Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models

Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification. Generally fitted by maximum likelihood estimation via the well-known EM algorithm, their application to high-dimensional problems is still therefore challenging. We consider the problem of fitting and feature selection in MoE models, and propose a regularized maximum likelihood estimation approach that encourages sparse solutions for heterogeneous regression data models with potentially high-dimensional predictors. Unlike state-of-the art regularized MLE for MoE, the proposed modelings do not require an approximate of the penalty function. We develop two hybrid EM algorithms: an Expectation-Majorization-Maximization (EM/MM) algorithm, and an EM algorithm with coordinate ascent algorithm. The proposed algorithms allow to automatically obtaining sparse solutions without thresholding, and avoid matrix inversion by allowing univariate parameter updates. An experimental study shows the good performance of the algorithms in terms of recovering the actual sparse solutions, parameter estimation, and clustering of heterogeneous regression data

Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models

Mixtures-of-Experts (MoE) are conditional mixture models that have shown their performance in modeling heterogeneity in data in many statistical learning approaches for prediction, including regression and classification, as well as for clustering. Their estimation in high-dimensional problems is still however challenging. We consider the problem of parameter estimation and feature selection in MoE models with different generalized linear experts models, and propose a regularized maximum likelihood estimation that efficiently encourages sparse solutions for heterogeneous data with high-dimensional predictors. The developed proximal-Newton EM algorithm includes proximal Newton-type procedures to update the model parameter by monotonically maximizing the objective function and allows to perform efficient estimation and feature selection. An experimental study shows the good performance of the algorithms in terms of recovering the actual sparse solutions, parameter estimation, and clustering of heterogeneous regression data, compared to the main state-of-the art competitors.Comment: arXiv admin note: text overlap with arXiv:1810.1216

Self-Compassion Mediates the Link Between Attachment Security and Intimate Relationship Quality for Couples Navigating Pregnancy

Millions of couples navigate the transition from pregnancy to postpartum in a given year, and this period of change and adjustment in the family is associated with elevated risk for intimate relationship dysfunction. Self-compassion has the potential to promote skills that are essential for healthy adaptation (e.g., emotion regulation, greater openness and flexibility, more awareness of the needs of oneself and one’s partner). The overarching goal of the present study was to investigate the role of self-compassion in intimate relationship quality during pregnancy. A sample of 159 couples completed semi-structured interviews and questionnaires. Parents engaging in more compassionate self-responding during pregnancy had higher quality intimate relationships as measured across multiple facets – the degree of emotional intimacy and closeness in the relationship, adaptive conflict management and resolution, high quality support in response to stress, and a high degree of respect and acceptance directed toward each other. Further, compassionate self-responding emerged as a mediator of the link between attachment security and intimate relationship quality. Specifically, mothers who were higher in attachment anxiety reported lower levels of compassionate self-responding which, in turn, undermined multiple dimensions of the intimate relationship. Further, fathers who were higher in attachment avoidance practiced less self-compassion, which had deleterious consequences for the couple. These results provide implications that can inform conceptual frameworks of intimate relationship quality and clinical implications for interventions targeting the transition into parenthood

Automorphism Groups of Graphical Models and Lifted Variational Inference

Using the theory of group action, we first introduce the concept of the automorphism group of an exponential family or a graphical model, thus formalizing the general notion of symmetry of a probabilistic model. This automorphism group provides a precise mathematical framework for lifted inference in the general exponential family. Its group action partitions the set of random variables and feature functions into equivalent classes (called orbits) having identical marginals and expectations. Then the inference problem is effectively reduced to that of computing marginals or expectations for each class, thus avoiding the need to deal with each individual variable or feature. We demonstrate the usefulness of this general framework in lifting two classes of variational approximation for MAP inference: local LP relaxation and local LP relaxation with cycle constraints; the latter yields the first lifted inference that operate on a bound tighter than local constraints. Initial experimental results demonstrate that lifted MAP inference with cycle constraints achieved the state of the art performance, obtaining much better objective function values than local approximation while remaining relatively efficient.Comment: Extended version of the paper to appear in Statistical Relational AI (StaRAI-12) workshop at UAI '1

