activity
20232026
most citedmSAM: Micro-Batch-Averaged Sharpness-Aware Minimization

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2026

An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

Lars Hertel, Gaurav Srivastava, Syed Ali Naqvi +21

LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed SR), a tra…

cs.LG2025

Generative Sequential Notification Optimization via Multi-Objective Decision Transformers

Borja Ocejo, Ruofan Wang, Ke Liu +7

Notifications are an important communication channel for delivering timely and relevant information. Optimizing their delivery involves addressing complex sequential decision-makin…

cs.LG2025

From Features to Transformers: Redefining Ranking for Scalable Impact

Fedor Borisyuk, Lars Hertel, Ganesh Parameswaran +14

We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modifie…

cs.LG2024

A Precise Characterization of SGD Stability Using Loss Surface Geometry

Gregory Dexter, Borja Ocejo, Sathiya Keerthi +3

Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent…

stat.ML2023★ 1 cited

mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization

Kayhan Behdin, Qingquan Song, Aman Gupta +7

Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…