1,022 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Learning and Control of Dynamical Systems
Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise.
In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems.
We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p
Multi-objective resource optimization in space-aerial-ground-sea integrated networks
Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground,
and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including
resource optimization when the users have diverse requirements and applications. We present a
comprehensive review of SAGSI networks from a resource optimization perspective. We discuss
use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized
resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization,
network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of
network and performance degradation, software-defined networking, and intelligent surveillance
and relay communication. We then formulate a mathematical framework for maximizing energy
efficiency, resource utilization, and user association. We optimize user association while satisfying
the constraints of transmit power, data rate, and user association with priority. The binary decision
variable is used to associate users with system resources. Since the decision variable is binary and
constraints are linear, the formulated problem is a binary linear programming problem. Based on
our formulated framework, we simulate and analyze the performance of three different algorithms
(branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare
the results. Simulation results show that the branch and bound algorithm shows the best results,
so this is our benchmark algorithm. The complexity of branch and bound increases exponentially
as the number of users and stations increases in the SAGSI network. We got comparable results
for the interior point method and barrier simplex algorithm to the benchmark algorithm with low
complexity. Finally, we discuss future research directions and challenges of resource optimization
in SAGSI networks
Exploring the role of socio-economic and cultural factors influencing the occurrence of VVF in Northern Nigeria
Background
Access to a range of adequate care, and support during pregnancy and after delivery is required to prevent maternal morbidity, however, difficulties accessing appropriate healthcare by pregnant women is a significant problem in low- and middle-income countries, especially in Nigeria. Though there are multiple, significant, maternal morbidities or complications, obstructed fistula was identified as the one that impacts most women especially, in sub-Saharan Africa. There are two major kinds of obstructed fistula common in developing countries, namely, Vesico Vaginal Fistula (VVF) and Recto Vaginal Fistula (RVF) (Tebeu et al., 2012). This study focuses on vesicovaginal fistula because it has the most debilitating impact and is most prevalent in developing countries, especially Nigeria, where it is also increasing in prevalence in the Northern Nigeria geopolitical zones (Ijaya et al., 2010). VVF is an avertible tragedy, and a preventable complication of pregnancy resulting in an abnormal passage or channel between the vagina and the bladder. The impacts include stillbirth, physical and psychological trauma for the victim.
Maternal healthcare has been jeopardised especially in low- and middle-income countries giving rise to occurrence of VVF. While the quality of available healthcare is a concern, socioeconomic and cultural factors has been identified as critical factors leading to its occurrence. The literature review identified evidence gaps including, a lack of in-depth exploration of the individual's risk of being at risk (due to socio-economic/cultural factors), the experiences of VVF women, influence on practitioners’ service delivery, available interventions, and the challenges in delivering VVF services.
Aims
This study aimed to (1) explore the socio-economic and cultural factors influencing VVF occurrence among women in Northern Nigeria and (2) Identify potential interventions to address the increasing prevalence of VVF in Northern Nigeria.
Methods
A critical interpretivist approach and purposive sampling was used to explore participant experiences of VVF. A 1:1 semi-structured interview was conducted with twenty-two VVF patients and ten VVF practitioners from across the three geopolitical zones of Northern Nigeria. The interviews were transcribed, and thematic analysis of the data completed guided by Braun and Clarke (2006).
Findings
The findings of this study produced three overarching themes which are:
Socioeconomic and inter-connections with cultural factors influencing the prevalence of VVF.
Occurrence of VVF and challenges hindering access to healthcare.
Potential solutions to reduce the prevalence of VVF.
The findings indicate the negative impact of multiple, inter-related socio economic and cultural factors on women’s health outcomes and experiences, specifically in relation to their perceived value and role in society, associated risk of VVF and access to preventative and treatment services.
Conclusions
Reducing VVF prevalence may not be realistic without in-depth exploration and adequate prevention of the risk of being at its risk. VVF may continue to thrive where there is no improvement in maternal healthcare services, poverty, poor quality of education status or access to education and dominance of predisposing cultural practices. This study identified causes and suggested strategies for preventing VVF occurrence, resulting in specific recommendations for future policy, practice, and research, whilst also highlighting the implications of leaving these unaddressed, for economic recovery and achieving Sustainable Development Goals (SDGs) among women in Northern Nigeria
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Hyperparameters of Deep Learning (DL) pipelines are crucial for their
downstream performance. While a large number of methods for Hyperparameter
Optimization (HPO) have been developed, their incurred costs are often
untenable for modern DL. Consequently, manual experimentation is still the most
prevalent approach to optimize hyperparameters, relying on the researcher's
intuition, domain knowledge, and cheap preliminary explorations. To resolve
this misalignment between HPO algorithms and DL researchers, we propose
PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs
and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency
across a range of DL benchmarks and show its gains under informative expert
input and robustness against poor expert belief
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequential
decision-making problems of many domains, it still faces key challenges of data
inefficiency and the lack of interpretability. Interestingly, many researchers
have leveraged insights from the causality literature recently, bringing forth
flourishing works to unify the merits of causality and address well the
challenges from RL. As such, it is of great necessity and significance to
collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL
methods, and investigate the potential functionality from causality toward RL.
In particular, we divide existing CRL approaches into two categories according
to whether their causality-based information is given in advance or not. We
further analyze each category in terms of the formalization of different
models, ranging from the Markov Decision Process (MDP), Partially Observed
Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment
Regime (DTR). Moreover, we summarize the evaluation matrices and open sources
while we discuss emerging applications, along with promising prospects for the
future development of CRL.Comment: 29 pages, 20 figure
Multi-Fidelity Multi-Armed Bandits Revisited
We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the
canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled
with different costs (fidelities) and observation accuracy. We study both the
best arm identification with fixed confidence (BAI) and the regret minimization
objectives. For BAI, we present (a) a cost complexity lower bound, (b) an
algorithmic framework with two alternative fidelity selection procedures, and
(c) both procedures' cost complexity upper bounds. From both cost complexity
bounds of MF-MAB, one can recover the standard sample complexity bounds of the
classic (single-fidelity) MAB. For regret minimization of MF-MAB, we propose a
new regret definition, prove its problem-independent regret lower bound
and problem-dependent lower bound , where is the number of arms and is the decision budget
in terms of cost, and devise an elimination-based algorithm whose worst-cost
regret upper bound matches its corresponding lower bound up to some logarithmic
terms and, whose problem-dependent bound matches its corresponding lower bound
in terms of
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