4,970 research outputs found
On Adaptivity in Information-constrained Online Learning
We study how to adapt to smoothly-varying ('easy') environments in well-known
online learning problems where acquiring information is expensive. For the
problem of label efficient prediction, which is a budgeted version of
prediction with expert advice, we present an online algorithm whose regret
depends optimally on the number of labels allowed and (the quadratic
variation of the losses of the best action in hindsight), along with a
parameter-free counterpart whose regret depends optimally on (the quadratic
variation of the losses of all the actions). These quantities can be
significantly smaller than (the total time horizon), yielding an
improvement over existing, variation-independent results for the problem. We
then extend our analysis to handle label efficient prediction with bandit
feedback, i.e., label efficient bandits. Our work builds upon the framework of
optimistic online mirror descent, and leverages second order corrections along
with a carefully designed hybrid regularizer that encodes the constrained
information structure of the problem. We then consider revealing action-partial
monitoring games -- a version of label efficient prediction with additive
information costs, which in general are known to lie in the \textit{hard} class
of games having minimax regret of order . We provide a
strategy with an bound for revealing action
games, along with one with a bound for the
full class of hard partial monitoring games, both being strict improvements
over current bounds.Comment: 34th AAAI Conference on Artificial Intelligence (AAAI 2020). Short
version at 11th Optimization for Machine Learning workshop (OPT 2019
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Submodular maximization is a general optimization problem with a wide range
of applications in machine learning (e.g., active learning, clustering, and
feature selection). In large-scale optimization, the parallel running time of
an algorithm is governed by its adaptivity, which measures the number of
sequential rounds needed if the algorithm can execute polynomially-many
independent oracle queries in parallel. While low adaptivity is ideal, it is
not sufficient for an algorithm to be efficient in practice---there are many
applications of distributed submodular optimization where the number of
function evaluations becomes prohibitively expensive. Motivated by these
applications, we study the adaptivity and query complexity of submodular
maximization. In this paper, we give the first constant-factor approximation
algorithm for maximizing a non-monotone submodular function subject to a
cardinality constraint that runs in adaptive rounds and makes
oracle queries in expectation. In our empirical study, we use
three real-world applications to compare our algorithm with several benchmarks
for non-monotone submodular maximization. The results demonstrate that our
algorithm finds competitive solutions using significantly fewer rounds and
queries.Comment: 12 pages, 8 figure
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
Highly automated robot ecologies (HARE), or societies of independent
autonomous robots or agents, are rapidly becoming an important part of much of
the world's critical infrastructure. As with human societies, regulation,
wherein a governing body designs rules and processes for the society, plays an
important role in ensuring that HARE meet societal objectives. However, to
date, a careful study of interactions between a regulator and HARE is lacking.
In this paper, we report on three user studies which give insights into how to
design systems that allow people, acting as the regulatory authority, to
effectively interact with HARE. As in the study of political systems in which
governments regulate human societies, our studies analyze how interactions
between HARE and regulators are impacted by regulatory power and individual
(robot or agent) autonomy. Our results show that regulator power, decision
support, and adaptive autonomy can each diminish the social welfare of HARE,
and hint at how these seemingly desirable mechanisms can be designed so that
they become part of successful HARE.Comment: 10 pages, 7 figures, to appear in the 5th International Conference on
Human Agent Interaction (HAI-2017), Bielefeld, German
Pandora's Box Problem with Order Constraints
The Pandora's Box Problem, originally formalized by Weitzman in 1979, models
selection from set of random, alternative options, when evaluation is costly.
This includes, for example, the problem of hiring a skilled worker, where only
one hire can be made, but the evaluation of each candidate is an expensive
procedure. Weitzman showed that the Pandora's Box Problem admits an elegant,
simple solution, where the options are considered in decreasing order of
reservation value,i.e., the value that reduces to zero the expected marginal
gain for opening the box. We study for the first time this problem when order -
or precedence - constraints are imposed between the boxes. We show that,
despite the difficulty of defining reservation values for the boxes which take
into account both in-depth and in-breath exploration of the various options,
greedy optimal strategies exist and can be efficiently computed for tree-like
order constraints. We also prove that finding approximately optimal adaptive
search strategies is NP-hard when certain matroid constraints are used to
further restrict the set of boxes which may be opened, or when the order
constraints are given as reachability constraints on a DAG. We complement the
above result by giving approximate adaptive search strategies based on a
connection between optimal adaptive strategies and non-adaptive strategies with
bounded adaptivity gap for a carefully relaxed version of the problem
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