34 citations · 129 across the 45 of their papers we have counts for
6 papers · 1 filter
Proper losses for discrete generative models
Rafael Frongillo, Dhamma Kimpara, Bo Waggoner
We initiate the study of proper losses for evaluating generative models in the discrete setting. Unlike traditional proper losses, we treat both the generative model and the target…
Consistent Polyhedral Surrogates for Top- Classification and Variants
Jessie Finocchiaro, Rafael Frongillo, Emma Goodwill +1
Top- classification is a generalization of multiclass classification used widely in information retrieval, image classification, and other extreme classification settings. Sever…
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates
Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner
We formalize and study the natural approach of designing convex surrogate loss functions via embeddings, for problems such as classification, ranking, or structured prediction. In…
The Structured Abstain Problem and the Lovász Hinge
Jessie Finocchiaro, Rafael Frongillo, Enrique Nueve
The Lovász hinge is a convex surrogate recently proposed for structured binary classification, in which binary predictions are made simultaneously and the error is judged by a…
Quantum Information Elicitation
Rafael Frongillo
In the classic scoring rule setting, a principal incentivizes an agent to truthfully report their probabilistic belief about some future outcome. This paper addresses the situation…
No-Regret Learning in Games is Turing Complete
Gabriel P. Andrade, Rafael Frongillo, Georgios Piliouras
Games are natural models for multi-agent machine learning settings, such as generative adversarial networks (GANs). The desirable outcomes from algorithmic interactions in these ga…