Beginning with Witkowski et al. [2022], recent work on forecasting
compe...
Constant-function market makers (CFMMs), such as Uniswap, are automated
...
We initiate the study of proper losses for evaluating generative models ...
We formalize and study the natural approach of designing convex surrogat...
We consider a principal-agent problem where the agent may privately choo...
We consider design of monetary mechanisms for two-sided matching. Mechan...
Inspired by Aumann's agreement theorem, Scott Aaronson studied the amoun...
Surrogate risk minimization is an ubiquitous paradigm in supervised mach...
This note investigates functions from ℝ^d to ℝ∪{±∞} that satisfy axioms ...
Winner-take-all competitions in forecasting and machine-learning suffer ...
Given a prediction task, understanding when one can and cannot design a
...
We study the computation of equilibria in prediction markets in perhaps ...
We investigate the sparse linear contextual bandit problem where the
par...
In a classical online decision problem, a decision-maker who is trying t...
We formalize and study the natural approach of designing convex surrogat...
Machine learning has recently enabled large advances in artificial
intel...
In this work we study loss functions for learning and evaluating probabi...
We study an online classification problem with partial feedback in which...
Recent work introduced loss functions which measure the error of a predi...
There are now several large scale deployments of differential privacy us...
Bandit learning is characterized by the tension between long-term explor...
We study an online linear classification problem, in which the data is
g...
Prediction markets are well-studied in the case where predictions are
pr...
We design mechanisms for online procurement of data held by strategic ag...