605 research outputs found
Learning recommender systems from biased user interactions
Recommender systems have been widely deployed to help users quickly find what they need from a collection of items. Predominant recommendation methods rely on supervised learning models to predict user ratings on items or the probabilities of users interacting with items. In addition, reinforcement learning models are crucial in improving long-term user engagement within recommender systems. In practice, both of these recommendation methods are commonly trained on logged user interactions and, therefore, subject to bias present in logged user interactions. This thesis concerns complex forms of bias in real-world user behaviors and aims to mitigate the effect of bias on reinforcement learning-based recommendation methods. The first part of the thesis consists of two research chapters, each dedicated to tackling a specific form of bias: dynamic selection bias and multifactorial bias. To mitigate the effect of dynamic selection bias and multifactorial bias, we propose a bias propensity estimation method for each. By incorporating the results from the bias propensity estimation methods, the widely used inverse propensity scoring-based debiasing method can be extended to correct for the corresponding bias. The second part of the thesis consists of two chapters that concern the effect of bias on reinforcement learning-based recommendation methods. Its first chapter focuses on mitigating the effect of bias on simulators, which enables the learning and evaluation of reinforcement learning-based recommendation methods. Its second chapter further explores different state encoders for reinforcement learning-based recommendation methods when learning and evaluating with the proposed debiased simulator
Adaptive Bayesian Predictive Inference
Bayesian predictive inference provides a coherent description of entire
predictive uncertainty through predictive distributions. We examine several
widely used sparsity priors from the predictive (as opposed to estimation)
inference viewpoint. Our context is estimating a predictive distribution of a
high-dimensional Gaussian observation with a known variance but an unknown
sparse mean under the Kullback-Leibler loss. First, we show that LASSO
(Laplace) priors are incapable of achieving rate-optimal performance. This new
result contributes to the literature on negative findings about Bayesian LASSO
posteriors. However, deploying the Laplace prior inside the Spike-and-Slab
framework (for example with the Spike-and-Slab LASSO prior), rate-minimax
performance can be attained with properly tuned parameters (depending on the
sparsity level sn). We highlight the discrepancy between prior calibration for
the purpose of prediction and estimation. Going further, we investigate popular
hierarchical priors which are known to attain adaptive rate-minimax performance
for estimation. Whether or not they are rate-minimax also for predictive
inference has, until now, been unclear. We answer affirmatively by showing that
hierarchical Spike-and-Slab priors are adaptive and attain the minimax rate
without the knowledge of sn. This is the first rate-adaptive result in the
literature on predictive density estimation in sparse setups. This finding
celebrates benefits of fully Bayesian inference
Inhomogeneous graph trend filtering via a l2,0 cardinality penalty
We study estimation of piecewise smooth signals over a graph. We propose a
-norm penalized Graph Trend Filtering (GTF) model to estimate
piecewise smooth graph signals that exhibits inhomogeneous levels of smoothness
across the nodes. We prove that the proposed GTF model is simultaneously a
k-means clustering on the signal over the nodes and a minimum graph cut on the
edges of the graph, where the clustering and the cut share the same assignment
matrix. We propose two methods to solve the proposed GTF model: a spectral
decomposition method and a method based on simulated annealing. In the
experiment on synthetic and real-world datasets, we show that the proposed GTF
model has a better performances compared with existing approaches on the tasks
of denoising, support recovery and semi-supervised classification. We also show
that the proposed GTF model can be solved more efficiently than existing models
for the dataset with a large edge set.Comment: 21 pages, 3 figures, 4 table
Spatial and temporal patterns in Holocene wildfire responses to environmental change in the northern extratropics
Fire is an important environmental process in the northern extratropics (NET), with various regions
predicted to experience the highest magnitude increases in fire activity compared to other global regions in future. Previous NET palaeofire studies are limited by poor data availability and a lack of
quantitative methods. A synthesis of charcoal records is conducted to reconstruct sub-continentalscale Holocene fire histories across the NET (>45°N) and to understand their environmental controls.
