Categories: FAANG

Location-Invariant Properties of Functions Versus Properties of Distributions: United in Testing but Separated in Verification

A property of functions is called location-invariant (or symmetric) if it can be characterized in terms of the frequencies in which each value occurs in the function, regardless of the locations in which each value occurs. It is known that the (query) complexity of testing location-invariant properties of functions is closely related to the (sample) complexity of testing the (corresponding properties of the) corresponding distributions. The main message of the current work is that this close relationship is not maintained in the context of verification. This holds both when considering…

SimPer: Simple self-supervised learning of periodic targets

July 19, 2023

In "FAANG"

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps

Optimal transport (OT) theory focuses, among all maps that can morph a probability measure onto another, on those that are the "thriftiest", i.e. such that the averaged cost between and its image be as small as possible. Many computational approaches have been proposed to estimate such Monge maps when is…

June 21, 2023

In "FAANG"

When Does Optimizing a Proper Loss Yield Calibration?

Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated. However, typical machine learning models are trained to approximately minimize loss over restricted families of predictors,…

October 3, 2023

In "FAANG"

AI Generated Robotic Content