nils-herrmann/clean_scirepeval huggingface dataset:
The Rational Basis of Representativeness

Representativeness is a central explanatory construct in cognitive science but suffers from the lack of a principled theoretical account. Here we present a formal definition of one sense of representativeness – what it means to be a good example of a process or category in the context of Bayesian inference. This analysis clarifies the relation between representativeness as an intuitive statistical heuristic and normative principles of inductive inference. It also leads to strong quantitative predictions about people’s judgments, which compare favorably to alternative accounts based on likelihood or similarity when evaluated on data from two experiments. Why do people think that Linda, the politically active, single, outspoken, and very bright 31-year-old, is more likely to be a feminist bankteller than to be a bankteller, even though this is logically impossible? Why do we think that the sequence HHTHT is more likely than the sequence HHHHH to be produced by flipping a fair coin, even though both are equally likely? The standard answer in cognitive psychology (Kahneman & Tversky, 1972) is that our brains are designed to judge “representativeness”, not probability: Linda is more representative of feminist banktellers than of banktellers, and HHTHT is more representative of flipping a fair coin than is HHHHH, despite anything that probability theory tells us. Not only errors in probabilistic reasoning, but numerous other phenomena of categorization, comparison, and inductive inference have been attributed to the influence of representativeness (or, under different names, prototypicality or “goodness of example”; Mervis & Rosch, 1981; Osherson, Smith, Wilkie, Lopez, & Shafir, 1990; Rips, 1975). However, a principled account of representativeness has not been easy to come by. Its leading proponents (Kahneman & Tversky, 1996; Mervis & Rosch, 1981) have asserted that representativeness should be defined only operationally, in terms of people’s direct judgments; an a priori, analytic definition need not be given. Consequently, critics have argued that the concept is either trivial or too vague to serve as an explanation of intuitive probability judgment (Gigerenzer, 1996). This paper presents a framework for constructing rational models of representativeness, based on a Bayesian analysis of what makes an observation a good example of a category or process. The goal of our analysis is to identify one precise sense of representativeness, and to show that it has a rational basis in normative principles of inductive reasoning. We will first point out some shortcomings of previous accounts of representativeness based on likelihood or similarity, and show how a Bayesian approach can overcome those problems. We will then compare the quantitative predictions of Bayesian, likelihood, and similarity models on two sizable datasets of representativeness judgments.
Graph approximations to geodesics on embedded manifolds

0  In  Tenenbaum, de Silva and Langford consider the problem of non-linear dimensionality reduction: discovering intrinsically low-dimensional structures embedded in high-dimensional data sets. They describe an algorithm, called Isomap, and demonstrate its successful application to several real and synthetic data sets. In this paper, we discuss some of the theoretical claims for Isomap made in . In particular, we give a full proof of the asymptotic convergence theorem referred to in that paper. Isomap deals with finite data sets of points in R n which are assumed to lie on a smooth submanifold M d of low dimension d < n. The algorithm attempts to recover M given only the data points. A crucial stage in the algorithm involves estimating the unknown geodesic distance in M between data points in terms of the graph distance with respect to some graph G constructed on the data points. We show that the two distance metrics approximate each other arbitrarily closely, as the density of data points tends to infinity.  The authors are listed alphabetically.
A global geometric framework for nonlinear dimensionality reduction.

Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.
Separating Style and Content with Bilinear Models

Perceptual systems routinely separate content from style, classifying familiar words spoken in an unfamiliar accent, identifying a font or handwriting style across letters, or recognizing a familiar face or object seen under unfamiliar viewing conditions. Yet a general and tractable computational model of this ability to untangle the underlying factors of perceptual observations remains elusive (Hofstadter, 1985). Existing factor models (Mardia, Kent, & Bibby, 1979; Hinton & Zemel, 1994; Ghahramani, 1995; Bell & Sejnowski, 1995; Hinton, Dayan, Frey, & Neal, 1995; Dayan, Hinton, Neal, & Zemel, 1995; Hinton & Ghahramani, 1997) are either insufficiently rich to capture the complex interactions of perceptually meaningful factors such as phoneme and speaker accent or letter and font, or do not allow efficient learning algorithms. We present a general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nonetheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization. We report promising results on three different tasks in three different perceptual domains: spoken vowel classification with a benchmark multi-speaker database, extrapolation of fonts to unseen letters, and translation of faces to novel illuminants.
Structure Learning in Human Causal Induction

We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayesian inference. Our argument is supported through the discussion of three data sets.
Rules and Similarity in Concept Learning

This paper argues that two apparently distinct modes of generalizing concepts - abstracting rules and computing similarity to exemplars - should both be seen as special cases of a more general Bayesian learning framework. Bayes explains the specific workings of these two modes - which rules are abstracted, how similarity is measured - as well as why generalization should appear rule - or similarity-based in different situations. This analysis also suggests why the rules/similarity distinction, even if not computationally fundamental, may still be useful at the algorithmic level as part of a principled approximation to fully Bayesian learning.
An example-based approach to style translation for line drawings

