139 citations · 199 across the 16 of their papers we have counts for
14 papers · 1 filter
Forecasting Competitions with Correlated Events
Rafael Frongillo, Manuel Lladser, Anish Thilagar +1
Beginning with Witkowski et al. [2022], recent work on forecasting competitions has addressed incentive problems with the common winner-take-all mechanism. Frongillo et al. [2021]…
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…
Surrogate Regret Bounds for Polyhedral Losses
Rafael Frongillo, Bo Waggoner
Surrogate risk minimization is an ubiquitous paradigm in supervised machine learning, wherein a target problem is solved by minimizing a surrogate loss on a dataset. Surrogate regr…
Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
Jessie Finocchiaro, Rafael Frongillo, Bo Waggoner
Given a prediction task, understanding when one can and cannot design a consistent convex surrogate loss, particularly a low-dimensional one, is an important and active area of mac…
Efficient Competitions and Online Learning with Strategic Forecasters
Rafael Frongillo, Robert Gomez, Anish Thilagar +1
Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. Witkowski et al. 2018 identified this problem and proposed ELF, a truthful mechan…
Non-parametric Binary regression in metric spaces with KL loss
Ariel Avital, Klim Efremenko, Aryeh Kontorovich +2
We propose a non-parametric variant of binary regression, where the hypothesis is regularized to be a Lipschitz function taking a metric space to [0,1] and the loss is logarithmic.…