Variable metric proximal gradient (VM-PG) is a widely used class of convex optimization method. Lately, there has been a lot of research on the theoretical guarantees of VM-PG with different metric selections. However, most such metric selections are dependent on (an expensive) Hessian, or limited to scalar stepsizes like the Barzilai-Borwein (BB) stepsize with lots of safeguarding. Instead, in this paper we propose an adaptive metric selection strategy called the diagonal Barzilai-Borwein (BB) stepsize. The proposed diagonal selection better captures the local geometry of the problem while keeping per-step computation cost similar to the scalar BB stepsize i.e. $O(n)$. Under this metric selection for VM-PG, the theoretical convergence is analyzed. Our empirical studies illustrate the improved convergence results under the proposed diagonal BB stepsize, specifically for ill-conditioned machine learning problems for both synthetic and real-world datasets.

--Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data require estimating the class prior or label noise ahead of building a classification model. However, the estimation and classifier learning are normally conducted in a pipeline instead of being jointly optimized. In this paper, we propose to alternatively train the two steps using reinforcement learning. Our proposal adopts a policy network to adaptively make assumptions on the labels of unlabeled data, while a classifier is built upon the output of the policy network and provides rewards to learn a better policy. The dynamic and interactive training between the policy maker and the classifier can exploit the unlabeled data in a more effective manner and yield a significant improvement in terms of classification performance. Furthermore, we present two different approaches to represent the actions taken by the policy. The first approach considers continuous actions as soft labels, while the other uses discrete actions as hard assignment of labels for unlabeled examples. We validate the effectiveness of the proposed method on two public benchmark datasets as well as one e-commerce dataset. The results show that the proposed method is able to consistently outperform state-of-the-art methods in various settings. PU learning refers to the problem of learning from a dataset where only a subset of examples are positively labeled and the rest are not annotated at all. It is a critical task due to its prevalence in various real-world applications [1], [2], [3]. In many common situations only positive data are available, for instance, an e-commerce website may only record users who have clicked on advertisements or purchased items. Meanwhile, it is not possible to simply assume that unlabeled instances are negative.

Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the delayed outcome (e.g., if customers read a third of the book in 24 hours) can help minimize regret, even though the proxy is not available when making a prediction. Motivated by our regret analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if the proxy is informative of the outcome in hindsight, and Residual Factored Forecaster (RFF) that is robust to a non-informative proxy. Experiments on two real-world datasets for predicting human behavior show that RFF outperforms both FF and a direct forecaster that does not make use of the proxy. Our results suggest that exploiting proxies by factorization is a promising way to mitigate the impact of long delays in human-behavior prediction tasks.

Woodie Flowers SM '68, MEng '71, PhD '73, the Pappalardo Professor Emeritus of Mechanical Engineering, passed away on Oct. 11 at the age of 75. Flowers' passion for design and his infectious kindness have impacted countless engineering students across the world. Flowers was instrumental in shaping MIT's hands-on approach to engineering design education, first developing teaching methods and learning opportunities that culminated in a design competition for class 2.70, now called 2.007 (Design and Manufacturing I). This annual MIT event, which has now been held for nearly five decades, has impacted generations of students and has been emulated at universities around the world. Flowers expanded this concept to high school and elementary school students, working to help found the world-wide FIRST Robotics Competition, which has introduced millions of children to science and engineering.

Artificial intelligence (AI) is rapidly transforming and improving the ways that industries like healthcare, banking, energy, and retail operate. However, there is one industry in particular that offers incredible potential for the application of AI technologies: education. The opportunities -- and challenges -- that the introduction of artificial intelligence could bring to higher education are significant. Today's colleges and universities face a wide range of challenges, including disengaged students, high dropout rates, and the ineffectiveness of a traditional "one-size-fits-all" approach to education. But when big data analytics and artificial intelligence are used correctly, personalized learning experiences can be created, which may in turn help to resolve some of these challenges.

In this tutorial, you will learn the three primary reasons your validation loss may be lower than your training loss when training your own custom deep neural networks. I first became interested in studying machine learning and neural networks in late high school. Back then there weren't many accessible machine learning libraries -- and there certainly was no scikit-learn. Every school day at 2:35 PM I would leave high school, hop on the bus home, and within 15 minutes I would be in front of my laptop, studying machine learning, and attempting to implement various algorithms by hand. I rarely stopped for a break, more than occasionally skipping dinner just so I could keep working and studying late into the night.

What a time this is to be working in the machine learning field! The last few years have been a dream run for anyone associated with machine learning as there have been a slew of developments and breakthroughs at an unprecedented pace. There's just one thing to keep in mind here – these breakthroughs did not happen overnight. It took years and in some cases, decades, of hard work and persistence. We are used to working with established machine learning algorithms like neural networks and random forest (and so on). We tend to lose sight of the effort it took to make these algorithms mainstream. To actually create them from scratch. The people who lay the groundwork for us – those are the true heroes of machine learning.

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Canada is continuing as one of the favorite abroad destinations for foreign students to study and newcomers to relocate from their home nation. Canada's openness to migration and favorable policies to foreign students makes it more favored among overseas students and aspirants. There are more famous universities & colleges in Canada which are immensely presenting world-class education to students and have been creating a higher number of graduates being successful in various fields! It is more clear that pursuing their education in Canada could make global students more successful. There are greater job openings after studying in Canada.

Canada is continuing as one of the favorite abroad destinations for foreign students to study and newcomers to relocate from their home nation. Canada's openness to migration and favorable policies to foreign students makes it more favored among overseas students and aspirants. There are more famous universities & colleges in Canada which are immensely presenting world-class education to students and have been creating a higher number of graduates being successful in various fields! It is more clear that pursuing their education in Canada could make global students more successful. There are greater job openings after studying in Canada.