nils-herrmann/clean_scirepeval huggingface dataset:
Modular decomposition in visuomotor learning

The principle of 'divide-and-conquer', the decomposition of a complex task into simpler subtasks each learned by a separate module, has been proposed as a computational strategy during learning1–3. We explore the possibility that the human motor system uses such a modular decomposition strategy to learn the visuomotor map, the relationship between visual inputs and motor outputs. Using a virtual reality system, subjects were exposed to opposite prism-like visuomotor remappings—discrepancies between actual and visually perceived hand locations— for movements starting from two distinct locations. Despite this conflicting pairing between visual and motor space, subjects learned the two starting-point-dependent visuomotor mappings and the generalization of this learning to intermediate starting locations demonstrated an interpolation of the two learned maps. This interpolation was a weighted average of the two learned visuomotor mappings, with the weighting sigmoidally dependent on starting location, a prediction made by a computational model of modular learning known as the "mixture of experts"1. These results provide evidence that the brain may employ a modular decomposition strategy during learning.
The EM algorithm for mixtures of factor analyzers

Factor analysis, a statistical method for modeling the covariance structure of high dimensional data using a small number of latent variables, can be extended by allowing di erent local factor models in di erent regions of the input space. This results in a model which concurrently performs clustering and dimensionality reduction, and can be thought of as a reduced dimension mixture of Gaussians. We present an exact Expectation{Maximization algorithm for tting the parameters of this mixture of factor analyzers.
Parameter estimation for linear dynamical systems

Linear systems have been used extensively in engineering to model and control the behavior of dynamical systems. In this note, we present the Expectation Maximization (EM) algorithm for estimating the parameters of linear systems (Shumway and Sto er, 1982). We also point out the relationship between linear dynamical systems, factor analysis, and hidden Markov models.  The goal of this note is to introduce the EM algorithm for estimating the parameters of linear dynamical systems (LDS). Such linear systems can be used both for supervised and unsupervised modeling of time series. We rst describe the model and then brie y point out its relation to factor analysis and other data modeling techniques. The Model Linear time-invariant dynamical systems, also known as linear Gaussian state-space models, can be described by the following two equations: xt+1 = Axt +wt (1) yt = Cxt+ vt: (2) Time is indexed by the discrete index t. The output yt is a linear function of the state, xt, and the state at one time step depends linearly on the previous state. Both state and output noise, wt and vt, are zero-mean normally distributed random variables with covariance matrices Q and R, respectively. Only the output of the system is observed, the state and all the noise variables are hidden. Rather than regarding the state as a deterministic value corrupted by random noise, we combine the state variable and the state noise variable into a single Gaussian random
Generalization to Local Remappings of the Visuomotor Coordinate Transformation

During visually guided movement, visual representations of target location must be transformed into coordinates appropriate for movement. To investigate the representation and plasticity of the visuomotor coordinate transformation, we examined the changes in pointing behavior after local visuomotor remappings. The visual feedback of finger position was limited to one or two locations in the workspace, at which a discrepancy was introduced between the actual and visually perceived finger position. These remappings induced changes in pointing, which were largest near the locus of remapping and decreased away from it. This pattern of spatial generalization highly constrains models of the computation of the visuomotor transformation in the CNS. A simple model, in which the transformation is computed via the population activity of a set of units with large sensory receptive fields, is shown to capture the observed pattern.
The delve manual

This manual describes the preliminary release of the DELVE environment. Some features described here have not yet implemented, as noted. Support for regression tasks is presently somewhat more developed than that for classiication tasks. We recommend that you exercise caution when using this version of DELVE for real work, as it is possible that bugs remain in the software. We hope that you will send us reports of any problems you encounter, as well as any other comments you may have on the software or manual, at the e-mail address below. Please mention the version number of the manual and/or the software with any comments you send. All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for non-commercial purposes only is hereby granted without fee, provided that the above copyright notice appears in all copies and that both the copyright notice and this permission notice appear in supporting documentation, and that the name of The University of Toronto not be used in advertising or publicity pertaining to distribution of the software without speciic, written prior permission. The University of Toronto makes no representations about the suitability of this software for any purpose. It is provided \as is" without express or implied warranty. The University of Toronto disclaims all warranties with regard to this software, including all implied warranties of merchantability and tness. In no event shall the University of Toronto be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or proots, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of this software. If you publish results obtained using DELVE, please cite this manual, and mention the version number of the software that you used.
Hidden Markov Decision Trees

We study a time series model that can be viewed as a decision tree with Markov temporal structure. The model is intractable for exact calculations, thus we utilize variational approximations. We consider three different distributions for the approximation: one in which the Markov calculations are performed exactly and the layers of the decision tree are decoupled, one in which the decision tree calculations are performed exactly and the time steps of the Markov chain are decoupled, and one in which a Viterbi-like assumption is made to pick out a single most likely state sequence. We present simulation results for artificial data and the Bach chorales.
Switching State-Space Models

