This is the Proceedings of the First Conference on Uncertainty in Artificial Intelligence, which was held in Los Angeles, CA, July 10-12, 1985

Many applications in translational medicine require the understanding of how diseases progress through the accumulation of persistent events. Specialized Bayesian networks called monotonic progression networks offer a statistical framework for modeling this sort of phenomenon. Current machine learning tools to reconstruct Bayesian networks from data are powerful but not suited to progression models. We combine the technological advances in machine learning with a rigorous philosophical theory of causation to produce Polaris, a scalable algorithm for learning progression networks that accounts for causal or biological noise as well as logical relations among genetic events, making the resulting models easy to interpret qualitatively. We tested Polaris on synthetically generated data and showed that it outperforms a widely used machine learning algorithm and approaches the performance of the competing special-purpose, albeit clairvoyant algorithm that is given a priori information about the model parameters. We also prove that under certain rather mild conditions, Polaris is guaranteed to converge for sufficiently large sample sizes. Finally, we applied Polaris to point mutation and copy number variation data in Prostate cancer from The Cancer Genome Atlas (TCGA) and found that there are likely three distinct progressions, one major androgen driven progression, one major non-androgen driven progression, and one novel minor androgen driven progression.

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.

The task of estimating a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Most works on matrix completion have focused on recovering an unknown real-valued low-rank matrix from a random sample of its entries. Here, we investigate the case of highly quantized observations when the measurements can take only a small number of values. These quantized outputs are generated according to a probability distribution parametrized by the unknown matrix of interest. This model corresponds, for example, to ratings in recommender systems or labels in multi-class classification. We consider a general, non-uniform, sampling scheme and give theoretical guarantees on the performance of a constrained, nuclear norm penalized maximum likelihood estimator. One important advantage of this estimator is that it does not require knowledge of the rank or an upper bound on the nuclear norm of the unknown matrix and, thus, it is adaptive. We provide lower bounds showing that our estimator is minimax optimal. An efficient algorithm based on lifted coordinate gradient descent is proposed to compute the estimator. A limited Monte-Carlo experiment, using both simulated and real data is provided to support our claims.

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.

In group decision making (GDM) problems fuzzy preference relations (FPR) are widely used for representing decision makers' opinions on the set of alternatives. In order to avoid misleading solutions, the study of consistency and consensus has become a very important aspect. This article presents a simulated annealing (SA) based soft computing approach to optimize the consistency/consensus level (CCL) of a complete fuzzy preference relation in order to solve a GDM problem. Consistency level indicates as expert's preference quality and consensus level measures the degree of agreement among experts' opinions. This study also suggests the set of experts for the necessary modifications in their prescribed preference structures without intervention of any moderator.

Operating vehicles in adversarial environments between a recurring origin-destination pair requires new planning techniques. A two players zero-sum game is introduced. The goal of the first player is to minimize the expected casualties undergone by a convoy. The goal of the second player is to maximize this damage. The outcome of the game is obtained via a linear program that solves the corresponding minmax optimization problem over this outcome. Different environment models are defined in order to compute routing strategies over unstructured environments. To compare these methods for increasingly accurate representations of the environment, a grid-based model is chosen to represent the environment and the existence of a sufficient network size is highlighted. A global framework for the generation of realistic routing strategies between any two points is described. This framework requires a good assessment of the potential casualties at any location, therefore the most important parameters are identified. Finally the framework is tested on real world environments.

In domains such as biomedicine, ontologies are prominently utilized for annotating data. Consequently, aligning ontologies facilitates integrating data. Several algorithms exist for automatically aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit online run time constraints, performing the alignment in a reasonable amount of time without compromising the quality of the alignment is a crucial challenge. A large class of alignment algorithms is iterative and often consumes more time than others in delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent (BCD), which exploits the subdimensions of the alignment problem identified using a partitioning scheme. We integrate this approach into multiple well-known alignment algorithms and show that the enhanced algorithms generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. Because BCD does not overly constrain how we partition or order the parts, we vary the partitioning and ordering schemes in order to empirically determine the best schemes for each of the selected algorithms. As biomedicine represents a key application domain for ontologies, we introduce a comprehensive biomedical ontology testbed for the community in order to evaluate alignment algorithms. Because biomedical ontologies tend to be large, default iterative techniques find it difficult to produce a good quality alignment within a reasonable amount of time. We align a significant number of ontology pairs from this testbed using BCD-enhanced algorithms. Our contributions represent an important step toward making a significant class of alignment techniques computationally feasible.

We use a semisupervised learning algorithm based on a topological data analysis approach to assign functional categories to yeast proteins using similarity graphs. This new approach to analyzing biological networks yields results that are as good as or better than state of the art existing approaches.

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also be formed and used for future prediction. The inTrees framework can applied to both classification and regression problems, and is applicable to many types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the "inTrees" R package.