3,540 research outputs found
Advances in machine learning algorithms for financial risk management
In this thesis, three novel machine learning techniques are introduced to address distinct
yet interrelated challenges involved in financial risk management tasks. These approaches
collectively offer a comprehensive strategy, beginning with the precise classification of credit
risks, advancing through the nuanced forecasting of financial asset volatility, and ending
with the strategic optimisation of financial asset portfolios.
Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk
assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture
modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed
using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression
model is then applied to predict the probability of default using the heuristically balanced
datasets. The results underscore the effectiveness of our proposed technique, with superior
performance observed in comparison to other imbalanced preprocessing approaches. This
advancement in credit risk classification lays a solid foundation for understanding individual
financial behaviours, a crucial first step in the broader context of financial risk management.
Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a
Triple Discriminator Generative Adversarial Network with a continuous wavelet transform
is proposed. The proposed model has the ability to decompose volatility time series into
signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform
component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a
Generative Adversarial Network consisting of triple Discriminator and Generator networks.
The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised
loss and reconstruction loss as part of its framework. Data from nine financial assets are
employed to demonstrate the effectiveness of the proposed model. This approach not only
enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis.
Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio
optimisation using historical Low, High, and Close prices of assets as input with weights of
assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return
on investment based on deep reinforcement learning. To provide more learning stability in
an online training process, a Markov Differential Sharpe Ratio reward function has been
proposed as the reinforcement learning objective function. Additionally, a Multi-Memory
Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout
a specified trading period. The use of the insights gained from volatility forecasting into
this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving
superior results based on risk-adjusted reward performance measures.
In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the
accuracy of credit risk classification, through the improvement and understanding of market
volatility, to optimisation of investment strategies. These methodologies collectively show
the potential of the use of machine learning to improve financial risk management
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin
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Interpretable Machine Learning Architectures for Efficient Signal Detection with Applications to Gravitational Wave Astronomy
Deep learning has seen rapid evolution in the past decade, accomplishing tasks that were previously unimaginable. At the same time, researchers strive to better understand and interpret the underlying mechanisms of the deep models, which are often justifiably regarded as "black boxes". Overcoming this deficiency will not only serve to suggest better learning architectures and training methods, but also extend deep learning to scenarios where interpretability is key to the application. One such scenario is signal detection and estimation, with gravitational wave detection as a specific example, where classic methods are often preferred for their interpretability. Nonetheless, while classic statistical detection methods such as matched filtering excel in their simplicity and intuitiveness, they can be suboptimal in terms of both accuracy and computational efficiency. Therefore, it is appealing to have methods that achieve ``the best of both worlds'', namely enjoying simultaneously excellent performance and interpretability.
In this thesis, we aim to bridge this gap between modern deep learning and classic statistical detection, by revisiting the signal detection problem from a new perspective. First, to address the perceived distinction in interpretability between classic matched filtering and deep learning, we state the intrinsic connections between the two families of methods, and identify how trainable networks can address the structural limitations of matched filtering. Based on these ideas, we propose two trainable architectures that are constructed based on matched filtering, but with learnable templates and adaptivity to unknown noise distributions, and therefore higher detection accuracy. We next turn our attention toward improving the computational efficiency of detection, where we aim to design architectures that leverage structures within the problem for efficiency gains. By leveraging the statistical structure of class imbalance, we integrate hierarchical detection into trainable networks, and use a novel loss function which explicitly encodes both detection accuracy and efficiency. Furthermore, by leveraging the geometric structure of the signal set, we consider using signal space optimization as an alternative computational primitive for detection, which is intuitively more efficient than covering with a template bank. We theoretical prove the efficiency gain by analyzing Riemannian gradient descent on the signal manifold, which reveals an exponential improvement in efficiency over matched filtering. We also propose a practical trainable architecture for template optimization, which makes use of signal embedding and kernel interpolation.
We demonstrate the performance of all proposed architectures on the task of gravitational wave detection in astrophysics, where matched filtering is the current method of choice. The architectures are also widely applicable to general signal or pattern detection tasks, which we exemplify with the handwritten digit recognition task using the template optimization architecture. Together, we hope the this work useful to scientists and engineers seeking machine learning architectures with high performance and interpretability, and contribute to our understanding of deep learning as a whole
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting
Wireless fingerprinting refers to a device identification method leveraging
hardware imperfections and wireless channel variations as signatures. Beyond
physical layer characteristics, recent studies demonstrated that user
behaviours could be identified through network traffic, e.g., packet length,
without decryption of the payload. Inspired by these results, we propose a
multi-layer fingerprinting framework that jointly considers the multi-layer
signatures for improved identification performance. In contrast to previous
works, by leveraging the recent multi-view machine learning paradigm, i.e.,
data with multiple forms, our method can cluster the device information shared
among the multi-layer features without supervision. Our information-theoretic
approach can be extended to supervised and semi-supervised settings with
straightforward derivations. In solving the formulated problem, we obtain a
tight surrogate bound using variational inference for efficient optimization.