We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time series models|the hidden Markov model and the linear dynamical system|and is related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network model (Jacobs et al., 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) alogithm cannot be applied. However, we present a variational approximation which maximizes a lower bound on the log likelihood and makes use of both the forward{backward recursions for hidden Markov models and the Kalman lter recursions for linear dynamical systems.
Computation and psychophysics of sensorimotor integration

All higher organisms are able to integrate information from multiple sensory modalities and use this information to select and guide movements. In order to do this, the central nervous system (CNS) must solve two problems: (1) Converting information from distinct sensory representations into a common coordinate system, and (2) integrating this information in a sensible way. This dissertation proposes a computational framework, based on statistics and information theory, to study these two problems. The framework suggests explicit models for both the coordinate transformation and integration problems, which are tested through human psychophysics. 
The experiments in Chapter 2 suggest that: (1) Spatial information from the visual and auditory systems is integrated so as to minimize the variance in localization. (2) When the relation between visual and auditory space is artificially remapped, the spatial pattern of auditory adaptation can be predicted from its localization variance. These studies suggest that multisensory integration and intersensory adaptation are closely related through the principle of minimizing localization variance. This principle is used to model sensorimotor integration of proprioceptive and motor signals during arm movements (Chapter 3). The temporal propagation of errors in estimating the hand's state is captured by the model, providing support for the existence of an internal model in the CNS that simulates the dynamic behavior of the arm. 
The coordinate transformation problem is examined in the visuomotor system, which mediates reaching to visually-perceived objects (Chapter 4). The pattern of changes induced by a local remapping of this transformation suggests a representation based on units with large functional receptive fields. Finally, the problem of converting information from disparate sensory representations into a common coordinate system is addressed computationally (Chapter 5). An unsupervised learning algorithm is proposed based on the principle of maximizing mutual information between two topographic maps. What results is an algorithm which develops multiple, mutually-aligned topographic maps based purely on correlations between the inputs to the different sensory modalities. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.
An internal model for sensorimotor integration.

On the basis of computational studies it has been proposed that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning; the existence and use of such an internal model is still under debate. A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces. The temporal propagation of errors in this task was analyzed within the theoretical framework of optimal state estimation. These results provide direct support for the existence of an internal model.
Active Learning with Statistical Models

For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.
Minimizing regret with label efficient prediction

We investigate label efficient prediction, a variant, proposed by Helmbold and Panizza, of the problem of prediction with expert advice. In this variant, the forecaster, after guessing the next element of the sequence to be predicted, does not observe its true value unless he asks for it, which he cannot do too often. We determine matching upper and lower bounds for the best possible excess prediction error, with respect to the best possible constant predictor, when the number of allowed queries is fixed. We also prove that Hannan consistency, a fundamental property in game-theoretic prediction models, can be achieved by a forecaster issuing a number of queries growing to infinity at a rate just slightly faster than logarithmic in the number of prediction rounds.
A distributed architecture for management and retrieval of extended points of interest

This paper presents a distributed architecture for the management and retrieval of particular objects called POIsmarts. POIsmarts can be considered as the convergence between virtual points of interest (Web bookmarks) and physical points of interest (GPS bookmarks). We provide a XML schema defining the structure and properties of POIsmarts and we show how these objects can be stored, shared, searched and accessed in a uniform way independently from the user device. The paper also reports preliminary results on performing automatic categorization and search (in personal or shared POIsmart hierarchies) using machine learning techniques.
Towards Highly Adaptive Services for Mobile Computing

The heterogeneity of device capabilities, network conditions and user contexts that is associated with mobile computing has emphasized the need for more advanced forms of adaptation of Internet services. This paper presents a framework that addresses this issue by managing distributed profile information and adaptation policies, solving possible conflicts by means of an inference engine and prioritization techniques. The profile information considered in the framework is very broad, including user preferences, device and network capabilities, and user location and context. The framework has been validated by experiments on the efficiency of the proposed conflict resolution mechanism, and by the implementation of the main components of the architecture. The paper also illustrates a specific testbed application in the context of proximity marketing.
Finite-time Analysis of the Multiarmed Bandit Problem

Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy's success in addressing this dilemma is the regret, that is the loss due to the fact that the globally optimal policy is not followed all the times. One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve this regret have been devised by Lai and Robbins and many others. In this work we show that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions

We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm using a linear-threshold classifier at each node of the taxonomy. These classifiers are trained and evaluated in a hierarchical top-down fashion. We then define a hierachical and parametric data model and prove a bound on the probability that our algorithm guesses the wrong multilabel for a random instance compared to the same probability when the true model parameters are known. Our bound decreases exponentially with the number of training examples and depends in a detailed way on the interaction between the process parameters and the taxonomy structure. Preliminary experiments on real-world data provide support to our theoretical results.
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms

