1 citations · 3 across the 4 of their papers we have counts for
5 papers
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction
Sejoon Oh, Fernando Amat Gil, Evan Smothers +11
Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a s…
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy
Will LeVine, Benjamin Pikus, Jacob Phillips +3
As deep neural networks become adopted in high-stakes domains, it is crucial to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely…
Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models
Will LeVine, Benjamin Pikus, Pranav Raja +1
Calibration of deep learning models is crucial to their trustworthiness and safe usage, and as such, has been extensively studied in supervised classification models, with methods…
Control Variates for Slate Off-Policy Evaluation
Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil +1
We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems a…
Off-Policy Evaluation of Slate Policies under Bayes Risk
Nikos Vlassis, Fernando Amat Gil, Ashok Chandrashekar
We study the problem of off-policy evaluation for slate bandits, for the typical case in which the logging policy factorizes over the slots of the slate. We slightly depart from th…