1.3k citations · 2.3k across the 69 of their papers we have counts for
18 papers · 1 filter
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Shengjia Zhao, Michael P. Kim, Roshni Sahoo +2
When facing uncertainty, decision-makers want predictions they can trust. A machine learning provider can convey confidence to decision-makers by guaranteeing their predictions are…
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
In many real-world problems, we want to infer some property of an expensive black-box function , given a budget of function evaluations. One example is budget constrained gl…
Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song, Conor Durkan, Iain Murray +1
Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matchin…
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Shengjia Zhao, Stefano Ermon
Decision makers often need to rely on imperfect probabilistic forecasts. While average performance metrics are typically available, it is difficult to assess the quality of individ…
A Framework for Sample Efficient Interval Estimation with Control Variates
Shengjia Zhao, Christopher Yeh, Stefano Ermon
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samp…
Individual Calibration with Randomized Forecasting
Shengjia Zhao, Tengyu Ma, Stefano Ermon
Machine learning applications often require calibrated predictions, e.g. a 90\% credible interval should contain the true outcome 90\% of the times. However, typical definitions of…