2,241 research outputs found
Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States
Consider an online convex optimization problem where the loss functions are
self-concordant barriers, smooth relative to a convex function , and
possibly non-Lipschitz. We analyze the regret of online mirror descent with
. Then, based on the result, we prove the following in a unified manner.
Denote by the time horizon and the parameter dimension. 1. For online
portfolio selection, the regret of , a variant of
exponentiated gradient due to Helmbold et al., is when . This improves on the original regret bound for . 2. For online portfolio
selection, the regret of online mirror descent with the logarithmic barrier is
. The regret bound is the same as that of Soft-Bayes due
to Orseau et al. up to logarithmic terms. 3. For online learning quantum states
with the logarithmic loss, the regret of online mirror descent with the
log-determinant function is also . Its per-iteration
time is shorter than all existing algorithms we know.Comment: 19 pages, 1 figur
An Efficient Interior-Point Method for Online Convex Optimization
A new algorithm for regret minimization in online convex optimization is
described. The regret of the algorithm after time periods is - which is the minimum possible up to a logarithmic term. In
addition, the new algorithm is adaptive, in the sense that the regret bounds
hold not only for the time periods but also for every sub-interval
. The running time of the algorithm matches that of newly
introduced interior point algorithms for regret minimization: in
-dimensional space, during each iteration the new algorithm essentially
solves a system of linear equations of order , rather than solving some
constrained convex optimization problem in dimensions and possibly many
constraints
Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets
In this paper, we explore online convex optimization (OCO) and introduce a
new analysis that provides fast rates by exploiting the curvature of feasible
sets. In online linear optimization, it is known that if the average gradient
of loss functions is larger than a certain value, the curvature of feasible
sets can be exploited by the follow-the-leader (FTL) algorithm to achieve a
logarithmic regret. This paper reveals that algorithms adaptive to the
curvature of loss functions can also leverage the curvature of feasible sets.
We first prove that if an optimal decision is on the boundary of a feasible set
and the gradient of an underlying loss function is non-zero, then the algorithm
achieves a regret upper bound of in stochastic environments.
Here, is the radius of the smallest sphere that includes the optimal
decision and encloses the feasible set. Our approach, unlike existing ones, can
work directly with convex loss functions, exploiting the curvature of loss
functions simultaneously, and can achieve the logarithmic regret only with a
local property of feasible sets. Additionally, it achieves an
regret even in adversarial environments where FTL suffers an
regret, and attains an regret bound in
corrupted stochastic environments with corruption level . Furthermore, by
extending our analysis, we establish a regret upper bound of
for
-uniformly convex feasible sets, where uniformly convex sets include
strongly convex sets and -balls for . This bound
bridges the gap between the regret bound for strongly convex sets
() and the regret bound for non-curved sets ().Comment: 17 page
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Adaptive gradient methods have become recently very popular, in particular as
they have been shown to be useful in the training of deep neural networks. In
this paper we have analyzed RMSProp, originally proposed for the training of
deep neural networks, in the context of online convex optimization and show
-type regret bounds. Moreover, we propose two variants SC-Adagrad and
SC-RMSProp for which we show logarithmic regret bounds for strongly convex
functions. Finally, we demonstrate in the experiments that these new variants
outperform other adaptive gradient techniques or stochastic gradient descent in
the optimization of strongly convex functions as well as in training of deep
neural networks.Comment: ICML 2017, 16 pages, 23 figure
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Adaptive gradient methods have become recently very popular, in particular as
they have been shown to be useful in the training of deep neural networks. In
this paper we have analyzed RMSProp, originally proposed for the training of
deep neural networks, in the context of online convex optimization and show
-type regret bounds. Moreover, we propose two variants SC-Adagrad and
SC-RMSProp for which we show logarithmic regret bounds for strongly convex
functions. Finally, we demonstrate in the experiments that these new variants
outperform other adaptive gradient techniques or stochastic gradient descent in
the optimization of strongly convex functions as well as in training of deep
neural networks.Comment: ICML 2017, 16 pages, 23 figure
Universal MMSE Filtering With Logarithmic Adaptive Regret
We consider the problem of online estimation of a real-valued signal
corrupted by oblivious zero-mean noise using linear estimators. The estimator
is required to iteratively predict the underlying signal based on the current
and several last noisy observations, and its performance is measured by the
mean-square-error. We describe and analyze an algorithm for this task which: 1.
Achieves logarithmic adaptive regret against the best linear filter in
hindsight. This bound is assyptotically tight, and resolves the question of
Moon and Weissman [1]. 2. Runs in linear time in terms of the number of filter
coefficients. Previous constructions required at least quadratic time.Comment: 14 page
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