nils-herrmann/clean_scirepeval huggingface dataset:
Uncertainty in artificial intelligence : proceedings of the Eighteenth Conference (2002) : August 1-4, 2002, University of Alberta, Edmonton

Proceedings of the annual Conference on Uncertainty in Artificial Intelligence, available for 1991-present. Since 1985, the Conference on Uncertainty in Artificial Intelligence (UAI) has been the primary international forum for exchanging results on the use of principled uncertain-reasoning methods in intelligent systems. The UAI Proceedings have become a basic reference for researches and practitioners who want to know about both theoretical advances and the latest applied developments in the field.
Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occurring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.
Learning Hierarchical Object Maps of Non-Stationary Environments with Mobile Robots

Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, recycling bins). It does so through a two-level hierarchical representation, which links individual objects with generic shape templates of object classes. We derive an approximate EM algorithm for learning shape parameters at both levels of the hierarchy, using local occupancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of objects and object templates in the environment. Experimental results using a real robot equipped with a laser range finder indicate that our approach performs well at learning object-based maps of simple office environments. The approach outperforms a previously developed non-hierarchical algorithm that models objects but lacks class templates.
Discriminative Probabilistic Models for Relational Data

In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, ignoring the correlations between them. Recently, Probabilistic Relational Models, a relational version of Bayesian networks, were used to define a joint probabilistic model for a collection of related entities. In this paper, we present an alternative framework that builds on (conditional) Markov networks and addresses two limitations of the previous approach. First, undirected models do not impose the acyclicity constraint that hinders representation of many important relational dependencies in directed models. Second, undirected models are well suited for discriminative training, where we optimize the conditional likelihood of the labels given the features, which generally improves classification accuracy. We show how to train these models effectively, and how to use approximate probabilistic inference over the learned model for collective classification of multiple related entities. We provide experimental results on a webpage classification task, showing that accuracy can be significantly improved by modeling relational dependencies.
Learning Probabilistic Models of Link Structure

Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models (PRMs) embraces the object-relational nature of structured data by capturing probabilistic interactions between attributes of related entities. In this paper, we extend this framework by modeling interactions between the attributes and the link structure itself. An advantage of our approach is a unified generative model for both content and relational structure. We propose two mechanisms for representing a probabilistic distribution over link structures: reference uncertainty and existence uncertainty. We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict link structure and, moreover, the observed link structure can be used to provide better predictions for the attributes in the model.
Multi-agent algorithms for solving graphical games

Consider the problem of a group of agents trying to find a stable strategy profile for a joint interaction. A standard approach is to describe the situation as a single multiplayer game and find an equilibrium strategy profile of that game. However, most algorithms for finding equilibria are computationally expensive; they are also centralized, requiring that all relevant payoff information be available to a single agent (or computer) who must determine the entire equilibrium profile. In this paper, we exploit two ideas to address these problems. We consider structured game representations, where the interaction between the agents is sparse, an assumption that holds in many real-world situations. We also consider the slightly relaxed task of finding an approximate equilibrium. We present two algorithms for finding approximate equilibria in these games, one based on a hill-climbing approach and one on constraint satisfaction. We show that these algorithms exploit the game structure to achieve faster computation. They are also inherently local, requiring only limited communication between directly interacting agents. They can thus be scaled to games involving large numbers of agents, provided the interaction between the agents is not too dense.
Acting rationally with incomplete utility information

Rational decision making requires full knowledge of the utility function of the person affected by the decisions. However, the task of acquiring such knowledge is often infeasible due to the size of the outcome space and the complexity of the utility elicitation process. We argue that a person's utility value for a given outcome can be treated as we treat other domain attributes: as a random variable with a density function over its possible values. 
We show that we can apply statistical density estimation techniques to learn a probabilistic model of utilities from a database of partially elicited utility functions. The Bayesian learning framework we define for this problem also allows us to discover the number of statistically coherent subpopulations and select a structured utility model for each subpopulation. The factorization of the utilities in the learned model and the generalization obtained from density estimation allow us to provide a compact and robust representation of the utility functions in the population. Such a model can serve as a prior distribution when we encounter a new user whose utility function we need to discover. Any information we obtain in the course of utility elicitation process or by observing the user's behavior can be used to adjust the model to the user by Bayesian conditioning. 
We present two applications of such customizable utility models. The first, designed for a decision support system, directs the utility elicitation process by choosing questions most relevant for the current user and the current decision problem. The second focuses on non-cooperative situations where a second agent is optimizing its own decisions relative to its uncertainty about the utility of the first.
Learning Bayesian Networks from Data

