Abstract: An information processing apparatus of the present disclosure includes: a region dividing unit that divides an instance input space of each of a plurality of machine learning models into a plurality of regions and assigns a probability to each of the division regions; a probability calculating unit that calculates a sampling probability on a predetermined instance belonging to the division region based on the probability assigned to the division region; and an instance selecting unit that selects the predetermined instance based on the sampling probability on the predetermined instance.

Abstract: An information processing apparatus (100) is disclosed. The information processing apparatus (100) includes an input means (102), a statistic calculation means (104) and an optimization means (106). The input means (102) receives input samples including responses and covariates. The statistic calculation means (104) transforms the responses into transformed samples using a function depending on the covariates and an unbiased parameter. A distribution of the transformed samples only depends on a dispersion parameter. The optimization means (106) maximizes a distribution of the transformed samples to determine an estimate of the dispersion parameter.

Abstract: An information processing apparatus includes: a calculation unit that adds an index value indicating a degree of uncertainty of a prediction, to each of a plurality of instances, on the basis of the prediction for each of the plurality of instances respectively outputted from a plurality of learning models; a selection unit that selects at least one instance, of which the added index value is included in a predetermined selection range, from the plurality of instances; and an output unit that outputs the selected at least one instance.

Abstract: In an information processing device, an input means accepts training examples formed by features. A label means assigns labels to the training examples. An error calculation means generates one or more student models using the training examples to which the labels are assigned, and calculates errors between predictions of the one or more student models and the labels. An error prediction model generation means generates an error prediction model which is a model for predicting the errors. An output means outputs each example for which the error is predicted to be significant based on the error prediction model.

Abstract: An information processing device, an input means receives training examples formed by features. A label generation means assigns labels to the training examples using a teacher model. An error calculation means generates one or more student models using at least a part of the training examples to which the labels are assigned, and calculates errors between predictions of the one or more student models and predictions of the teacher model by using the error calculation examples different from examples used to generate the one or more student models. A data retention means retains examples formed by features. A data extraction means extracts and outputs each example for which the error is to be significant based on the errors calculated by the error calculation means, from the data retention means.

Abstract: An object of the present disclosure is to provide an information processing apparatus, an information processing method, and a non-transitory computer readable medium capable of producing an accurate output to detect outlier(s). An information processing apparatus according to the present disclosure includes at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: calculate each probability of each data point being an outlier by using a temperature parameter t, wherein t>0; lower the temperature parameter t towards 0 with a plural of step; and output the probability.

Abstract: In an information processing device, an observation data input means receives a pair of observation data and a predicted value of a target model for the observation data. A rule set input means receive a rule set including a plurality of rules, the rule including a pair of a condition and a predicted value corresponding to the condition. A satisfying rule selection means selects a satisfying rule from the rule set, the satisfying rule being a rule in which the condition becomes true for the observation data. An error calculation means calculates an error between a predicted value of the satisfying rule for the observation data and the predicted value of the target model. A surrogate rule determination means associates the rule which minimizes the error, among the satisfying rules, with the observation data as a surrogate rule for the target model.

Abstract: A policy creation apparatus capable of creating policies with high quality and high visibility is provided. A rule creation unit (302) creates a plurality of rule sets including a plurality of rules that are a combination of a condition for determining a necessity of an action to be taken regarding an object and the action to be performed when the condition holds. An order determination unit (304) determines an order of the rules in each of the plurality of rule sets. An action determination unit (306) determines whether or not the condition holds in accordance with the determined order, and determines the action when the condition holds.

Abstract: In a rule generation apparatus, a rule generation unit generates a rule group for dividing a training example into a plurality of clusters related to target values using a rule base model so that a “first constraint” is satisfied. The training example includes at least one real example and at least one synthetic example. Each of the real and the synthetic examples includes a feature value vector of which vector elements are one or a plurality of feature values corresponding to feature parameters different from each other, and a target value. The feature value and the target value included in each of the real examples are measured values, while each of the synthetic examples is an example formed based on the real example. The “first constraint” includes a constraint that each of the clusters includes at least N (N is a natural number) real example.

Abstract: An information processing apparatus (10) includes: a formal language query accepting unit (12) that accepts a query expression and correct answer data; a semi-structured data accepting unit (14) that accepts semi-structured data that includes text nodes; a node text extraction unit (16) that extracts natural language text from the text node, as node text; a node text expression generation unit (18) that receives the node text from the a converter (100) and obtains node text expressions; an answer calculation unit (20) that calculates an answer to the query expression with use of the node text expressions; and an update unit (22) that, if the answer calculated by the answer calculation unit (20) matches the correct answer data, updates parameters in the converter (100) such that the corresponding node text expression is more likely to be output in the converter (100).

Abstract: An information processing apparatus 10 includes an acceptance unit 12, a formal language generation unit 14, an inference unit 16, and an update unit 18. The formal language generation unit 14 generates training expressions in a formal language based on parameters prepared in advance and pieces of text accepted by the acceptance unit 12. The inference unit 16 executes at least one inference out of deductive inference and abduction on the above training expressions using a knowledge base prepared in advance. The update unit 18 compares an inference result of the inference unit 16 with a model answer input in advance, and updates parameters such that an inference result that matches the model answer is likely to be obtained through inference performed by the inference unit 16.

