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Found 11 papers, 0 papers with code
Causal Bandits: Learning Good Interventions via Causal Inference
We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment.
Compliance-Aware Bandits
Motivated by clinical trials, we study bandits with observable non-compliance.
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).
Fast rates in statistical and online learning
For bounded losses, we show how the central condition enables a direct proof of fast rates and we prove its equivalence to the Bernstein condition, itself a generalization of the Tsybakov margin condition, both of which have played a central role in obtaining fast rates in statistical learning.
Risk Dynamics in Trade Networks
We introduce a new framework to model interactions among agents which seek to trade to minimize their risk with respect to some future outcome.
Generalized Mixability via Entropic Duality
Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice.
Generalised Mixability, Constant Regret, and Bayesian Updating
Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm.
Mixability in Statistical Learning
We show that, in the special case of log-loss, stochastic mixability reduces to a well-known (but usually unnamed) martingale condition, which is used in existing convergence theorems for minimum description length and Bayesian inference.
Interpreting prediction markets: a stochastic approach
We strengthen recent connections between prediction markets and learning by showing that a natural class of market makers can be understood as performing stochastic mirror descent when trader demands are sequentially drawn from a fixed distribution.
Composite Multiclass Losses
We also show that the integral representation for binary proper losses can not be extended to multiclass losses.