Statistics, Pattern Recognition and Information Theory There are many books on statistics. We find [DeGroot 1970] to be a good introduction to statistics and Bayesian statistics in particular. A more recent book [Gelman et al. 1995] is also a good introduction to this field and also discusses recent advances, such as hierarchical priors. Books in pattern recognition, including the classic [Duda and Hart 1973] and the more recent [Bishop 1995], cover basic issues in density estimation and their use for pattern recognition and classification. A good introduction to information theory, and notions such as KL divergence and mutual information can be found in [Cover and Thomas 1991].
Active Learning for Structure in Bayesian Networks

The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper uses active learning to determine the experiments that are most informative towards uncovering the underlying structure. We formalize the causal learning task as that of learning the structure of a causal Bayesian network. We consider an active learner that is allowed to conduct experiments, where it intervenes in the domain by setting the values of certain variables. We provide a theoretical framework for the active learning problem, and an algorithm that actively chooses the experiments to perform based on the model learned so far. Experimental results show that active learning can substantially reduce the number of observations required to determine the structure of a domain.
Probabilistic Abstraction Hierarchies

Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in "nearby" classes in the taxonomy are similar. In this paper, we provide a general probabilistic framework for clustering data into a set of classes organized as a taxonomy, where each class is associated with a probabilistic model from which the data was generated. The clustering algorithm simultaneously optimizes three things: the assignment of data instances to clusters, the models associated with the clusters, and the structure of the abstraction hierarchy. A unique feature of our approach is that it utilizes global optimization algorithms for both of the last two steps, reducing the sensitivity to noise and the propensity to local maxima that are characteristic of algorithms such as hierarchical agglomerative clustering that only take local steps. We provide a theoretical analysis for our algorithm, showing that it converges to a local maximum of the joint likelihood of model and data. We present experimental results on synthetic data, and on real data in the domains of gene expression and text.
Probabilistic Classification and Clustering in Relational Data

Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational models in which instances of multiple types are related to each other in complex ways. For example, in a scientific paper domain, papers are related to each other via citation, and are also related to their authors. In this case, the label of one entity (e.g., the topic of the paper) is often correlated with the labels of related entities. We propose a general class of models for classification and clustering in relational domains that capture probabilistic dependencies between related instances. We show how to learn such models efficiently from data. We present empirical results on two real world data sets. Our experiments in a transductive classification setting indicate that accuracy can be significantly improved by modeling relational dependencies. Our algorithm automatically induces a very natural behavior, where our knowledge about one instance helps us classify related ones, which in turn help us classify others. In an unsupervised setting, our models produced coherent clusters with a very natural interpretation, even for instance types that do not have any attributes.
Solving Factored POMDPs with Linear Value Functions

Partially Observable Markov Decision Processes (POMDPs) provide a coherent mathematical framework for planning under uncertainty when the state of the system cannot be fully observed. However, the problem of finding an exact POMDP solution is intractable. Computing such solution requires the manipulation of a piecewise linear convex value function, which specifies a value for each possible belief state. This value function can be represented by a set of vectors, each one with dimension equal to the size of the state space. In nontrivial problems, however, these vectors are too large for such a representation to be feasible, preventing the use of exact POMDP algorithms. We propose an approximation scheme where each vector is represented as a linear combination of basis functions to provide a compact approximation to the value function. We also show that this representation can be exploited to allow for efficient computations in approximate value and policy iteration algorithms in the context of factored POMDPs, where the transition model is specified using a dynamic Bayesian network.
Rich probabilistic models for gene expression

Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. Second, clustering methods cannot readily incorporate additional types of information, such as clinical data or known attributes of genes. To circumvent these shortcomings, we propose the use of a single coherent probabilistic model, that encompasses much of the rich structure in the genomic expression data, while incorporating additional information such as experiment type, putative binding sites, or functional information. We show how this model can be learned from the data, allowing us to discover patterns in the data and dependencies between the gene expression patterns and additional attributes. The learned model reveals context-specific relationships, that exist only over a subset of the experiments in the dataset. We demonstrate the power of our approach on synthetic data and on two real-world gene expression data sets for yeast. For example, we demonstrate a novel functionality that falls naturally out of our framework: predicting the "cluster" of the array resulting from a gene mutation based only on the gene's expression pattern in the context of other mutations.
Sampling in Factored Dynamic Systems