We present an example-based system for translating line drawings into di erent styles. The system is given a training set of many di erent lines, each drawn by an artist in various styles, which is used to translate new lines made by a user into a particular desired style with a K-nearest neighbor algorithm. This algorithm ts each input line as a linear combination of the several training lines in the same style which are most similar to it. The t line can then be rendered in di erent styles because the training set contains versions of each training line in each style. By describing input lines as linear combinations of training set lines, this procedure is expressive enough to t a broad range of input drawings. By restricting these linear combinations to contain only the most similar training set lines, this procedure is constrained enough to preserve the distinctive stylistic features of translated lines. We represent input lines by splines with nonuniformly spaced control points, which emphasizes these stylistic features. Our example-based approach has a number of advantages over conventional parameteric approaches to translating style. It can handle styles which are di cult to describe parametrically, and its repertoire can be easily extended by the user at any time. Moreover, given appropriate representations, it can be generalized to modify the style of other kinds of graphics objects, such as the font of a letter or the movement style of an animated character.
Bayesian Modeling of Human Concept Learning

I consider the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely but which computers are rarely capable of. Bridging machine learning and cognitive science perspectives, I present both theoretical analysis and an empirical study with human subjects for the simple task oflearning concepts corresponding to axis-aligned rectangles in a multidimensional feature space. Existing learning models, when applied to this task, cannot explain how subjects generalize from only a few examples of the concept. I propose a principled Bayesian model based on the assumption that the examples are a random sample from the concept to be learned. The model gives precise fits to human behavior on this simple task and provides qualitative insights into more complex, realistic cases of concept learning.
Analyzing the view dependence of population codes in inferior temporal cortex

The recent discovery of cells in inferior temporal (IT) cortex that show responses selective for specific views of learned wire-like objects [4] supports a computational model of object recognition built on two-dimensional view-based representations [7] over models based on three-dimensional structural descriptions , in line with many psychophysical demonstrations of view-dependent recognition performance . However, many more IT cells show significantly view-sensitive responses without strict view-tuning, which suggests that an analysis of population encodings may be necessary to fully understand the nature of object representation in IT. By analyzing how the similarity of IT population responses to different views of two objects rotated in depth depends on the orientation difference between the views, we provide further evidence that neural representations of object shape depend on abstract two-dimensional views (possibly built from collections of image features), and demonstrate a clear correspondence between the view dependence of IT population responses and the view dependence of human observers’ similarity judgments.
Learning bilinear models for two-factor problems in vision

In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color constancy; rendering conditions from surface shape in shape-from-shading; face identity and head pose in face recognition; or font and letter class in character recognition. We refer to these two factors generically as "style" and "content". Bilinear models offer a powerful framework for extracting the two-factor structure of a set of observations, and are familiar in computational vision from several well-known lines of research. This paper shows how bilinear models can be used to learn the style-content structure of a pattern analysis or synthesis problem, which can then be generalized to solve related tasks using different styles and/or content. We focus on three tasks: extrapolating the style of data to unseen content classes, classifying data with known content under a novel style, and translating data from novel content classes and style to a known style or content. We show examples from color constancy, face pose estimation, shape-from-shading, typography and speech.
Author ' s personal copy Inductive reasoning about causally transmitted properties q

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates’ context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.
Mapping a Manifold of Perceptual Observations

Nonlinear dimensionality reduction is formulated here as the problem of trying to find a Euclidean feature-space embedding of a set of observations that preserves as closely as possible their intrinsic metric structure - the distances between points on the observation manifold as measured along geodesic paths. Our isometric feature mapping procedure, or isomap, is able to reliably recover low-dimensional nonlinear structure in realistic perceptual data sets, such as a manifold of face images, where conventional global mapping methods find only local minima. The recovered map provides a canonical set of globally meaningful features, which allows perceptual transformations such as interpolation, extrapolation, and analogy - highly nonlinear transformations in the original observation space - to be computed with simple linear operations in feature space.
in Adv. in Neural Info. Proc. Systems, volume 9, MIT Press, 1997.

We seek to analyze and manipulate two factors, which we generically call style and content, underlying a set of observations. We t training data with bilinear models which explicitly represent the two-factor structure. These models can adapt easily during testing to new styles or content, allowing us to solve three general tasks: extrapolation of a new style to unobserved content; classi cation of content observed in a new style; and translation of new content observed in a new style. For classi cation, we embed bilinear models in a probabilistic framework, Separable Mixture Models (SMMs), which generalizes earlier work on factorial mixture models [7, 3]. Signi cant performance improvement on a benchmark speech dataset shows the bene ts of our approach.
Separating Style and Content

We seek to analyze and manipulate two factors, which we call style and content, underlying a set of observations. We fit training data with bilinear models which explicitly represent the two-factor structure. These models can adapt easily during testing to new styles or content, allowing us to solve three general tasks: extrapolation of a new style to unobserved content; classification of content observed in a new style; and translation of new content observed in a new style. For classification, we embed bilinear models in a probabilistic framework, Separable Mixture Models (SMMs), which generalizes earlier work on factorial mixture models [7, 3]. Significant performance improvement on a benchmark speech dataset shows the benefits of our approach.
Learning the Structure of Similarity