Abstract: An information processing apparatus for determining a threshold on classification scores includes: a score ranking component that sorts all classification scores from samples of an evaluation data set that was not used for training the classifier and removes scores for which the class label is false; and an iteration component that iterates the threshold from the highest score returned from the score ranking component down until the number of samples with score not lower than the current threshold is larger than a user specified recall value times the number of true labels in the evaluation data set.

Abstract: An information processing device according to the present invention includes: a memory; and a processor coupled to the memory. The processor performs operations. The operations includes: generating, based on language data, a predicate argument structure including a predicate and an argument being an object of the predicate; generating first data indicating co-occurrence of the predicate and the argument in the predicate argument structure; decomposing the first data into a plurality of pieces of second data including fewer elements than elements included in the first data, and generating, based on the second data, third data including potential co-occurrence of the predicate and the argument; selecting the predicate argument structure by using the first data and the third data, and calculating, by using the third data, a score for a pair of the predicate argument structures including the selected predicate argument structure; and selecting the pair, based on the score.

Abstract: This invention provides an information processing apparatus for learning parameters to convert a question in a natural language into a query expression in a formal language, including an acceptor accepting an input of a set of a question in the natural language and a correct answer to the question, a condition generator generating condition(s) to be satisfied by the formal language input on searching a database for the correct answer, a query expression generator generating a query expression in the formal language corresponding to the question using parameters of the converter to satisfy any of the at least one condition, an answer acquirer acquiring an answer to the question based on a search of the database using the generated query expression, and a updater updating the parameters such that the question is converted with priority into the generated query expression when the answer and the correct answer match.

Abstract: A visualization device visualizes plural clustering results. The clustering result ordering unit orders plural clustering results based on quality criteria. Each of the clustering results includes covariate clusters. The hierarchical arrangement unit creates hierarchical tree structure including the covariate clusters as nodes. The created hierarchical structure is displayed.

Abstract: A density estimation unit 81 is given observed covariates and estimates a conditional probability density of a random variable, denoting the real value that is the result of a smooth function map of the unobserved covariates, by training a regression model with the response corresponding to the random variable, and the regressors corresponding to the observed covariates. An integral estimation unit 82 that estimates the one-dimensional integral of the product of a sigmoidal function with the input random variable and the conditional probability density function of the random variable.

Abstract: The input unit 81 receives a set of rules each including a condition and a prediction, and pairs of observed data and correct answers. The stochastic decision list generator 82 assigns each rule in the set of rules to a plurality of positions in the decision list with a degree of occurrence indicating occurrence degree. The learning unit 83 updates a parameter determining the degree of occurrence so that a difference between an integrated prediction acquired by integrating, based on the degree of occurrence, the predictions of the rules whose conditions are satisfied by the observed data and the correct answer becomes small.

Abstract: An information processing apparatus (2000) acquires input data (10). The information processing apparatus (2000) extracts a prediction rule (50) used for prediction related to the input data (10) from a usage rule set (60) by using a neural network (30). The usage rule set (60) includes a plurality of candidates for the prediction rule (50) used for prediction related to the input data (10). The prediction rule (50) is information in which condition data (52) representing a basis for prediction and conclusion data (54) representing a prediction related to the input data (10) are associated with each other. The prediction rule (50) used for prediction related to the input data (10) indicates the condition data (52) indicating a condition satisfied by the input data (10). The information processing apparatus (2000) outputs a prediction result (20), based on the conclusion data (54) indicating the extracted prediction rule (50).

Abstract: An information processing apparatus (10) includes: a formal language query accepting unit (12) that accepts a query expression and correct answer data; a semi-structured data accepting unit (14) that accepts semi-structured data that includes text nodes; a node text extraction unit (16) that extracts natural language text from the text node, as node text; a node text expression generation unit (18) that receives the node text from the a converter (100) and obtains node text expressions; an answer calculation unit (20) that calculates an answer to the query expression with use of the node text expressions; and an update unit (22) that, if the answer calculated by the answer calculation unit (20) matches the correct answer data, updates parameters in the converter (100) such that the corresponding node text expression is more likely to be output in the converter (100).

Abstract: An information processing device according to the present invention includes: a memory; and a processor coupled to the memory. The processor performs operations. The operations includes: generating, based on language data, a predicate argument structure including a predicate and an argument being an object of the predicate; generating first data indicating co-occurrence of the predicate and the argument in the predicate argument structure; decomposing the first data into a plurality of pieces of second data including fewer elements than elements included in the first data, and generating, based on the second data, third data including potential co-occurrence of the predicate and the argument; selecting the predicate argument structure by using the first data and the third data, and calculating, by using the third data, a score for a pair of the predicate argument structures including the selected predicate argument structure; and selecting the pair, based on the score.