In many real-world situations, we are interested in monitoring the evolution of a complex situation over time. For example, we may be monitoring a patient’s vital signs in an intensive care unit (Dagum and Galper 1995), analyzing a complex freeway traffic scene with the goal of controlling a moving vehicle (Huang, Koller, Malik, Ogasawara, Rao, Russell and Weber 1994), localizing a robot in a complex environment (Fox, Burgard and Thrun 1999) (see also Murphy and Russell (2001: this volume)), or tracking motion of non-rigid objects in a cluttered visual scene (Isard and Blake 1998a). We treat such systems as being in one of a possible set of states, where the state changes over time. We model the states as changing at discrete time intervals, so that x t is the state of the system at time t. In most systems, we model the system states as having some internal structure: the system state is typically represented by some vector of variables X = (X 1,...,X n ), where each X i takes on values in some space Dom[X i ]. The possible states x are assignments of values to the variables X. In a traffic surveillance application, the state might contain variables such as the vehicle position, its velocity, the weather, and more.
Learning Probabilistic Models of Relational Structure

Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the relational structure. The recently introduced framework of probabilistic relational models (PRMs) allows us to represent probabilistic models over multiple entities that utilize the relations between them. In this paper, we propose the use of probabilistic models not only for the attributes in a relational model, but for the relational structure itself. We propose two mechanisms for modeling structural uncertainty: reference uncertainty and existence uncertainty. We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict relational structure and, moreover, the observed relational structure can be used to provide better predictions for the attributes in the model.
UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001

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Exact Inference in Networks with Discrete Children of Continuous Parents

Many real life domains contain a mixture of discrete and continuous variables and can be modeled as hybrid Bayesian Networks (BNs). An important subclass of hybrid BNs are conditional linear Gaussian (CLG) networks, where the conditional distribution of the continuous variables given an assignment to the discrete variables is a multivariate Gaussian. Lauritzen's extension to the clique tree algorithm can be used for exact inference in CLG networks. However, many domains include discrete variables that depend on continuous ones, and CLG networks do not allow such dependencies to be represented. In this paper, we propose the first "exact" inference algorithm for augmented CLG networks -- CLG networks augmented by allowing discrete children of continuous parents. Our algorithm is based on Lauritzen's algorithm, and is exact in a similar sense: it computes the exact distributions over the discrete nodes, and the exact first and second moments of the continuous ones, up to inaccuracies resulting from numerical integration used within the algorithm. In the special case of softmax CPDs, we show that integration can often be done efficiently, and that using the first two moments leads to a particularly accurate approximation. We show empirically that our algorithm achieves substantially higher accuracy at lower cost than previous algorithms for this task.
Max-norm Projections for Factored MDPs

Markov Decision Processes (MDPs) provide a coherent mathematical framework for planning under uncertainty. However, exact MDP solution algorithms require the manipulation of a value function, which specifies a value for each state in the system. Most real-world MDPs are too large for such a representation to be feasible, preventing the use of exact MDP algorithms. Various approximate solution algorithms have been proposed, many of which use a linear combination of basis functions as a compact approximation to the value function. Almost all of these algorithms use an approximation based on the (weighted) L2-norm (Euclidean distance); this approach prevents the application of standard convergence results for MDP algorithms, all of which are based on max-norm. This paper makes two contributions. First, it presents the first approximate MDP solution algorithms - both value and policy iteration - that use max-norm projection, thereby directly optimizing the quantity required to obtain the best error bounds. Second, it shows how these algorithms can be applied efficiently in the context of factored MDPs, where the transition model is specified using a dynamic Bayesian network.
Selectivity estimation using probabilistic models

Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query processing. It arises in cost-based query optimization, query profiling, and approximate query answering. In this paper, we show how probabilistic graphical models can be effectively used for this task as an accurate and compact approximation of the joint frequency distribution of multiple attributes across multiple relations. Probabilistic Relational Models (PRMs) are a recent development that extends graphical statistical models such as Bayesian Networks to relational domains. They represent the statistical dependencies between attributes within a table, and between attributes across foreign-key joins. We provide an efficient algorithm for constructing a PRM front a database, and show how a PRM can be used to compute selectivity estimates for a broad class of queries. One of the major contributions of this work is a unified framework for the estimation of queries involving both select and foreign-key join operations. Furthermore, our approach is not limited to answering a small set of predetermined queries; a single model can be used to effectively estimate the sizes of a wide collection of potential queries across multiple tables. We present results for our approach on several real-world databases. For both single-table multi-attribute queries and a general class of select-join queries, our approach produces more accurate estimates than standard approaches to selectivity estimation, using comparable space and time.