nils-herrmann/clean_scirepeval huggingface dataset:
Apprenticeship learning via inverse reinforcement learning

We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be difficult to write down an explicit reward function specifying exactly how different desiderata should be traded off. We think of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and give an algorithm for learning the task demonstrated by the expert. Our algorithm is based on using "inverse reinforcement learning" to try to recover the unknown reward function. We show that our algorithm terminates in a small number of iterations, and that even though we may never recover the expert's reward function, the policy output by the algorithm will attain performance close to that of the expert, where here performance is measured with respect to the expert's unknown reward function.
Comparing clusterings: an axiomatic view

This paper views clusterings as elements of a lattice. Distances between clusterings are analyzed in their relationship to the lattice. From this vantage point, we first give an axiomatic characterization of some criteria for comparing clusterings, including the variation of information and the unadjusted Rand index. Then we study other distances between partitions w.r.t these axioms and prove an impossibility result: there is no "sensible" criterion for comparing clusterings that is simultaneously (1) aligned with the lattice of partitions, (2) convexely additive, and (3) bounded.
Unsupervised Spectral Learning

In spectral clustering and spectral image segmentation, the data is partioned starting from a given matrix of pairwise similarities S. the matrix S is constructed by hand, or learned on a separate training set. In this paper we show how to achieve spectral clustering in unsupervised mode. Our algorithm starts with a set of observed pairwise features, which are possible components of an unknown, parametric similarity function. This function is learned iteratively, at the same time as the clustering of the data. The algorithm shows promosing results on synthetic and real data.
Regularized spectral learning

Spectral clustering is a technique for finding groups in data consisting of similarities Sij between pairs of points. We approach the problem of learning the similarity as a function of other observed features, in order to optimize spectral clustering results on future data. This paper formulates a new objective for learning in spectral clustering, that balances a clustering accuracy term, the gap, and a stability term, the eigengap with the later in the role of a regularizer. We derive an algorithm to optimize this objective, and semiautomatic methods to chose the optimal regularization. Preliminary experiments confirm the validity of the approach. We start by introducing notation and some basic facts in section 2, we define the learning problem in section 3 and introduce the new objective in 4. Sections 5, 6 present respectively a gradient algorithm for optimizing the criterion and a method for selecting the anlount of regularization. Experimental results are in section 7 and 8 concludes the paper. 2 Spectral clustering notation and background In spectral clustering, the data is a set of similarities Sij, satisfying Sij = Sji '2.: 0, between pairs of points i, j in a set V, IVI = n, The matrix S [Sij]i,jEV is called the similarity matrix. We denote by D i ; Vol {i} (1)
Spectral Clustering of Biological Sequence Data

In this paper, we apply spectral techniques to clustering biological sequence data that has proved more difficult to cluster effectively. For this purpose, we have to (1) extend spectral clustering algorithms to deal with asymmetric affinities. like the alignment scores used in the comparison of biological sequences. and (2) devise a hierarchical algorithm that can handle many clusters with imbalanced sizes robustly. We present an algorithm for clustering asymmetric affinity data, and demonstrate the performance of this algorithm at recovering the higher levels of the Structural Classification of Proteins (SCOP) on a data base of highly conserved subsequences.
Real-time particle filters

Particle filters estimate the state of dynamic systems from sensor information. In many real-time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situations is to update the particle filter as often as possible and to discard sensor information that cannot be processed in time. In this paper we present real-time particle filters, which make use of all sensor information even when the filter update rate is below the update rate of the sensors. This is achieved by representing posteriors as mixtures of sample sets, where each mixture component integrates one observation arriving during a filter update. The weights of the mixture components are set so as to minimize the approximation error introduced by the mixture representation. Thereby, our approach focuses computational resources on valuable sensor information. Experiments using data collected with a mobile robot show that our approach yields strong improvements over other approaches.
Comparing Clusterings by the Variation of Information

This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set. The criterion, called variation of information (VI), measures the amount of information lost and gained in changing from clustering \({\cal C}\) to clustering \({\cal C}'\). The criterion makes no assumptions about how the clusterings were generated and applies to both soft and hard clusterings. The basic properties of VI are presented and discussed from the point of view of comparing clusterings. In particular, the VI is positive, symmetric and obeys the triangle inequality. Thus, surprisingly enough, it is a true metric on the space of clusterings.
A new paradigm for recognizing 3-D objects from range data

Most of the work on 3D object recognition from range data has used an alignment-verification approach in which a specific 3D object is matched to an exact instance of the same object in a scene. This approach has been successfully used in industrial machine vision, but it is not capable of dealing with the complexities of recognizing classes of similar objects. This paper undertakes this task by proposing and testing a component-based methodology encompassing three main ingredients: 1) a new way of learning and extracting shape-class components from surface shape information; 2) a new shape representation called a symbolic surface signature that summarizes the geometric relationships among components; and 3) an abstract representation of shape classes formed by a hierarchy of classifiers that learn object-class parts and their spatial relationships from examples.
Adaptive real-time particle filters for robot localization