A circum-NET-scale analysis, and a more spatially resolved analysis at the European scale (n of 21
regions) are conducted. At the NET scale, simulated palaeo climate and plant productivity data are
used in a novel clustering method to define a stratification that delineates spatial units of coherent
fire-relevant environmental change. At the European scale, this is done using pollen-based reconstructions of Holocene forest cover, summer temperature and precipitation change. Fire histories are
reconstructed by aggregating charcoal records from the Reading Palaeofire Database within clusters.
Fire reconstructions are correlated with climate and land cover reconstructions at 4000-year intervals.
Fire responses of 20 regions show correlation values of >= |0.75| with at least one environmental
variable for at least one 4000-year interval. Across Europe, fire increased over the Holocene, initially
in response to the Fennoscandian Ice Sheet collapse and associated climate drying and forestation.
Mid-to-late Holocene fire increases were caused by forest compositional shifts, human deforestation,
and agricultural expansion. Across North America, the early-Holocene collapse of the Laurentide Ice
Sheet caused continent-wide productivity increases leading to fire increases. A subsequent long-term
moisture increase drove late-Holocene fire declines across most of the continent. In central Asia, a
general Holocene-wide moisture increase drove a long-term fire decline. The results support previous study showing that sub-continental palaeofire histories in the NET are explained by variations
in climate variables influencing fuel moisture and load, but that these effects can be modulated by
land cover processes influencing fuel structure and composition. The results provide a basis for spatial prediction of fire regime changes in response to future climate, vegetation and human land use
processes
Quantum Algorithm for Maximum Biclique Problem
Identifying a biclique with the maximum number of edges bears considerable
implications for numerous fields of application, such as detecting anomalies in
E-commerce transactions, discerning protein-protein interactions in biology,
and refining the efficacy of social network recommendation algorithms. However,
the inherent NP-hardness of this problem significantly complicates the matter.
The prohibitive time complexity of existing algorithms is the primary
bottleneck constraining the application scenarios. Aiming to address this
challenge, we present an unprecedented exploration of a quantum computing
approach. Efficient quantum algorithms, as a crucial future direction for
handling NP-hard problems, are presently under intensive investigation, of
which the potential has already been proven in practical arenas such as
cybersecurity. However, in the field of quantum algorithms for graph databases,
little work has been done due to the challenges presented by the quantum
representation of complex graph topologies. In this study, we delve into the
intricacies of encoding a bipartite graph on a quantum computer. Given a
bipartite graph with n vertices, we propose a ground-breaking algorithm qMBS
with time complexity O^*(2^(n/2)), illustrating a quadratic speed-up in terms
of complexity compared to the state-of-the-art. Furthermore, we detail two
variants tailored for the maximum vertex biclique problem and the maximum
balanced biclique problem. To corroborate the practical performance and
efficacy of our proposed algorithms, we have conducted proof-of-principle
experiments utilizing IBM quantum simulators, of which the results provide a
substantial validation of our approach to the extent possible to date
Semidefinite approximations for bicliques and biindependent pairs
We investigate some graph parameters asking to maximize the size of biindependent pairs (A,B) in a bipartite graph G=(V1∪V2,E), where A⊆V1, B⊆V2 and A∪B is independent. These parameters also allow to study bicliques in general graphs (via bipartite double graphs). When the size is the number |A∪B| of vertices one finds the stability number α(G), well-known to be polynomial-time computable. When the size is the product |A|⋅|B| one finds the parameter g(G), shown to be NP-hard by Peeters (2003), and when the size is the ratio |A|⋅|B|/|A∪|B| one finds the parameter h(G), introduced by Vallentin (2020) for bounding product-free sets in finite groups. We show that h(G) is an NP-hard parameter and, as a crucial ingredient, that it is NP-complete to decide whether a bipartite graph G has a balanced maximum independent set. These hardness results motivate introducing semidefinite programming bounds for g(G), h(G), and αbal(G) (the maximum cardinality of a balanced independent set). We show that these bounds can be seen as natural variations of the Lovász ϑ-number, a well-known semidefinite bound on α(G) (equal to it for G bipartite). In addition we formulate closed-form eigenvalue bounds, which coincide with the semidefinite bounds for vertex- and edge-transitive graphs, and we show relationships among them as well as with earlier spectral parameters by Hoffman, Haemers (2001) and Vallentin (2020)
Sum-of-squares representations for copositive matrices and independent sets in graphs
