During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it's not accessible for these DR methods to achieve satisfactory performance when facing the features from multiple views. In multi-view learning problems, one instance can be represented by multiple heterogeneous features, which are highly related but sometimes look different from each other. In addition, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view learning methods. Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging. To implement the above target, this paper proposes a novel multi-view learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering the scalability and robustness of the framework. It aims to straightforwardly extend those existing DR methods into multi-view learning domain by preserving the similarity between different views to capture the low-dimensional embedding. Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other and then an iterative alternating strategy is developed to obtain the optimal solution. The proposed method is evaluated on 5 benchmark datasets and comprehensive experiments show that our proposed multi-view framework can yield comparable and promising performance with previous approaches proposed in recent literatures.

In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in $T$ rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher than the announced reserve price in that round will report their values to the seller as their bids. The bidder with the highest bid larger than the reserved price would win the item and she will pay to the seller the price equal to the second-highest bid or the reserve price, whichever is larger. The seller wants to maximize her total revenue during the time horizon $T$ while learning the distribution of private values over time. The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the \emph{non-stationary regret} to measure the performance of our algorithm. To our knowledge, this paper is the first to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound $\tilde{\mathcal{O}}(\min\{\sqrt{\mathcal S T}, \bar{\mathcal{V}}^{\frac{1}{3}}T^{\frac{2}{3}}\})$, where $\mathcal S$ is the number of switches in the distribution, and $\bar{\mathcal{V}}$ is the sum of total variation, and $\mathcal S$ and $\bar{\mathcal{V}}$ are not needed to be known by the algorithm. We also prove regret lower bounds $\Omega(\sqrt{\mathcal S T})$ in the switching case and $\Omega(\bar{\mathcal{V}}^{\frac{1}{3}}T^{\frac{2}{3}})$ in the dynamic case, showing that our algorithm has nearly optimal \emph{non-stationary regret}.

In this session we will discuss the definition of computational thinking and how it exists in everyday life.

The report states that 28.6% of Louisville's jobs are at "high risk" of being automated. A central purpose in this partnership is to make sure Louisville is well-equipped for the technological revolution, according to Grace Simrall, chief of Civic Innovation and Technology for Louisville Metro Government. "Experts know that automation and AI are coming," Simrall previously told The Courier Journal. "They know that they will probably destroy tasks and potentially even jobs faster than we can replace them if we don't do something about it." Fischer also announced Wednesday afternoon that Ben Reno-Weber, a social entrepreneur and project director of the independent, non-partisan civic data initiative The Greater Louisville Project, will serve as director of the Future of Work Initiative.

Microbiology and MBA grad JK started to learn about big data and machine learning in his job, but wanted to learn more about data science in a structured environment. He enrolled in Springboard's Machine Learning Career Track to learn about ML and AI online. JK tells us how he balanced his full-time job with the Springboard bootcamp (hint: he didn't sleep much), and how networking at conferences helped him land his new job as a Data Engineer at KPMG! What is your educational and career background? I didn't come from a computer science (CS) background. My undergrad was in microbiology, immunology and molecular genetics. I then completed an MBA with a concentration in Accounting and Finance, working at the Australian Chamber of Commerce in Korea. And that's where I got a taste of some CS database work.

This is an episode from Josh Reppun's "What School Could Be in Hawai'i," a podcast on the people, technology and methodologies pushing the mantle of education in the 50th state. Susannah Johnson is the founder of Individualized Realized, an education consultancy aimed at meeting educators where they are – as she did in the classroom with students for thirteen years – on the path to student-centered, authentic, globally minded, and liberated learning. In the move towards student-centered learning technology is essential for individualized learning. Over ten years developing a fully individualized program, the use of technology not only opens up learning to be multidimensional, but also for the asynchronous management of dozens of curricula. When students own their own learning, technology moves beyond learning tool to become a partner for that learning.

It's the classic trope of buddy cop movies: you introduce two characters with little in common aside from the job that they do. Maybe one's a maverick and the other is a stickler for doing things by the book. At first they don't get along. Perhaps one is new to the precinct and the other fears that they're being phased out as a result. But, wouldn't you know it, they turn out to be a great team.

It's the classic trope of buddy cop movies: you introduce two characters with little in common aside from the job that they do. Maybe one's a maverick and the other is a stickler for doing things by the book. At first they don't get along. Perhaps one is new to the precinct and the other fears that they're being phased out as a result. But, wouldn't you know it, they turn out to be a great team.

This is the next post of a series of Trends & Perspectives blog posts. The Track Chairs will reflect on each of this years' presentation tracks, analyze and discuss some of the trends that you can expect to hear about at OLC Accelerate this year, and also get the perspectives of the Best-in-Track winners. This blog post features the presenters for the Best In Track selection for the Teaching and Learning Effectiveness Track at the upcoming OLC Accelerate Conference. Their express workshop, Show it Off! Showcase Your Artificial Intelligence, will be held on Wednesday, November 20th from 1:15-2:00pm in Southern Hemisphere 1.

Companies using data science are rapidly eating the market shares of their competitors. This shift can be observed across many industries. The root cause is that advanced analytics offers pronounced decision-making leverage. This effect is stronger in a digitized economy where a correct decision can quickly propagate through networks of businesses and consumers. Resources are rapidly allocated to the best companies.