In extracting the shared device information, we develop an algorithm based on
the Wyner common information method, enjoying reduced computation complexity as
compared to existing approaches. The algorithm can be applied to data
distributions belonging to the exponential family class. Empirically, we
evaluate the algorithm in a synthetic dataset with real-world video traffic and
simulated physical layer characteristics. Our empirical results show that the
proposed method outperforms the state-of-the-art baselines in both supervised
and unsupervised settings
Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior
Tensor regression methods have been widely used to predict a scalar response
from covariates in the form of a multiway array. In many applications, the
regions of tensor covariates used for prediction are often spatially connected
with unknown shapes and discontinuous jumps on the boundaries. Moreover, the
relationship between the response and the tensor covariates can be nonlinear.
In this article, we develop a nonlinear Bayesian tensor additive regression
model to accommodate such spatial structure. A functional fused elastic net
prior is proposed over the additive component functions to comprehensively
model the nonlinearity and spatial smoothness, detect the discontinuous jumps,
and simultaneously identify the active regions. The great flexibility and
interpretability of the proposed method against the alternatives are
demonstrated by a simulation study and an analysis on facial feature data
A bayesian wavelet shrinkage rule under LINEX loss function
This work proposes a wavelet shrinkage rule under asymmetric LINEX loss
function and a mixture of a point mass function at zero and the logistic
distribution as prior distribution to the wavelet coefficients in a
nonparametric regression model with gaussian error. Underestimation of a
significant wavelet coefficient can lead to a bad detection of features of the
unknown function such as peaks, discontinuities and oscillations. It can also
occur under asymmetrically distributed wavelet coefficients. Thus the proposed
rule is suitable when overestimation and underestimation have asymmetric
losses. Statistical properties of the rule such as squared bias, variance,
frequentist and bayesian risks are obtained. Simulation studies are conducted
to evaluate the performance of the rule against standard methods and an
application in a real dataset involving infrared spectra is provided
Game Theory in Distributed Systems Security: Foundations, Challenges, and Future Directions
Many of our critical infrastructure systems and personal computing systems
have a distributed computing systems structure. The incentives to attack them
have been growing rapidly as has their attack surface due to increasing levels
of connectedness. Therefore, we feel it is time to bring in rigorous reasoning
to secure such systems. The distributed system security and the game theory
technical communities can come together to effectively address this challenge.
In this article, we lay out the foundations from each that we can build upon to
achieve our goals. Next, we describe a set of research challenges for the
community, organized into three categories -- analytical, systems, and
integration challenges, each with "short term" time horizon (2-3 years) and
"long term" (5-10 years) items. This article was conceived of through a
community discussion at the 2022 NSF SaTC PI meeting.Comment: 11 pages in IEEE Computer Society magazine format, including
references and author bios. There is 1 figur
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
Despite the basic premise that next-generation wireless networks (e.g., 6G)
will be artificial intelligence (AI)-native, to date, most existing efforts
remain either qualitative or incremental extensions to existing ``AI for
wireless'' paradigms. Indeed, creating AI-native wireless networks faces
significant technical challenges due to the limitations of data-driven,
training-intensive AI. These limitations include the black-box nature of the AI
models, their curve-fitting nature, which can limit their ability to reason and
adapt, their reliance on large amounts of training data, and the energy
inefficiency of large neural networks. In response to these limitations, this
article presents a comprehensive, forward-looking vision that addresses these
shortcomings by introducing a novel framework for building AI-native wireless
networks; grounded in the emerging field of causal reasoning. Causal reasoning,
founded on causal discovery, causal representation learning, and causal
inference, can help build explainable, reasoning-aware, and sustainable
wireless networks. Towards fulfilling this vision, we first highlight several
wireless networking challenges that can be addressed by causal discovery and
representation, including ultra-reliable beamforming for terahertz (THz)
systems, near-accurate physical twin modeling for digital twins, training data
augmentation, and semantic communication. We showcase how incorporating causal
discovery can assist in achieving dynamic adaptability, resilience, and
cognition in addressing these challenges. Furthermore, we outline potential
frameworks that leverage causal inference to achieve the overarching objectives
of future-generation networks, including intent management, dynamic
adaptability, human-level cognition, reasoning, and the critical element of
time sensitivity
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