Alliance’s research and engagement contributes to developing national action plan for transparent, responsible, and sustainable food systems transformation in Vietnam

Alliance successfully integrated the concepts of sustainable healthy diets and sustainable food systems into the Q&A guidelines for the Strategy for Sustainable Agriculture and Rural Development, Period 2021–2030 and Vision to 2050 led by IPSARD-MARD. Since 2021, Alliance researchers have engaged in the process of UNFSS dialogues and summit, and technical meetings for drafting the technical report highlighting the government’s need for a food systems’ plan and developing the National Action Plan for Transparent, Responsible, and Sustainable Food Systems Transformation (2022–2030) which was approved by the Prime Minister level (March 2023) and is critical to the Strategy’s implementation

Alliance’s research and engagement contributes to developing National Action Plan for Transparent, Responsible, and Sustainable Food Systems Transformation in Vietnam

Alliance successfully integrated the concepts of sustainable healthy diets and sustainable food systems into the Q&A guidelines for the Strategy for Sustainable Agriculture and Rural Development, Period 2021–2030 and Vision to 2050 led by IPSARD-MARD. Since 2021, Alliance researchers have engaged in the process of UNFSS dialogues and summit, and technical meetings for drafting the technical report highlighting the government’s need for a food systems’ plan and developing the National Action Plan for Transparent, Responsible, and Sustainable Food Systems Transformation (2022–2030) which was approved by the Prime Minister level (March 2023) and is critical to the Strategy’s implementation

CGIAR Initiative on Securing the food systems of Asian Mega-Deltas for climate and livelihood resilience (AMD): Nutrition Sensitive Deltaic Agri-food Systems

A presentation introducing the CGIAR Initiative on Securing the food systems of Asian Mega-Deltas for climate and livelihood resilience (AMD): Nutrition Sensitive Deltaic Agri-food Systems at the International Health Science Conference in Can Tho City, Vietnam on 24 December 2022

AN EXPERIMENT ON THE PROPOSED ONLINE ASSESSMENT AND EXAMINATION METHOD AT DALAT UNIVERSITY

Digital transformation is one of the key tasks of the education field. In this context, online teaching is becoming a potential solution in education to provide learners with a flexible learning environment that is independent of space and time. This trend has become even more urgent as the world has just experienced the global COVID-19 pandemic. Along with online teaching are methods of online assessments and examinations. The biggest challenges to online assessments and examinations are internet speed, authentication of the examinee’s identity, and test authenticity, especially for classes with large numbers of students. In this article, we propose an online oral exam process that can be applied to a large class but still ensures seriousness, fairness, and objectivity. Experiments conducted in 11 courses taught and assessed at Dalat University during the second semester of the 2020–2021 school year and the first semester of the 2021–2022 school year are discussed to illustrate the process

Nutrition landscape and climate in Vietnam: Identifying climate service entry points

This paper comprises a current state assessment of the nutrition landscape in Vietnam to identify potential climate service entry-points. Through secondary research of global databases, ministry websites and previous studies in this field we mapped a list of nutrition-specific and nutrition-sensitive interventions, stakeholders, and data sources in Vietnam. This was followed by interviews with key stakeholders, which highlighted the major challenges and opportunities for using climate data to inform programs and policies aimed at improving nutritional outcomes within the country. The results indicate there are capacity and coordination challenges amongst government departments and between government and development agencies. The key stakeholders we interviewed are cognizant of the effect of climate on food systems and nutrition. It is possible, therefore, that with an increased awareness at the leadership level necessary systemic changes can be achieved. It is recommended to prioritize the inclusion of climate indicators into the nutrition surveys in particular and policy decisions in general, followed by strengthening the data-sharing (climate data) and coordination mechanisms between departments. Finally, the upcoming National Nutrition Strategy (2021-30) offers a great opportunity to formalize the climate and nutrition linkage, while also highlighting the urgency of the matter