52,977 research outputs found
Accelerating Stochastic Composition Optimization
Consider the stochastic composition optimization problem where the objective
is a composition of two expected-value functions. We propose a new stochastic
first-order method, namely the accelerated stochastic compositional proximal
gradient (ASC-PG) method, which updates based on queries to the sampling oracle
using two different timescales. The ASC-PG is the first proximal gradient
method for the stochastic composition problem that can deal with nonsmooth
regularization penalty. We show that the ASC-PG exhibits faster convergence
than the best known algorithms, and that it achieves the optimal sample-error
complexity in several important special cases. We further demonstrate the
application of ASC-PG to reinforcement learning and conduct numerical
experiments
Online Reinforcement Learning for Dynamic Multimedia Systems
In our previous work, we proposed a systematic cross-layer framework for
dynamic multimedia systems, which allows each layer to make autonomous and
foresighted decisions that maximize the system's long-term performance, while
meeting the application's real-time delay constraints. The proposed solution
solved the cross-layer optimization offline, under the assumption that the
multimedia system's probabilistic dynamics were known a priori. In practice,
however, these dynamics are unknown a priori and therefore must be learned
online. In this paper, we address this problem by allowing the multimedia
system layers to learn, through repeated interactions with each other, to
autonomously optimize the system's long-term performance at run-time. We
propose two reinforcement learning algorithms for optimizing the system under
different design constraints: the first algorithm solves the cross-layer
optimization in a centralized manner, and the second solves it in a
decentralized manner. We analyze both algorithms in terms of their required
computation, memory, and inter-layer communication overheads. After noting that
the proposed reinforcement learning algorithms learn too slowly, we introduce a
complementary accelerated learning algorithm that exploits partial knowledge
about the system's dynamics in order to dramatically improve the system's
performance. In our experiments, we demonstrate that decentralized learning can
perform as well as centralized learning, while enabling the layers to act
autonomously. Additionally, we show that existing application-independent
reinforcement learning algorithms, and existing myopic learning algorithms
deployed in multimedia systems, perform significantly worse than our proposed
application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table
Coordinate Descent with Bandit Sampling
Coordinate descent methods usually minimize a cost function by updating a
random decision variable (corresponding to one coordinate) at a time. Ideally,
we would update the decision variable that yields the largest decrease in the
cost function. However, finding this coordinate would require checking all of
them, which would effectively negate the improvement in computational
tractability that coordinate descent is intended to afford. To address this, we
propose a new adaptive method for selecting a coordinate. First, we find a
lower bound on the amount the cost function decreases when a coordinate is
updated. We then use a multi-armed bandit algorithm to learn which coordinates
result in the largest lower bound by interleaving this learning with
conventional coordinate descent updates except that the coordinate is selected
proportionately to the expected decrease. We show that our approach improves
the convergence of coordinate descent methods both theoretically and
experimentally.Comment: appearing at NeurIPS 201
- …