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
Nikita Dhawan
Karolina Dziugaite
Transactions on Machine Learning Research (2024)
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace
Hugo Yèche
Bernhard Schölkopf
Gunnar Rätsch
The Twelfth International Conference on Learning Representations (2024)
Media Mix Model Calibration With Bayesian Priors
Mike Wurm
Brenda Price
Ying Liu
research.google (2024)
Data Exchange Markets via Utility Balancing
Aditya Bhaskara
Sungjin Im
Kamesh Munagala
Govind S. Sankar
WebConf (2024)
