Algorithms and Theory
Google’s mission presents many exciting algorithmic and optimization challenges across different product areas including Search, Ads, Social, and Google Infrastructure. These include optimizing internal systems such as scheduling the machines that power the numerous computations done each day, as well as optimizations that affect core products and users, from online allocation of ads to page-views to automatic management of ad campaigns, and from clustering large-scale graphs to finding best paths in transportation networks. Other than employing new algorithmic ideas to impact millions of users, Google researchers contribute to the state-of-the-art research in these areas by publishing in top conferences and journals.
Recent Publications
Leveraging Function Space Aggregation for Federated Learning at Scale
Karolina Dziugaite
Nikita Dhawan
Transactions on Machine Learning Research (2024)
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Hugo Yèche
Alizée Pace
Gunnar Rätsch
Bernhard Schölkopf
The Twelfth International Conference on Learning Representations (2024)
Media Mix Model Calibration With Bayesian Priors
Mike Wurm
Brenda Price
Ying Liu
research.google (2024)
First Passage Percolation with Queried Hints
Yiheng Shen
Ali Sinop
Kritkorn Karntikoon
Aaron Schild
AISTATS (2024)
