Multi-task additive models with shared transfer functions based on dictionary learning

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is their interpretability: the transfer functions provide visual means for inspecting the models and identifying domain-specific relations between inputs and outputs. However, in large-scale problems involving the prediction of many related tasks, learning independently additive models results in a loss of model interpretability, and can cause overfitting when training data is scarce. We introduce a novel multi-task learning approach which provides a corpus of accurate and interpretable additive models for a large number of related forecasting tasks. Our key idea is to share transfer functions across models in order to reduce the model complexity and ease the exploration of the corpus. We establish a connection with sparse dictionary learning and propose a new efficient fitting algorithm which alternates between sparse coding and transfer function updates. The former step is solved via an extension of Orthogonal Matching Pursuit, whose properties are analyzed using a novel recovery condition which extends existing results in the literature. The latter step is addressed using a traditional dictionary update rule. Experiments on real-world data demonstrate that our approach compares favorably to baseline methods while yielding an interpretable corpus of models, revealing structure among the individual tasks and being more robust when training data is scarce. Our framework therefore extends the well-known benefits of additive models to common regression settings possibly involving thousands of tasks

Multimodal Multipart Learning for Action Recognition in Depth Videos

The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy

Structured Learning in Time-dependent Cox Models

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (i.e., covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. Sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.Comment: 49 pages (with 19 pages of appendix),9 tables, 3 figure

Searching for Prosociality in Qualitative Data: Comparing Manual, Closed-Vocabulary, and Open-Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self–presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants’ self–described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample ( N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample ( N = 496). Raters’ attempts to predict donations to charity based on reading participants’ personal strivings were also unsuccessful. However, raters’ predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open–ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psycholog