Particle filters have recently been applied with great success to mobile robot localization. This success is mostly due to their simplicity and their ability to represent arbitrary, multi-modal densities over a robot's state space. The increased representational power, however, comes at the cost of higher computational complexity. In this paper we introduce adaptive real-time particle filters that greatly increase the performance of particle filters under limited computational resources. Our approach improves the efficiency of state estimation by adapting the size of sample sets on-the-fly. Furthermore, even when large sample sets are needed to represent a robot's uncertainty, the approach takes every sensor measurement into account, thereby avoiding the risk of losing valuable sensor information during the update of the filter. We demonstrate empirically that this new algorithm drastically improves the performance of particle filters for robot localization.
A Comparison of Spectral Clustering Algorithms

Spectral Clustering has become quite popular over the last few years and several new algorithms have been published. In this paper, we compare several of the best-known algorithms from the point of view of clustering quality over artificial and real datasets. We implement many variations of the existing spectral algorithms and compare their performance to see which features are more important. We also demonstrate that spectral methods show competitive performance on real dataset with respect to existing methods.
Discriminating Deformable Shape Classes

We present and empirically test a novel approach for categorizing 3-D free form object shapes represented by range data. In contrast to traditional surface-signature based systems that use alignment to match specific objects, we adapted the newly introduced symbolic-signature representation to classify deformable shapes . Our approach constructs an abstract description of shape classes using an ensemble of classifiers that learn object class parts and their corresponding geometrical relationships from a set of numeric and symbolic descriptors. We used our classification engine in a series of large scale discrimination experiments on two well-defined classes that share many common distinctive features. The experimental results suggest that our method outperforms traditional numeric signature-based methodologies.
Real-time Particle Filters Using Mixtures of Samples Sets

Recently, particle filters have been applied with great success to a variety of state estimation problems. In many real time applications, sensor information arrives significantly faster than the particle filter can process. The prevalent approach to this problem is to update the particle filter as often as possible and to discard sensor information that cannot be processed in time. In this paper we present an alternative approach. Our real time particle filter makes use of all sensor information by partitioning sample sets into mixtures of smaller sample sets. Each small sample set contains as many samples as can be processed between the arrival of two observations. These small sets are combined as soon as the total number of samples in the mixture is sufficiently high. Using efficient subsampling, we determine the mixture weights so as to minimize the KL-divergence between the mixture density and the true posterior. Thereby, our approach focuses computational resources on valuable sensor information. Experiments using data collected with a mobile robot show that our approach yields drastic improvements over alternative techniques.
A new signature-based method for efficient 3-D object recognition

The paper considers the problem of shape-based recognition and pose estimation of 3D free-form objects in scenes that contain occlusion and clutter. Our approach is based on a novel set of discriminating descriptors called spherical spin images, which encode the shape information conveyed by classes of distributions of surface points constructed with respect to reference points on the surface of an object. The key to this approach is the relationship that exists between the l/sub 2/ metric, which compares n-dimensional signatures in Euclidean space, and the metric of the compact space on which the class representatives (spherical spin images) are defined. The connection allows us to efficiently utilize the linear correlation coefficient to discriminate scene points which have spherical spin images that are similar to the spherical spin images of points on the object being sought. The paper also addresses the problem of compressed spherical-spin-image representation by means of a random projection of the original descriptors that reduces the dimensionality without a significant loss of recognition/localization performance. Finally, the efficacy of the proposed representation is validated in a comparative study of the two algorithms presented that use uncompressed and compressed spherical spin images versus two previous spin image algorithms reported previously (A.E. Johnson and M. Hebert, 1999). The results of 2012 experiments suggest that the performance of our proposed algorithms is significantly better with respect to accuracy and speed than the performance of the other algorithms tested.
A Random Walks View of Spectral Segmentation

We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut method arises naturally from our framework and we provide a complete characterization of the cases when the Normalized Cut algorithm is exact. Then we discuss other spectral segmentation and clustering methods showing that several of them are essentially the same as NCut.
Intransitive Likelihood-Ratio Classifiers

In this work, we introduce an information-theoretic based correction term to the likelihood ratio classification method for multiple classes. Under certain conditions, the term is sufficient for optimally correcting the difference between the true and estimated likelihood ratio, and we analyze this in the Gaussian case. We find that the new correction term significantly improves the classification results when tested on medium vocabulary speech recognition tasks. Moreover, the addition of this term makes the class comparisons analogous to an intransitive game and we therefore use several tournament-like strategies to deal with this issue. We find that further small improvements are obtained by using an appropriate tournament. Lastly, we find that intransitivity appears to be a good measure of classification confidence.
Learning Segmentation by Random Walks

We present a new view of image segmentation by pairwise similarities. We interpret the similarities as edge flows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This interpretation shows that spectral methods for clustering and segmentation have a probabilistic foundation. In particular, we prove that the Normalized Cut method arises naturally from our framework. Finally, the framework provides a principled method for learning the similarity function as a combination of features.
Learning with Mixtures of Trees

