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Found 22 papers, 0 papers with code
Forecasting Competitions with Correlated Events
We initiate the study of forecasting competitions for correlated events.
Proper losses for discrete generative models
The characterization rules out a loss whose expectation is the cross-entropy between the target distribution and the model.
Consistent Polyhedral Surrogates for Top-$k$ Classification and Variants
For the proposed hinge-like surrogates that are convex (i. e., polyhedral), we apply the recent embedding framework of Finocchiaro et al. (2019; 2022) to determine the prediction problem for which the surrogate is consistent.
The Structured Abstain Problem and the Lovász Hinge
The Lov\'asz hinge is a convex surrogate recently proposed for structured binary classification, in which $k$ binary predictions are made simultaneously and the error is judged by a submodular set function.
No-Regret Learning in Games is Turing Complete
Games are natural models for multi-agent machine learning settings, such as generative adversarial networks (GANs).
Surrogate Regret Bounds for Polyhedral Losses
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.
Graphical Economies with Resale
Kakade, Kearns, and Ortiz (KKO) introduce a graph-theoretic generalization of the classic Arrow--Debreu (AD) exchange economy.
Unifying lower bounds on prediction dimension of convex surrogates
The convex consistency dimension of a supervised learning task is the lowest prediction dimension $d$ such that there exists a convex surrogate $L : \mathbb{R}^d \times \mathcal Y \to \mathbb R$ that is consistent for the given task.
Learning in Matrix Games can be Arbitrarily Complex
In this paper we show that, in a strong sense, this dynamic complexity is inherent to games.
Efficient Competitions and Online Learning with Strategic Forecasters
Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives.
Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
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 machine learning research.
An Embedding Framework for Consistent Polyhedral Surrogates
Conversely, we show how to construct a consistent polyhedral surrogate for any given discrete loss.
Convex Elicitation of Continuous Properties
A property or statistic of a distribution is said to be elicitable if it can be expressed as the minimizer of some loss function in expectation.
Multi-Observation Regression
Recent work introduced loss functions which measure the error of a prediction based on multiple simultaneous observations or outcomes.
Multi-Observation Elicitation
We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously.
Eliciting Categorical Data for Optimal Aggregation
Our model generalizes both categories and enables the joint exploration of optimal elicitation and aggregation.
Convergence Analysis of Prediction Markets via Randomized Subspace Descent
However, little is known about rates and guarantees for the convergence of these sequential mechanisms, and two recent papers cite this as an important open question. In this paper we show how some previously studied prediction market trading models can be understood as a natural generalization of randomized coordinate descent which we call randomized subspace descent (RSD).
On Elicitation Complexity
Elicitation is the study of statistics or properties which are computable via empirical risk minimization.
A Market Framework for Eliciting Private Data
We propose a mechanism for purchasing information from a sequence of participants. The participants may simply hold data points they wish to sell, or may have more sophisticated information; either way, they are incentivized to participate as long as they believe their data points are representative or their information will improve the mechanism's future prediction on a test set. The mechanism, which draws on the principles of prediction markets, has a bounded budget and minimizes generalization error for Bregman divergence loss functions. We then show how to modify this mechanism to preserve the privacy of participants' information: At any given time, the current prices and predictions of the mechanism reveal almost no information about any one participant, yet in total over all participants, information is accurately aggregated.
Elicitation Complexity of Statistical Properties
We lay the foundation for a general theory of elicitation complexity, including several basic results about how elicitation complexity behaves, and the complexity of standard properties of interest.
Market Making with Decreasing Utility for Information
We study information elicitation in cost-function-based combinatorial prediction markets when the market maker's utility for information decreases over time.
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal
We consider a popular problem in finance, option pricing, through the lens of an online learning game between Nature and an Investor.
