Abbe Herzig, AMS Director of Education In the midst of the upheaval due to the Coronavirus, students and faculty are transitioning to new virtual classrooms. Many of us haven’t chosen to learn or teach, but here we are, making the … Continue reading →

By: Matt Stamps, Yale-NUS College When Yale-NUS College reviewed the curriculum for its Mathematical, Computational, and Statistical (MCS) Sciences major in the autumn of 2018, I spent several weeks reading about mathematics programs at similar institutions.  A common learning objective … Continue reading →

This year’s Institute for Teaching Learning program is scheduled for Aug. 23-26 in Atlanta. Now in its 14th year, with over 700 alumni, the program combines presentations, discussions, small group activities and peer-to-peer learnings to give participants new teaching skills. The onsite program is followed by a six-month distance learning experience that include online activities and interactive webinars.

Registration opens April 22 for the ADA FDC Annual Meeting, which will offer a variety of learning and social opportunities for dentists and their teams to enjoy.

OBJECTIVE

We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.

As maritime arrivals climbed in 2015, EU policymakers struggled to mount a coordinated response. A range of ad hoc crisis-response tools emerged, but many officials worry that if another migration emergency were to hit Europe, the European Union may still be unprepared. This report traces the evolution of the EU response to the 2015–16 crisis and lays out recommendations to lock in progress and shore up weaknesses.

In an era of stepped-up immigration enforcement, speakers at this event present their research on the impact of enforcement policies on children from immigrant families and U.S. public schools. 

OBJECTIVE

To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.

Gayle Lorenzi
Sep 1, 2010; 28:157-162
Bridges to Excellence

Three classical pieces that boost memory retention.

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→ Explore PsyBlog's ebooks, all written by Dr Jeremy Dean:

• Accept Yourself: How to feel a profound sense of warmth and self-compassion
• The Anxiety Plan: 42 Strategies For Worry, Phobias, OCD and Panic
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Too often, teachers are asked to use SEL practices without enough training and ongoing support, tanking the effectiveness.

School districts are competing against each other for purchases of digital devices as remote learning expands to schools across the country.

The Old Dominion is embedding future-ready knowledge and skills into its education system, giving students a personal arsenal of content mastery and core deeper learning skills.

Coronavirus is spreading rapidly in pre- and post-trial correctional facilities across the United States, and the challenges of social distancing for students in regular districts are all massively compounded for students behind bars.

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

A statewide survey of educators in Tennessee provides critical insights into connections that exist between standards, curriculum, professional development, and ultimately student success.

In this fifth installment on the growth in dual-language learning, the executive director of the BUENO Center for Multicultural Education at the University of Colorado, Boulder., says districts should focus on the what students and their families need, not what educators want.

The Chan Zuckerberg Initiative, states like Vermont and Rhode Island, and companies such as AltSchool all generated headlines about personalized learning in 2017.

The Chan-Zuckerberg Initiative and other funders are supporting Rhode Island's efforts to define and research personalized learning in traditional public schools.

The Rhode Island Department of Education and the nonprofit Learning Accelerator are teaming to develop a strategic plan and a communications strategy aimed at expanding blended learning.

Lisa Duty of The Learning Accelerator, a Rhode Island Department of Education (RIDE) and Highlander Institute funding partner, outlines the Rhode Islands's commitment to a blended learning future. She describes how the state is developing its new five-year strategic plan that's engaging RIDE's Ambas

In this fourth installment on the growth in dual-language learning, the director of dual-language education in Portland, Ore., says schools must have a clear reason for why they are offering dual-language instruction.

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

Stephanie Hirsh recently visited several schools in Delaware to see first-hand the impact of the state's redesigned professional learning system.

In this seventh installment on the growth in dual-language learning, two experts from Delaware explore how state education leaders can build capacity to support both students and educators.

The Knowledge Sharing for the Development of Learning Resources tutorial provides a professional step forward, a learning experience that leads to recognition that your leadership is well founded as well as ensuring that participants in the development of learning resources recognize they are contributing to an exceptional achievement.

We asked parents, students, and educators to share what their home learning environments look like as nearly all schools are shut down for extended periods because of the coronavirus pandemic.                          

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

When Idaho education leaders pitched social-emotional learning training for teachers, some state lawmakers compared the plan to dystopian behavior control. Some walked out of the meeting.

The director of teaching and learning for a Minnesota district talks about putting e-learning days into action under difficult circumstances.

In the Green Mountain State, education leaders discuss their focus on the whole child.

[London] (at the Historic Gallery, 87 Pall Mall) : Pub.d by Mr Stone.

Rockville, Maryland : National Institute on Drug Abuse, 1988.

François Bachoc, Baptiste Broto, Fabrice Gamboa, Jean-Michel Loubes.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 503--546.

Abstract:
In the framework of the supervised learning of a real function defined on an abstract space $mathcal{X}$, Gaussian processes are widely used. The Euclidean case for $mathcal{X}$ is well known and has been widely studied. In this paper, we explore the less classical case where $mathcal{X}$ is the non commutative finite group of permutations (namely the so-called symmetric group $S_{N}$). We provide an application to Gaussian process based optimization of Latin Hypercube Designs. We also extend our results to the case of partial rankings.

