cyrus DiCiccio, Sriram Vasudevan, Kinjal Basu +2
Machine learning models are central to people's lives and impact society in ways as fundamental as determining how people access information. The gravity of these models imparts a…
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang +1
The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize thei…