879 research outputs found
Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application
Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems:
1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3).
2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4).
3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5).
4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A Theistic Critique of Secular Moral Nonnaturalism
This dissertation is an exercise in Theistic moral apologetics. It will be developing both a critique of secular nonnaturalist moral theory (moral Platonism) at the level of metaethics, as well as a positive form of the moral argument for the existence of God that follows from this critique. The critique will focus on the work of five prominent metaethical theorists of secular moral non-naturalism: David Enoch, Eric Wielenberg, Russ Shafer-Landau, Michael Huemer, and Christopher Kulp. Each of these thinkers will be critically examined. Following this critique, the positive moral argument for the existence of God will be developed, combining a cumulative, abductive argument that follows from filling in the content of a succinct apagogic argument. The cumulative abductive argument and the apagogic argument together, with a transcendental and modal component, will be presented to make the case that Theism is the best explanation for the kind of moral, rational beings we are and the kind of universe in which we live, a rational intelligible universe
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
Gibbs posteriors are proportional to a prior distribution multiplied by an
exponentiated loss function, with a key tuning parameter weighting information
in the loss relative to the prior and providing a control of posterior
uncertainty. Gibbs posteriors provide a principled framework for
likelihood-free Bayesian inference, but in many situations, including a single
tuning parameter inevitably leads to poor uncertainty quantification. In
particular, regardless of the value of the parameter, credible regions have far
from the nominal frequentist coverage even in large samples. We propose a
sequential extension to Gibbs posteriors to address this problem. We prove the
proposed sequential posterior exhibits concentration and a Bernstein-von Mises
theorem, which holds under easy to verify conditions in Euclidean space and on
manifolds. As a byproduct, we obtain the first Bernstein-von Mises theorem for
traditional likelihood-based Bayesian posteriors on manifolds. All methods are
illustrated with an application to principal component analysis
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Foundations of Node Representation Learning
Low-dimensional node representations, also called node embeddings, are a cornerstone in the modeling and analysis of complex networks. In recent years, advances in deep learning have spurred development of novel neural network-inspired methods for learning node representations which have largely surpassed classical \u27spectral\u27 embeddings in performance. Yet little work asks the central questions of this thesis: Why do these novel deep methods outperform their classical predecessors, and what are their limitations?
We pursue several paths to answering these questions. To further our understanding of deep embedding methods, we explore their relationship with spectral methods, which are better understood, and show that some popular deep methods are equivalent to spectral methods in a certain natural limit. We also introduce the problem of inverting node embeddings in order to probe what information they contain. Further, we propose a simple, non-deep method for node representation learning, and find it to often be competitive with modern deep graph networks in downstream performance.
To better understand the limitations of node embeddings, we prove some upper and lower bounds on their capabilities. Most notably, we prove that node embeddings are capable of exact low-dimensional representation of networks with bounded max degree or arboricity, and we further show that a simple algorithm can find such exact embeddings for real-world networks. By contrast, we also prove inherent bounds on random graph models, including those derived from node embeddings, to capture key structural properties of networks without simply memorizing a given graph
Interactive Graph Convolutional Filtering
Interactive Recommender Systems (IRS) have been increasingly used in various
domains, including personalized article recommendation, social media, and
online advertising. However, IRS faces significant challenges in providing
accurate recommendations under limited observations, especially in the context
of interactive collaborative filtering. These problems are exacerbated by the
cold start problem and data sparsity problem. Existing Multi-Armed Bandit
methods, despite their carefully designed exploration strategies, often
struggle to provide satisfactory results in the early stages due to the lack of
interaction data. Furthermore, these methods are computationally intractable
when applied to non-linear models, limiting their applicability. To address
these challenges, we propose a novel method, the Interactive Graph
Convolutional Filtering model. Our proposed method extends interactive
collaborative filtering into the graph model to enhance the performance of
collaborative filtering between users and items. We incorporate variational
inference techniques to overcome the computational hurdles posed by non-linear
models. Furthermore, we employ Bayesian meta-learning methods to effectively
address the cold-start problem and derive theoretical regret bounds for our
proposed method, ensuring a robust performance guarantee. Extensive
experimental results on three real-world datasets validate our method and
demonstrate its superiority over existing baselines
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
Consumer Neuroscience e Brand Relationship: misurare l’associazione implicita tra il Sé del consumatore e il brand.