This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu (1968) in a different and complementary direction to that of Bayesian networks. We present efficient algorithms for learning mixtures-of-trees models in maximum likelihood and Bayesian frameworks. We also discuss additional efficiencies that can be obtained when data are "sparse," and we present data structures and algorithms that exploit such sparseness. Experimental results demonstrate the performance of the model for both density estimation and classification. We also discuss the sense in which tree-based classifiers perform an implicit form of feature selection, and demonstrate a resulting insensitivity to irrelevant attributes.
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data

Chow and Liu introduced an algorithm for fitting a multivariate distribution with a tree (i.e. a density model that assumes that there are only pairwise dependencies between variables) and that the graph of these dependencies is a spanning tree. The original algorithm is quadratic in the dimesion of the domain, and linear in the number of data points that define the target distribution $P$. This paper shows that for sparse, discrete data, fitting a tree distribution can be done in time and memory that is jointly subquadratic in the number of variables and the size of the data set. The new algorithm, called the acCL algorithm, takes advantage of the sparsity of the data to accelerate the computation of pairwise marginals and the sorting of the resulting mutual informations, achieving speed ups of up to 2-3 orders of magnitude in the experiments.
Cached Sufficient Statistics for Automated Mining and Discovery from Massive Data Sources

There many massive databases in industry and science. There are also many ways that decision makers, scientists, and the public need to interact with these data sources. Wide ranging statistics and machine learning algorithms similarly need to query databases, sometimes millions of times for a single inference. With millions or billions of records (e.g. biotechnology databases, inventory management systems, astrophysics sky surveys, corporate sales information, science lab data repositories) this can be intractable using current algorithms. (a start-up company), both jointly run by Andrew Moore and Jee Schneider, are concerned with the fundamental computer science of making very advanced data analysis techniques computationally feasible for massive datasets. How can huge data sources (Gigabytes up to Terabytes) be analyzed automatically? There is no oo-the-shelf technology for this. There are devastating computational and statistical diiculties; manual analysis of such data sources is now passing from being simply tedious into a new, fundamentally impossible realm where the data sources are just too large to assimilate by humans. This situation is ironic given the large investment the US has put into gathering scientiic data. The only alternative is automated discovery. It is our thesis that the emerging technology of cached suucient statistics will be critical to developing automated discovery on massive data. A cached suucient statistics representation is a 1
Triangulation by Continuous Embedding

When triangulating a belief network we aim to obtain a junction tree of minimum state space. According to (Rose, 1970), searching for the optimal triangulation can be cast as a search over all the permutations of the graph's vertices. Our approach is to embed the discrete set of permutations in a convex continuous domain D. By suitably extending the cost function over D and solving the continous nonlinear optimization task we hope to obtain a good triangulation with respect to the aformentioned cost. This paper presents two ways of embedding the triangulation problem into continuous domain and shows that they perform well compared to the best known heuristic.
Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood.
Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections : Climate Teleconnection Mechanisms

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the indices, not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. 1. Institute for Human and Machine Cognition, University of West Florida 2. Department of Philosophy, Carnegie Mellon University Research for this paper was supported by the National Aeronautics and Space Administration Intelligent Data Understanding program and the Office of Naval Research. We thank Drs. Rama Nemani, Alessio Monetta and Peter Spirtes for valuable assistance.
The supposed competition between theories of human causal inference

Newsome (( 2003 ). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87–107.) recently published a critical review of psychological theories of human causal inference. In that review, he characterized covariation and mechanism theories, the two dominant theory types, as competing, and offered possible ways to integrate them. I argue that Newsome has misunderstood the theoretical landscape, and that covariation and mechanism theories do not directly conflict. Rather, they rely on distinct sets of reliable indicators of causation, and focus on different types of causation (type vs. token). There are certainly debates in the research field, but the theoretical landscape is not as fractured as Newsome suggests, and a potential unifying framework has already emerged using causal Bayes nets. Philosophical work on causal epistemology matters for psychologists, but not in the way Newsome suggests.
Scientific Coherence and the Fusion of Experimental Results

A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In this paper, we provide a positive answer to this question by developing several inference rules that use local causal models to place constraints on the integrated model, given quite general assumptions. We also demonstrate the practical value of these rules by applying them to a case study from ecology. 1. Experimental scope in applied sciences2. Fusing the results of experiments3. A concrete example of the inference rules4. Application to a case study Experimental scope in applied sciences Fusing the results of experiments A concrete example of the inference rules Application to a case study
Flexible automated monitoring and notification for complex processes using KARMEN

Automated monitoring for complex systems such as the space shuttle fueling and launch process can increase the effectiveness of operators in detecting abnormal conditions before become hazardous to the point they may compromise the mission. We have developed the KAoS reactive monitoring and event notification (KARMEN) multi-agent system to allow users to describe and change monitoring conditions at any time in order to effectively monitor such processes and appropriately notify key operations personnel about off-nominal conditions. These notification actions are overseen by policies that contain process requirements and users preferences.