A polynomial optimization problem asks for minimizing a polynomial function (cost) given a set of constraints (rules) represented by polynomial inequalities and equations. Many hard problems in combinatorial optimization and applications in operations research can be naturally encoded as polynomial optimization problems. A common approach for addressing such computationally hard problems is by considering variations of the original problem that give an approximate solution, and that can be solved efficiently. One such approach for attacking hard combinatorial problems and, more generally, polynomial optimization problems, is given by the so-called sum-of-squares approximations. This thesis focuses on studying whether these approximations find the optimal solution of the original problem.We investigate this question in two main settings: 1) Copositive programs and 2) parameters dealing with independent sets in graphs. Among our main new results, we characterize the matrix sizes for which sum-of-squares approximations are able to capture all copositive matrices. In addition, we show finite convergence of the sums-of-squares approximations for maximum independent sets in graphs based on their continuous copositive reformulations. We also study sum-of-squares approximations for parameters asking for maximum balanced independent sets in bipartite graphs. In particular, we find connections with the Lovász theta number and we design eigenvalue bounds for several related parameters when the graphs satisfy some symmetry properties.<br/
Biclustering random matrix partitions with an application to classification of forensic body fluids
Classification of unlabeled data is usually achieved by supervised learning
from labeled samples. Although there exist many sophisticated supervised
machine learning methods that can predict the missing labels with a high level
of accuracy, they often lack the required transparency in situations where it
is important to provide interpretable results and meaningful measures of
confidence. Body fluid classification of forensic casework data is the case in
point. We develop a new Biclustering Dirichlet Process (BDP), with a
three-level hierarchy of clustering, and a model-based approach to
classification which adapts to block structure in the data matrix. As the class
labels of some observations are missing, the number of rows in the data matrix
for each class is unknown. The BDP handles this and extends existing
biclustering methods by simultaneously biclustering multiple matrices each
having a randomly variable number of rows. We demonstrate our method by
applying it to the motivating problem, which is the classification of body
fluids based on mRNA profiles taken from crime scenes. The analyses of
casework-like data show that our method is interpretable and produces
well-calibrated posterior probabilities. Our model can be more generally
applied to other types of data with a similar structure to the forensic data.Comment: 45 pages, 10 figure
Machine Learning and Natural Language Processing in Stock Prediction
In this thesis, we first study the two ill-posed natural language processing tasks related to stock prediction, i.e. stock movement prediction and financial document-level event extraction. While implementing stock prediction and event extraction, we encountered difficulties that could be resolved by utilizing out-of-distribution detection. Consequently, we presented a new approach for out-of-distribution detection, which is the third focus of this thesis. First, we systematically build a platform to study the NLP-aided stock auto-trading algorithms. Our platform is characterized by three features: (1) We provide financial news for each specific stock. (2) We provide various stock factors for each stock. (3) We evaluate performance from more financial-relevant metrics. Such a design allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a more realistic setting. We also propose a system to automatically learn a good feature representation from various input information. The key to our algorithm is a method called semantic role labelling Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a compact representation of each news paragraph. Based on SRLP, we further incorporate other stock factors to make the stock movement prediction. In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system. Through our experimental study, we show that the proposed method achieves better performance and outperforms all strong baselines’ annualized rate of return as well as the maximum drawdown in back-testing. Second, we propose a generative solution for document-level event extraction that takes into account recent developments in generative event extraction, which have been successful at the sentence level but have not yet been explored for document-level extraction. Our proposed solution includes an encoding scheme to capture entity-to-document level information and a decoding scheme that takes into account all relevant contexts. Extensive experimental results demonstrate that our generative-based solution can perform as well as state-of-theart methods that use specialized structures for document event extraction. This allows our method to serve as an easy-to-use and strong baseline for future research in this area. Finally, we propose a new unsupervised OOD detection model that separates, extracts, and learns the semantic role labelling guided fine-grained local feature representation from different sentence arguments and the full sentence using a margin-based contrastive loss. Then we demonstrate the benefit of applying a self-supervised approach to enhance such global-local feature learning by predicting the SRL extracted role. We conduct our experiments and achieve state-of-the-art performance on out-of-distribution benchmarks.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202
Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling
In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects
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