Time: 4:30-5:30 p.m.
Date: Wednesday, March 11, 2026
Speaker: Robert Kleinberg
Title: Prediction as a Service
Abstract: Machine learning models can be trained to predict probabilities of uncertain events. A standard application of this paradigm requires obtaining training data, training a model to optimize the utility of a decision maker or agent, and deploying the model to make predictions optimized for that agent. An alternative is to train an agent-agnostic “prediction service” to forecast probabilities of events, and let different agents best-respond to the forecasts. This alternative, first put forward by Gopalan et al. in 2022, is known as "omniprediction". It has clear advantages in terms of model reuse and privacy, but what is lost in terms of efficiency? Can omnipredictors be trained to provide predictions that are nearly as trustworthy as if each agent used the same amount of training data to optimize their own utility? We will describe some settings in which, surprisingly, the answer is yes. Underlying these results is a new measure of forecast calibration called "proper calibration" that is weak enough to be achievable using only logarithmically more samples than the easiest statistical learning tasks, yet strong enough to provide near-optimal guarantees on agents’ regret.
This is joint work with Princewill Okoroafor, Michael P. Kim, Renato Paes Leme, Jon Schneider, and Yifeng Teng.
Bio: Robert Kleinberg is a Professor of Computer Science at Cornell University and a part-time Research Scientist at Google. His research concerns algorithms and their applications to machine learning, economics, networking, and other areas. Prior to receiving his doctorate from MIT in 2005, Kleinberg spent three years at Akamai Technologies; he and his co-workers received the 2018 SIGCOMM Networking Systems Award for pioneering the first Internet content delivery network. He is a Fellow of the ACM and a recipient of the ACM SIGecom Mid-Career Award for advancing the understanding of on-line learning and decision problems and their application to mechanism design.