Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN

Inference And Learning: Computational Difficulty And Efficiency

In this thesis, we mainly investigate two collections of problems: statistical network inference and model selection in regression. The common feature shared by these two types of problems is that they typically exhibit an interesting phenomenon in terms of computational difficulty and efficiency. For statistical network inference, our goal is to infer the network structure based on a noisy observation of the network. Statistically, we model the network as generated from the structural information with the presence of noise, for example, planted submatrix model (for bipartite weighted graph), stochastic block model, and Watts-Strogatz model. As the relative amount of ``signal-to-noise\u27\u27 varies, the problems exhibit different stages of computational difficulty. On the theoretical side, we investigate these stages through characterizing the transition thresholds on the ``signal-to-noise\u27\u27 ratio, for the aforementioned models. On the methodological side, we provide new computationally efficient procedures to reconstruct the network structure for each model. For model selection in regression, our goal is to learn a ``good\u27\u27 model based on a certain model class from the observed data sequences (feature and response pairs), when the model can be misspecified. More concretely, we study two model selection problems: to learn from general classes of functions based on i.i.d. data with minimal assumptions, and to select from the sparse linear model class based on possibly adversarially chosen data in a sequential fashion. We develop new theoretical and algorithmic tools beyond empirical risk minimization to study these problems from a learning theory point of view

Decision making under uncertainty

Almost all important decision problems are inevitably subject to some level of uncertainty either about data measurements, the parameters, or predictions describing future evolution. The significance of handling uncertainty is further amplified by the large volume of uncertain data automatically generated by modern data gathering or integration systems. Various types of problems of decision making under uncertainty have been subject to extensive research in computer science, economics and social science. In this dissertation, I study three major problems in this context, ranking, utility maximization, and matching, all involving uncertain datasets. First, we consider the problem of ranking and top-k query processing over probabilistic datasets. By illustrating the diverse and conflicting behaviors of the prior proposals, we contend that a single, specific ranking function may not suffice for probabilistic datasets. Instead we propose the notion of parameterized ranking functions, that generalize or can approximate many of the previously proposed ranking functions. We present novel exact or approximate algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations or the probability distributions are continuous. The second problem concerns with the stochastic versions of a broad class of combinatorial optimization problems. We observe that the expected value is inadequate in capturing different types of risk-averse or risk-prone behaviors, and instead we consider a more general objective which is to maximize the expected utility of the solution for some given utility function. We present a polynomial time approximation algorithm with additive error ε for any ε > 0, under certain conditions. Our result generalizes and improves several prior results on stochastic shortest path, stochastic spanning tree, and stochastic knapsack. The third is the stochastic matching problem which finds interesting applications in online dating, kidney exchange and online ad assignment. In this problem, the existence of each edge is uncertain and can be only found out by probing the edge. The goal is to design a probing strategy to maximize the expected weight of the matching. We give linear programming based constant-factor approximation algorithms for weighted stochastic matching, which answer an open question raised in prior work

Generalized belief change with imprecise probabilities and graphical models

We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under extended conditions of uncertainty, inconsistency and imprecision. We motivate our kinematical approach by specializing our discussion to probabilistic reasoning with graphical models, whose modular representation allows for efficient inference. Most results in this direction are derived from the relevant work of Chan and Darwiche (2005), that first proved the inter-reducibility of virtual and probabilistic evidence. Such forms of information, deeply distinct in their meaning, are extended to the conditional and imprecise frameworks, allowing further generalizations, e.g. to experts' qualitative assessments. Belief aggregation and iterated revision of a rational agent's belief are also explored

Constraint based approaches to interpretable and semi-supervised machine learning

Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social media and governance. A related major challenge in deploying ML systems pertains to reliable learning when expert annotation is severely limited. This dissertation prescribes a common framework to address these challenges, based on the use of constraints that can make an ML model more interpretable, lead to novel methods for explaining ML models, or help to learn reliably with limited supervision. In particular, we focus on the class of latent variable models and develop a general learning framework by constraining realizations of latent variables and/or model parameters. We propose specific constraints that can be used to develop identifiable latent variable models, that in turn learn interpretable outcomes. The proposed framework is first used in Non–negative Matrix Factorization and Probabilistic Graphical Models. For both models, algorithms are proposed to incorporate such constraints with seamless and tractable augmentation of the associated learning and inference procedures. The utility of the proposed methods is demonstrated for our working application domain – identifiable phenotyping using Electronic Health Records (EHRs). Evaluation by domain experts reveals that the proposed models are indeed more clinically relevant (and hence more interpretable) than existing counterparts. The work also demonstrates that while there may be inherent trade–offs between constraining models to encourage interpretability, the quantitative performance of downstream tasks remains competitive. We then focus on constraint based mechanisms to explain decisions or outcomes of supervised black-box models. We propose an explanation model based on generating examples where the nature of the examples is constrained i.e. they have to be sampled from the underlying data domain. To do so, we train a generative model to characterize the data manifold in a high dimensional ambient space. Constrained sampling then allows us to generate naturalistic examples that lie along the data manifold. We propose ways to summarize model behavior using such constrained examples. In the last part of the contributions, we argue that heterogeneity of data sources is useful in situations where very little to no supervision is available. This thesis leverages such heterogeneity (via constraints) for two critical but widely different machine learning algorithms. In each case, a novel algorithm in the sub-class of co–regularization is developed to combine information from heterogeneous sources. Co–regularization is a framework of constraining latent variables and/or latent distributions in order to leverage heterogeneity. The proposed algorithms are utilized for clustering, where the intent is to generate a partition or grouping of observed samples, and for Learning to Rank algorithms – used to rank a set of observed samples in order of preference with respect to a specific search query. The proposed methods are evaluated on clustering web documents, social network users, and information retrieval applications for ranking search queries.Electrical and Computer Engineerin

Meta-optimizations for Cluster Analysis

This dissertation thesis deals with advances in the automation of cluster analysis.This dissertation thesis deals with advances in the automation of cluster analysis

Diversifying Group Recommendation

Recommender-systems has been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on existing user preferences. In some scenarios, the items to be recommended are not intended for personal use but a group of users. Group recommendation is rather more since group members have wide-ranging levels of interests and often involve conflicts. However, group recommendation endures the over-specification problem, in which the presumingly relevant items do not necessarily match true user intent. In this paper, we address the problem of diversity in group recommendation by improving the chance of returning at least one piece of information that embraces group satisfaction. We proposed a bounded algorithm that finds a subset of items with maximal group utility and maximal variety of information. Experiments on real-world rating datasets show the efficiency and effectiveness of our approach