This paper studies the nonparametric modal regression problem systematically from a statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially modal regression. We show that the nonparametric modal regression problem can be approached via the classical empirical risk minimization. Some efforts are then made to develop a framework for analyzing and implementing modal regression. For instance, the modal regression function is described, the modal regression risk is defined explicitly and its Bayes rule is characterized; for the sake of computational tractability, the surrogate modal regression risk, which is termed as the generalization risk in our study, is introduced. On the theoretical side, the excess modal regression risk, the excess generalization risk, the function estimation error, and the relations among the above three quantities are studied rigorously. It turns out that under mild conditions, function estimation consistency and convergence may be pursued in modal regression as in vanilla regression protocols such as mean regression, median regression, and quantile regression. On the practical side, the implementation issues of modal regression including the computational algorithm and the selection of the tuning parameters are discussed. Numerical validations on modal regression are also conducted to verify our findings.

One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data. Sometimes referred to as manifold learning, this problem is closely related to the problem of localization, which aims at embedding a weighted graph into a low-dimensional Euclidean space. Several methods have been proposed for localization, and also manifold learning. Nonetheless, the robustness property of most of them is little understood. In this paper, we obtain perturbation bounds for classical scaling and trilateration, which are then applied to derive performance bounds for Isomap, Landmark Isomap, and Maximum Variance Unfolding. A new perturbation bound for procrustes analysis plays a key role.

Graph learning from data is a canonical problem that has received substantial attention in the literature. Learning a structured graph is essential for interpretability and identification of the relationships among data. In general, learning a graph with a specific structure is an NP-hard combinatorial problem and thus designing a general tractable algorithm is challenging. Some useful structured graphs include connected, sparse, multi-component, bipartite, and regular graphs. In this paper, we introduce a unified framework for structured graph learning that combines Gaussian graphical model and spectral graph theory. We propose to convert combinatorial structural constraints into spectral constraints on graph matrices and develop an optimization framework based on block majorization-minimization to solve structured graph learning problem. The proposed algorithms are provably convergent and practically amenable for a number of graph based applications such as data clustering. Extensive numerical experiments with both synthetic and real data sets illustrate the effectiveness of the proposed algorithms. An open source R package containing the code for all the experiments is available at https://CRAN.R-project.org/package=spectralGraphTopology.

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.

This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory.

Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.

We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, the inferred causal relationships among the observed variables are often wrong. Under faithfulness assumption, we propose a method to check whether there exists a causal path between any two observed variables. From this information, we can obtain the causal order among the observed variables. The next question is whether the causal effects can be uniquely identified as well. We show that causal effects among observed variables cannot be identified uniquely under mere assumptions of faithfulness and non-Gaussianity of exogenous noises. However, we are able to propose an efficient method that identifies the set of all possible causal effects that are compatible with the observational data. We present additional structural conditions on the causal graph under which causal effects among observed variables can be determined uniquely. Furthermore, we provide necessary and sufficient graphical conditions for unique identification of the number of variables in the system. Experiments on synthetic data and real-world data show the effectiveness of our proposed algorithm for learning causal models.

We present a theoretical analysis framework for relational ensemble models. We show that ensembles of collective classifiers can improve predictions for graph data by reducing errors due to variance in both learning and inference. In addition, we propose a relational ensemble framework that combines a relational ensemble learning approach with a relational ensemble inference approach for collective classification. The proposed ensemble techniques are applicable for both single and multiple graph settings. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed framework. Finally, our experimental results support the theoretical analysis and confirm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both, are the best performers.

In this paper we introduce a statistical model, called additively faithful directed acyclic graph (AFDAG), for causal learning from observational data. Our approach is based on additive conditional independence (ACI), a recently proposed three-way statistical relation that shares many similarities with conditional independence but without resorting to multi-dimensional kernels. This distinct feature strikes a balance between a parametric model and a fully nonparametric model, which makes the proposed model attractive for handling large networks. We develop an estimator for AFDAG based on a linear operator that characterizes ACI, and establish the consistency and convergence rates of this estimator, as well as the uniform consistency of the estimated DAG. Moreover, we introduce a modified PC-algorithm to implement the estimating procedure efficiently, so that its complexity is determined by the level of sparseness rather than the dimension of the network. Through simulation studies we show that our method outperforms existing methods when commonly assumed conditions such as Gaussian or Gaussian copula distributions do not hold. Finally, the usefulness of AFDAG formulation is demonstrated through an application to a proteomics data set.

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad class of actors and critics, including Gaussian, exponential families, and policies with bounded support. For Gaussian policies, we introduce an exploration method that uses covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. For discrete action spaces, we derive a variant of EPG based on softmax policies. We also establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we provide an extensive experimental evaluation of EPG and show that it outperforms existing approaches on multiple challenging control domains.