nils-herrmann/clean_scirepeval huggingface dataset:
Teaching the Normative Theory of Causal Reasoning 1

There is now substantial agreement about the representational component of a normative theory of statistical causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from single trials under a variety of conditions. In contrast, one component of the normative theory focuses on learning from a sample drawn from a population under some experimental or study regime. Through a virtual Causality Lab that embodies the normative theory of causal reasoning and which allows us to record student behavior, we have begun to systematically explore how best to teach the normative theory. In this papers we explain the overall project and report on pilot studies which suggest that students can quickly be taught to (appear to) be quite rational.
A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.
Constraint-Based Human Causal Learning

Much of human cognition and activity depends on causal beliefs and reasoning. In psychological research on human causal learning and inference, we usually suppose that we have a set of binary potential causes, C1, ..., Cn, and a known binary effect, E, all typically present-absent values of a property or event. The differentiation into potential causes and effect is made on the basis of external factors, including prior knowledge or temporal information. Given these variables, people are then asked to infer the existence and strength of causal relationships between the Ci’s and E from observed data in one of several formats (serially, as a list, or in a summary). The standard measure of people’s causal beliefs is a rating of some proxy for causal influence, where a zero rating indicates no causal relationship. The exact probe question varies between experiments, and has been found to significantly impact participants’ ratings (e.g., Collins & Shanks, under review). A variety of theories have been proposed to explain people’s causal inferences in this type of highly limited scenario (see Danks, forthcoming, for a theoretical overview and synthesis). One general view for which there is a growing body of evidence is that people’s causal beliefs and learning are well-modeled as though they are learning a causal Bayesian network (CBN, henceforth).
A theory of causal learning in children: causal maps and Bayes nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.
Confirmation versus Search in Gene Regulation : The Complexity of Gene Perturbation Experimentation as a Search Method

Various proposals have been made for understanding gene regulation through measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to some flaw in the algorithm, but rather to the fact that there are far too many networks consistent with the data (at least 10, to be precise). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy.
The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search

Our primary interest is in determining how many gene perturbation experiments are required to determine the network of regulatory relations among any given set of genes, ignoring questions of uncertainty in statistical decisions. Secondarily, we are interested in whether it is feasible to compute which further experiments will be most informative. And, finally, since the sample sizes (number of expression measurements per gene) in such experiments are typically small, we are concerned with the stability of statistical decisions about differential expression given different statistical tests. Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to an algorithmic flaw, but rather to the fact that there are far too many networks consistent with the data (10 in the provided example). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy. The answers we find are far more pessimistic than has previously been suggested in the literature (e.g., [Onami et al., 2001; Ideker et al., 2000]). We show that, while perturbation experiments can eventually eliminate possible regulatory relations, they do not efficiently eliminate them. We give an anytime algorithm for computing weakly monotonically increasing lower bounds on the number of alternative network explanations for the results of any set of gene perturbation experiments. The lower bound is typically astronomical. We illustrate the point by computing a lower bound—10—on the number of networks for 9 genes that are consistent with a recent series of gene perturbation experiments [Ideker et al., 2001]. Finally, we argue that the computation of the most informative experiments can only be heuristic.
Inferring Hidden Causes

One of the important aspects of human causal reasoning is that from the time we are young children we reason about unobserved causes. How can we learn about unobserved causes from information about observed events?  Bayes nets provide a formal account of how causal structure is learned from a combination of associations and interventions. This formalism makes specific predictions about the conditions under which learners postulate hidden causes. In this study adult learners were shown a pattern of associations and interventions on a novel causal system. We found that they were able to infer hidden causes as predicted by the Bayes net formalism, and were able to distinguish between one hidden common cause and two hidden independent causes of the observed events.
Learning the Causal Structure of Overlapping Variable Sets

In many real-world applications of machine learning and data mining techniques, one finds that one must separate the variables under consideration into multiple subsets (perhaps to reduce computational complexity, or because of a shift in focus during data collection and analysis). In this paper, we use the framework of Bayesian networks to examine the problem of integrating the learning outputs for multiple overlapping datasets.In particular, we provide rules for extracting causal information about the true (unknown) Bayesian network from the previously learned (partial) Bayesian networks. We also provide the SLPR algorithm, which efficiently uses these previously learned Bayesian networks to guide learning of the full structure. A complexity analysis of the "worst-case" scenario for the SLPR algorithm reveals that the algorithm is always less complex than a comparable "reference" algorithm (though no absolute optimality proof is known).Although no "expectedcase" analysis is given, the complexity analysis suggests that (given the currently available set of algorithms) one should always use the SLPR algorithm, regardless of the underlying generating structure. The results provided in this paper point to a wide range of open questions, which are briefly discussed.
Learning Complex Causal Structures

Most current theories of human causal learning are essentially parameter estimators: they assume a fixed causal structure and estimate causal strengths within that structure. In these theories, absence of causation is represented as zero causal strength, rather than a distinct causal structure. In this paper, we first present the theoretical framework of Bayesian networks, which can represent both structure (presence/absence of causation) and parameters (strength of causation). We then present a series of experiments involving a particularly complex causal structure and a novel methodology that focuses on structural discriminations, rather than parameter estimation. These experiments suggest that people are capable of doing more than just parameter estimation. A significant group of participants seems to be learning (something isomorphic to) a Bayesian
Equilibria of the Rescorla-Wagner Model 1 Equilibria of the

