Abstract: A computer-implemented method is provided for learning with incomplete data in which some of entries are missing. The method includes acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete set of covariates {tilde over (x)}. The method further includes obtaining, by a hardware processor, a predictive distribution p?(y|x) of an outcome y by using the incomplete set of covariates x and a parameter ?, the parameter ? being unknown. A learning of the parameter ? includes performing a maximization by maximizing a stochastically approximated conditional evidence lower bound.

Abstract: A computer-implemented method is provided for policy evaluation. In the method, the utility of the given decision-making policy is estimated based on a dataset of state-action-reward-state tuples, a set of candidate bootstrapping estimators of the fitted Q-evaluation (FQE) algorithm, and a criterion function. The method automatically selects the best bootstrapping estimator from the candidates based on the criterion function and, when the criterion function is appropriately designed, it produces a good policy-value estimate such that the estimation error is small (below a threshold).

Abstract: A computer-implemented method for training a lattice layer in a Deep Lattice Network includes preparing parameters of vertices, each of the parameters corresponding to each vertex of a subdivided unit hypercube defined by subdividing an S-dimensional unit hypercube by a s predetermined number k with k vertices and defining each parameter by identifying one vertex in a specific order, identifying a first set of vertices that appear before the identified vertex in the specific order, identifying a second set of vertices that appear before the identified vertex in the specific order, defining a lower bound as a maximum value among values of vertices in the first set of vertices, defining an upper bound as a minimum value among values of vertices in the second set of vertices, and defining the parameter of the identified vertex based on the lower bound, the upper bound, and a parameter corresponding to the identified vertex.

Abstract: Methods and systems for learning a policy model include determining an imitation learning expert policy. A policy model neural network is iteratively trained using the determined imitation learning expert policy, including modifying the policy model neural network at iteration to decrease a difference between an output of the policy model neural network and a target signal that is based on the determined imitation learning expert policy.

Abstract: A computer-implemented method is provided for data reduction in a memory device for machine learning. The method includes storing, in the memory device, data that has been used for training in a tree-based fitted Q iteration session which learns an action value function with an ensemble of decision trees from the data. The method further includes determining, by a processor device, samples to be removed from the data based on a number of samples which belong to leaf nodes of the decision trees. The method also includes removing, from the memory device, the determined samples from the data to reduce an amount of the data. The method additionally includes learning, by the processor device, a new ensemble of decision trees using the data from which the determined samples have been removed together with new data.

Abstract: A computer-implemented method for computing an objective function of discriminative inference with generative models with incomplete data in which some of entries are missing is provided including acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete features {tilde over (x)} and computing a predictive distribution p?(y|x) of an outcome y by using the incomplete set of covariates x and a parameter ?, the parameter ? being unknown. Learning of the parameter ? is performed by minimizing an objective function (?):=?ln p?(y|x)=ln p?({tilde over (x)}|m)?ln p?(y,x|m), and the objective function (?) is bounded with a difference between a marginal evidence upper bound MEUBO and a joint evidence lower bound JELBO, where ln p?({tilde over (x)}|m)?MEUBO and ln p?(y,{tilde over (x)}|m)?JELBO.

Abstract: A computer-implemented method for implementing a computer system task involving streaming data by removing biased gradients from memory includes generating a parameter sequence including a plurality of parameters corresponding to respective iteration counts. Generating the parameter sequence includes obtaining a first parameter value corresponding to a given iteration count by updating memory corresponding to the given iteration count based on a second parameter value corresponding to a prior iteration count, adapting a size of the updated memory to remove biased gradients, and obtaining the first parameter value by performing a step of a gradient descent method based on the adaptation and the second parameter value. The method further includes learning a time-series model based on the parameter sequence, and implementing a computer system task using the time-series model.

Abstract: A computer-implemented method is provided for predicting a medical event time. The method includes receiving an electronic health record (EHR) including a plurality of pairs of observation variables and corresponding timestamps. Each of the plurality of pairs include a respective observation variable and a respective corresponding timestamp. The method further includes converting the EHR into a K-dimensional vector representing a cumulative-stay time at a finite number of patient medical states, the patient medical states being determined by values of the observation variables. The method also includes processing, by a hardware processor, the K-dimensional vector using a medical event time prediction model to output a prediction of a medical event time. The medical event time prediction model has been configured through training to receive and process K-dimensional vectors converted from past EHRs to output predicted medical event times.

Abstract: A method is presented for estimating conditional quantile values of a response variable distribution. The method includes acquiring training data with first values and second values, a list of quantile levels, a lower bound of the second values, and an upper bound of the second values and transforming the list of quantile levels into a tree-structure by recursively dividing an interval in a range between 0 and 1 into sub-intervals by using the list of quantile levels such that each node of the tree-structure is associated with a tuple of three quantile levels. The method further includes training a neural network for each node in the tree-structure and estimating a relative quantile value for each of the first values by using a first estimated quantile value as a lower bound and a second estimated quantile value as an upper bound.

Abstract: Determinantal Point Process-based predictions are provided by training an asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by calculating an inverse matrix of a sum of the asymmetric kernel and a first identity matrix, the calculating using an inverse of a sum of the first identity matrix and a symmetric positive semidefinite matrix, a concatenated matrix made from a first matrix and a second matrix and a second identity matrix, the asymmetric kernel including the symmetric positive semidefinite matrix and a skewed-symmetric matrix, the skewed-symmetric matrix being calculated from the first matrix and the second matrix, to produce a prediction model, and outputting the asymmetric kernel as at least a part of the prediction model to make a prediction.

Abstract: A computer-implemented method for implementing a computer system task involving nonstationary streaming time-series data based on a bias-variance-based adaptive learning rate includes generating a parameter sequence including a plurality of parameters corresponding to respective iteration counts. Generating the parameter sequence includes obtaining a first parameter value corresponding to a given iteration count by calculating estimators of moments associated with an objective function corresponding to the given iteration count based on a second parameter value corresponding to a prior iteration count using a sequential mean tracking method, and obtaining the first parameter value by performing a step of a gradient descent method based on the calculated moments and the second parameter value. The method further includes learning a time-series model based on the parameter sequence, and implementing a computer system task using the time-series model.

Abstract: A computer-implemented method for implementing a computer system task involving streaming data by removing biased gradients from memory includes generating a parameter sequence including a plurality of parameters corresponding to respective iteration counts. Generating the parameter sequence includes obtaining a first parameter value corresponding to a given iteration count by updating memory corresponding to the given iteration count based on a second parameter value corresponding to a prior iteration count, adapting a size of the updated memory to remove biased gradients, and obtaining the first parameter value by performing a step of a gradient descent method based on the adaptation and the second parameter value. The method further includes learning a time-series model based on the parameter sequence, and implementing a computer system task using the time-series model.