Il presente elaborato si focalizza sulla connessione tra Consumer Neuroscience e Brand Relationship con un focus specifico sul Sé del consumatore, analizzato attraverso uno strumento di misurazione indiretta del comportamento. L’obiettivo è stato quello di contribuire alla validazione e all’utilizzo nel contesto italiano di un SC-IAT per lo studio dell’associazione tra Sé e brand, interpretandone i risultati tramite un’analisi di matrice neuroscientifica su stimoli brand-related. Il vantaggio di questo strumento, rispetto allo IAT tradizionale, è quello di poter ‘fotografare’ un’istantanea sulla relazione senza la necessità di utilizzare una dimensione comparativa. Misurando direttamente la forza dell’associazione tra il concetto del brand e quello del Sé. Per farlo, l’autore è passato attraverso fasi distinte che hanno prima indagato gli aspetti puramente psicometrici dello strumento, per dedicarsi successivamente a un test neuroscientifico. I risultati hanno evidenziato delle buone performance del SC-IAT, così pensato, suggerendo approfondimenti futuri e applicazioni a brand dalla differente architettura. Inoltre, l’analisi neurofisiologica ha evidenziato come lo strumento possa risultare efficace nel fornire un’interpretazione aggiuntiva agli indicatori neurofisiologici testati durante la visualizzazione di uno stimolo relativo al brand
Multidimensional Time Series Methods for Economics and Finance
Questa tesi mira ad affrontare le questioni inferenziali e interpretative nei modelli ad alta dimensione e multidimensionali nel contesto dell'Economia e della Finanza. La crescente integrazione economica e finanziaria ha reso di fondamentale importanza considerare i Paesi e i Mercati Finanziari come un'unica, grande e interconnessa entità . Le principali sfide indotte da questo quadro riguardano la stima e l'interpretazione di ampi Panel data, in cui le unità possono essere rappresentate da paesi o attività finanziarie, osservate attraverso diversi indicatori nel tempo. Questa tesi propone tecniche di stima Bayesiana per nuovi modelli matriciali e tensoriali e utilizza tecniche della Teoria dei Grafi per facilitare l'interpretazione di network ad alta dimensione. I contributi sono presentati in tre capitoli. Nel Capitolo 2, vengono proposti approcci della Teoria dei Grafi per studiare le strutture e le interazioni in Network direzionali e pesati. Nel Capitolo 3, viene proposto un approccio Bayesiano di variable selection per gestire il problema della sovrapparametrizzazione nei modelli di Autorregressione Matriciale di grandi dimensioni. Nel Capitolo 4, viene esplorata la relazione dinamica tra rendimenti, volatilità e sentiment nel settore delle criptovalute attraverso un modello Autoregressivo Matriciale, che rappresenta il primo tentativo di considerare i dati sugli asset finanziari come strutture multidimensionali.This thesis aims to address the inferential and interpretational issues in high and multi-dimensional models in the context of Economics and Finance. The growing economic and financial integration has made imperative the need to conceive Countries and Financial Markets as a single, large, interconnected entity. The main challenges induced by this framework concern the estimation and interpretation of large panels, where units can be represented by countries or assets, observed via several indicators across time. This thesis proposes Bayesian estimation techniques for novel matrix and tensor-valued models and employs new methodological tools from Graph Theory to facilitate interpretation of high-dimensional networks. The contributions are presented in three chapters. In Chapter 2, Graph Theory approaches are proposed to study the structures and interactions of weighted directed networks of multivariate time series observations/relationships. In Chapter 3, a Bayesian variable selection approach is proposed to handle the over-parametrization problem in large Matrix Autoregressive models. In Chapter 4, the dynamic relationship among returns, volatility, and sentiment in the cryptocurrency class is explored through a Bayesian Matrix Autoregressive model, which is the first attempt to consider financial asset data as multi-dimensional structures
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