We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, i.e., algorithms which can be efficiently run in any reproducing kernel Hilbert space. Our algorithms exploit a simple margin-based randomized rule to decide whether to query the current label. We obtain selective sampling algorithms achieving on average the same bounds as those proven for their deterministic counterparts, but using much fewer labels. We complement our theoretical findings with an empirical comparison on two text categorization tasks. The outcome of these experiments is largely predicted by our theoretical results: Our selective sampling algorithms tend to perform as good as the algorithms receiving the true label after each classification, while observing in practice substantially fewer labels.
Incremental Algorithms for Hierarchical Classification

We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function, the H-loss, implementing the simple intuition that additional mistakes in the subtree of a mistaken class should not be charged for. Based on a probabilistic data model introduced in earlier work, we derive the Bayes-optimal classifier for the H-loss. We then empirically compare two incremental approximations of the Bayes-optimal classifier with a flat SVM classifier and with classifiers obtained by using hierarchical versions of the Perceptron and SVM algorithms. The experiments show that our simplest incremental approximation of the Bayes-optimal classifier performs, after just one training epoch, nearly as well as the hierarchical SVM classifier (which performs best). For the same incremental algorithm we also derive an H-loss bound showing, when data are generated by our probabilistic data model, exponentially fast convergence to the H-loss of the hierarchical classifier based on the true model parameters.
Minimizing Regret with Label Efficient Prediction

We investigate label efficient prediction, a variant of the problem of prediction with expert advice, proposed by Helmbold and Panizza, in which the forecaster does not have access to the outcomes of the sequence to be predicted unless he asks for it, which he can do for a limited number of times. We determine matching upper and lower bounds for the best possible excess error when the number of allowed queries is a constant. We also prove that a query rate of order (ln n) (ln ln n)2/n is sufficient for achieving Hannan consistency, a fundamental property in game-theoretic prediction models. Finally, we apply the label efficient framework to pattern classification and prove a label efficient mistake bound for a randomized variant of Littlestone’s zero-threshold Winnow algorithm.
Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling

In this paper we investigate selective sampling, a learning model where the learner observes a sequence of i.i.d. unlabeled instances each time deciding whether to query the label of the current instance. We assume that labels are binary and stochastically related to instances via a linear probabilistic function whose coefficients are arbitrary and unknown. We then introduce a new selective sampling rule and show that its expected regret (with respect to the classifier knowing the underlying linear function and observing the label realization after each prediction) grows not much faster than the number of sampled labels. Furthermore, under additional assumptions on the true margin distribution, we prove that the number of sampled labels grows only logarithmically in the number of observed instances. Experiments carried out on a text categorization problem show that: (1) our selective sampling algorithm performs better than the Perceptron algorithm even when the latter is given the true label after each classification; (2) when allowed to observe the true label after each classification, the performance of our algorithm remains the same. Finally, we note that by expressing our selective sampling rule in dual variables we can learn nonlinear probabilistic functions via the kernel machinery.
Integrated Profile Management for Mobile Computing

In this paper we identify the main requisites of an infrastructure for the integrated management of profile data and propose a high-level description of its implementation. The main goal of our system is to offer to mobile users contents targeted to their needs, using a presentation suited to their device. This task can be accomplished by offering to content providers every information that can be useful for identifying the content and the presentation that best fit the user’s expectations and the device capabilities. We identify the entities that are more feasible for providing profile data, and propose a mechanism for retrieving and integrating information from the different sources in this distributed environment. The presentation and content directives to be applied are determined by evaluating rules declared by both users and content providers, resolving possible conflicts.
Adaptive and Self-Confident On-Line Learning Algorithms

Most of the performance bounds for on-line learning algorithms are proven assuming a constant learning rate. To optimize these bounds, the learning rate must be tuned based on quantities that are generally unknown, as they depend on the whole sequence of examples. In this paper we show that essentially the same optimized bounds can be obtained when the algorithms adaptively tune their learning rates as the examples in the sequence are progressively revealed. Our adaptive learning rates apply to a wide class of on-line algorithms, including p-norm algorithms for generalized linear regression and Weighted Majority for linear regression with absolute loss. We emphasize that our adaptive tunings are radically different from previous techniques, such as the so-called doubling trick. Whereas the doubling trick restarts the on-line algorithm several times using a constant learning rate for each run, our methods save information by changing the value of the learning rate very smoothly. In fact, for Weighted Majority over a finite set of experts our analysis provides a better leading constant than the doubling trick.
The Nonstochastic Multiarmed Bandit Problem

In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. 
In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the per-round payoff of our algorithm approaches that of the best arm at the rate O(T-1/2). We show by a matching lower bound that this is the best possible. 
We also prove that our algorithm approaches the per-round payoff of any set of strategies at a similar rate: if the best strategy is chosen from a pool of N strategies, then our algorithm approaches the per-round payoff of the strategy at the rate O((log N1/2 T-1/2). Finally, we apply our results to the problem of playing an unknown repeated matrix game. We show that our algorithm approaches the minimax payoff of the unknown game at the rate O(T-1/2).