8 citations · 16 across the 5 of their papers we have counts for
5 papers
Improving FIM Code Completions via Context & Curriculum Based Learning
Hitesh Sagtani, Rishabh Mehrotra, Beyang Liu
Fill-in-the-Middle (FIM) models play a vital role in code completion tasks, leveraging both prefix and suffix context to provide more accurate and contextually relevant suggestions…
AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations
Jan Hartman, Rishabh Mehrotra, Hitesh Sagtani +5
In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant. Connecting the task of providing code recommendations in multiple formats to…
Learning-to-Rank with Nested Feedback
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in the Learning-to-Ran…
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
Olivier Jeunen, Hitesh Sagtani, Himanshu Doi +7
Practitioners who wish to build real-world applications that rely on ranking models, need to decide which modelling paradigm to follow. This is not an easy choice to make, as the r…
Ad-load Balancing via Off-policy Learning in a Content Marketplace
Hitesh Sagtani, Madan Jhawar, Rishabh Mehrotra +1
Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and re…