The Rescorla-Wagner model has been a leading theory of animal causal induction for nearly thirty years, and human causal induction for the past fifteen. Recent theories (especially Cheng, 1997) have provided alternative explanations of how people draw causal conclusions from covariational data. However, theoretical attempts to compare the Rescorla-Wagner model with more recent models have been hampered by the fact that the Rescorla-Wagner model is an algorithmic theory, while the more recent theories are all computational. This paper provides a detailed derivation of the long-run behavior of the Rescorla-Wagner model under a wide range of parameters and experimental setups, so that the model can be compared with computational theories. It also shows that the model agrees with competing theories on a wider range of cases than had previously been thought. The paper concludes by showing how recent suggested modifications of the Rescorla-Wagner model impact the long-run behavior of the model.
Linearity Properties of Bayes Nets with Binary Variables

It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of "instrumental variables" sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a path or pair of paths with a single common vertex (a trek) can be computed directly from the parameter values associated with each edge in the trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables. 
 
These properties are exploited in search procedures. We show that (1) and (2) hold for all Bayes nets with binary variables. We further show that for Bayes nets parameterized as noisy-OR and noisy-AND gates, all of these properties save (4) hold.
Information Retrieval System

An information retrieval system is described for retrieving a list of documents such as web pages or other items from a document index in response to a user query. In an embodiment a prediction engine is used to predict both explicit relevance information such as judgment labels and implicit relevance information such as click data. In an embodiment the predicted relevance information is applied to a stored utility function that describes user satisfaction with a search session. This produces utility scores for proposed lists of documents. Using the utility scores one of the lists of documents is selected. In this way different sources of relevance information are combined into a single information retrieval system in a principled and effective manner which gives improved performance.
Learning Gaussian processes from multiple tasks

We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.
Hierarchical Bayesian modelling with Gaus-sian processes

We present a novel method for Gaussian process regression in a hierarchical Bayesian framework. The method is suited for cases where data from separate, but related problem scenarios is available. In the hierarchical framework, kernel matrices on a fixed set of input points (transduction) can be learned from data using a simple and efficient EM algorithm. This does not require a parametric form for the kernel function, thus problems that require, for example, nonstationary kernels, can be handled. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.
A method and computer program with program code means, and computer program product for determining a structure contained in data using separable graphical models

The invention relates to a detection of a graphical model structure into data for continuous variables. Here, a graphical model to describe the data is broken down into subgraphs. Using kernel density estimators subgraph densities are determined for the partial graphs. Using the subgraph densities, and a method of a cross-validation scheme, an evaluation function is determined which rated adding an edge to the model in terms of a description of the quality of a structure contained in the data by the model. Using the evaluation function those edges are determined, which are inserted into the model, and thus determines the graphical model structure.
Learning Gaussian Process Kernels via Hierarchical Bayes

We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystrom method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.
Probabilistic memory-based collaborative filtering

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.
Mining functional modules in genetic networks with decomposable graphical models.

In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count.
Laser Imaging Techniques for Follow-up Analysis of Joint Inflammation in Patients with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is the most frequent chronic inflammatory arthropathy that often leads to joint destruction. Essential for the progress of the disease are both an early diagnosis and a sensitive follow-up of synovitis. In this paper, we present new laser based imaging techniques for the transillumination of small finger joints. The “Laserscan” technique allows the transillumination of small finger joints using red laser light with a wavelength of 675nm. The laser is positioned above the finger joint and a CCD camera detects the scattering distribution below the joint. After processing the data through a statistical machine learning method, we could show in a preliminary clinical study that it was possible to correctly classify the inflammatory status of 62 of 72 joints from 22 patients with RA compared to precise clinical examination. Sagittal optical tomography is a further development of “Laserscan”. In this technique, a red laser is moved in sagittal plane above the proximal interphalangeal finger joint while a detector underneath the joint measures the scattered light distribution in the same plane. Using model-based iterative image reconstruction algorithms, sagittal cross-sectional images are obtained based on the acquired data. These images reflect the distribution of scattering and absorption properties within and around the joint cavity of a first healthy volunteer.
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes

Collaborative filtering (CF) and content-based filtering (CBF) have widely been used information filtering applications, both approaches having their individual strengths and weaknesses. This paper proposes a novel probabilistic framework to unify CF and CBF, named collaborative ensemble learning. Based on content based probabilistic models for each user's preferences (the CBF idea), it combines a society of users' preferences to predict an active user's preferences (the CF idea). While retaining an intuitive explanation, the combination scheme can be interpreted as a hierarchical Bayesian approach in which a common prior distribution is learned from related experiments. It does not require a global training stage and thus can incrementally incorporate new data. We report results based on two data sets, the neuters-21578 text data set and a data base of user opionions on art images. For both data sets, collaborative ensemble achieved excellent performance in terms of recommendation accuracy. In addition to recommendation engines, collaborative ensemble learning is applicable to problems typically solved via classical hierarchical Bayes, like multisensor fusion and multitask learning.