The additive clustering (ADCLUS) model (Shepard & Arabie, 1979) treats the similarity of two stimuli as a weighted additive measure of their common features. Inspired by recent work in unsupervised learning with multiple cause models, we propose a new, statistically well-motivated algorithm for discovering the structure of natural stimulus classes using the ADCLUS model, which promises substantial gains in conceptual simplicity, practical efficiency, and solution quality over earlier efforts. We also present preliminary results with artificial data and two classic similarity data sets.
Intrinsically Motivated Reinforcement Learning: A Promising Framework for Developmental Robot Learning

One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex behavior in a self-motivated, open-ended way Barto, Singh, and Chentanez (Barto, Singh, & Chentanez 2004; Singh, Barto, & Chentanez 2004) have recently presented an algorithm for intrinsically motivated reinforcement learning that strives to achieve broad competence in an environment in a task-nonspecific manner by incorporating internal reward to build a hierarchical collection of skills. This paper suggests that with its emphasis on task-general, self-motivated, and hierarchical learning, intrinsically motivated reinforcement learning is an obvious choice for organizing behavior in developmental robotics. We present additional preliminary results from a gridworld abstraction of a robot environment and advocate a layered learning architecture for applying the algorithm on a physically embodied system.
Identifying useful subgoals in reinforcement learning by local graph partitioning

We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphs---those that are constructed using only the most recent experiences of the agent. The local scope of our subgoal discovery method allows it to successfully identify the type of subgoals we seek---states that lie between two densely-connected regions of the state space while producing an algorithm with low computational cost.
Evaluating the Feasibility of Learning Student Models from Data

Recent work on intelligent tutoring systems has used Bayesian networks to model students’ acquisition of skills. In many cases, researchers have hand-coded the parameters of the networks, arguing that the conditional probabilities of models containing hidden variables are too difficult to learn from data. We present a machine learning approach that uses Expectation-Maximization to learn the parameters of a dynamic Bayesian network with hidden variables. We test our methodology on data that was simulated using a state-based model of skill acquisition. Results indicate that it is possible to learn the parameters of hidden variables given enough sequential data of training sessions on similar problems.
Approximate optimal control as a model for motor learning.

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein's (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm.
A causal approach to hierarchical decomposition of factored MDPs

We present Variable Influence Structure Analysis, an algorithm that dynamically performs hierarchical decomposition of factored Markov decision processes. Our algorithm determines causal relationships between state variables and introduces temporally-extended actions that cause the values of state variables to change. Each temporally-extended action corresponds to a subtask that is significantly easier to solve than the overall task. Results from experiments show great promise in scaling to larger tasks.
Control models of natural language parsing

Most recent statistical parsers fall into one of two groups. The largest group consists of parsers which are based on some variation of a probabilistic context-free grammar, use joint probability models, and use tabular methods to find the most probable parse. Parsers in the second group are based on probabilistic push-down automata, use conditional probability models, and use some form of state-space search to find the most probable parse. 
This thesis is a study of natural language parsing as a control problem. This view leads to parsers of the second type. We show that search can be done very efficiently for such parsers. The control approach leads to a particular interpretation of the history-based parsing tradition, in which history is equated with state. The corresponding probability model is called a Markov parsing model, which can be used both for syntactic disambiguation and for search. The resulting parsers are simple, fast, have excellent coverage, and are reasonably accurate. 
Using treebanks (collections of text, which are expert-annotated with syntactic structure), we learn controllers for parsers that can be applied with little or no search. We call these greedy or nearly-greedy policies. Thus we are studying parsers which are constrained to operate efficiently.
Learning Skills in Reinforcement Learning Using Relative Novelty

We present a method for automatically creating a set of useful temporally-extended actions, or skills, in reinforcement learning. Our method identifies states that allow the agent to transition to a different region of the state space—for example, a doorway between two rooms—and generates temporally-extended actions that efficiently take the agent to these states. In identifying such states we use the concept of relative novelty, a measure of how much short-term novelty a state introduces to the agent. The resulting algorithm is simple, has low computational complexity, and is shown to improve performance in a number of problems.
Intrinsic Motivation For Reinforcement Learning Systems

Motivation is a key factor in human learning. We learn best when we are highly motivated to learn. Psychologists distinguish between extrinsically-motivated behavior, which is behavior undertaken to achieve some externally supplied reward, such as a prize, a high grade, or a high-paying job, and intrinsically-motivated behavior, which is behavior done for its own sake. Is there an analogous distinction for machine learning systems? Can we say of a machine learning system that it is motivated to learn, and if so, can it be meaningful to distinguish between extrinsic and intrinsic motivation? In this paper, we argue that the answer to both questions is “yes,” and we describe some computational experiments that explore these ideas within the framework of computational reinforcement learning. In particular, we describe an approach by which artificial agents can learn hierarchies of reusable skills through a computational analog of intrinsic motivation.