252 research outputs found
Hoeffding's inequality in game-theoretic probability
This note makes the obvious observation that Hoeffding's original proof of
his inequality remains valid in the game-theoretic framework. All details are
spelled out for the convenience of future reference.Comment: 5 page
Continuous and randomized defensive forecasting: unified view
Defensive forecasting is a method of transforming laws of probability (stated
in game-theoretic terms as strategies for Sceptic) into forecasting algorithms.
There are two known varieties of defensive forecasting: "continuous", in which
Sceptic's moves are assumed to depend on the forecasts in a (semi)continuous
manner and which produces deterministic forecasts, and "randomized", in which
the dependence of Sceptic's moves on the forecasts is arbitrary and
Forecaster's moves are allowed to be randomized. This note shows that the
randomized variety can be obtained from the continuous variety by smearing
Sceptic's moves to make them continuous.Comment: 10 pages. The new version: (1) relaxes the assumption that the
outcome space is finite, and now it is only assumed to be compact; (2) shows
that in the case where the outcome space is finite of cardinality C, the
randomized forecasts can be chosen concentrated on a finite set of
cardinality at most
Competitive on-line learning with a convex loss function
We consider the problem of sequential decision making under uncertainty in
which the loss caused by a decision depends on the following binary
observation. In competitive on-line learning, the goal is to design decision
algorithms that are almost as good as the best decision rules in a wide
benchmark class, without making any assumptions about the way the observations
are generated. However, standard algorithms in this area can only deal with
finite-dimensional (often countable) benchmark classes. In this paper we give
similar results for decision rules ranging over an arbitrary reproducing kernel
Hilbert space. For example, it is shown that for a wide class of loss functions
(including the standard square, absolute, and log loss functions) the average
loss of the master algorithm, over the first observations, does not exceed
the average loss of the best decision rule with a bounded norm plus
. Our proof technique is very different from the standard ones and
is based on recent results about defensive forecasting. Given the probabilities
produced by a defensive forecasting algorithm, which are known to be well
calibrated and to have good resolution in the long run, we use the expected
loss minimization principle to find a suitable decision.Comment: 26 page
Defensive forecasting for optimal prediction with expert advice
The method of defensive forecasting is applied to the problem of prediction
with expert advice for binary outcomes. It turns out that defensive forecasting
is not only competitive with the Aggregating Algorithm but also handles the
case of "second-guessing" experts, whose advice depends on the learner's
prediction; this paper assumes that the dependence on the learner's prediction
is continuous.Comment: 14 page
Merging of opinions in game-theoretic probability
This paper gives game-theoretic versions of several results on "merging of
opinions" obtained in measure-theoretic probability and algorithmic randomness
theory. An advantage of the game-theoretic versions over the measure-theoretic
results is that they are pointwise, their advantage over the algorithmic
randomness results is that they are non-asymptotic, but the most important
advantage over both is that they are very constructive, giving explicit and
efficient strategies for players in a game of prediction.Comment: 26 page
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