38 citations · 43 across the 4 of their papers we have counts for
5 papers
Reinforcement Learning Towards Broadly and Persistently Beneficial Models
Akshay V. Jagadeesh, Rahul K. Arora, Khaled Saab +5
As AI systems are deployed across increasingly diverse and high-stakes settings, model alignment must generalize beyond the tasks and domains seen during training. This is especial…
HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats
Rebecca Soskin Hicks, Mikhail Trofimov, Dominick Lim +13
Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited. We introduce HealthBench Pro…
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii +3
In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss.…
Exponential Machines
Alexander Novikov, Mikhail Trofimov, Ivan Oseledets
Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we in…
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov +2
Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Catego…