We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of $K$ classes and lie in the $d$-dimensional Euclidean space. Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linearly separable by a margin $\gamma$. In this work, we take a first step towards this problem. We consider two notions of linear separability, \emph{strong} and \emph{weak}. 1. Under the strong linear separability condition, we design an efficient algorithm that achieves a near-optimal mistake bound of $O\left( K/\gamma^2 \right)$. 2. Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$. Our algorithm is based on kernel Perceptron, which is inspired by the work of \citet{Klivans-Servedio-2008} on improperly learning intersection of halfspaces.

We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative. Our algorithm only observes the true label of an individual if they are given a positive classification. This setting captures many classification problems for which fairness is a concern: for example, in criminal recidivism prediction, recidivism is only observed if the inmate is released; in lending applications, loan repayment is only observed if the loan is granted. We require that our algorithms satisfy common statistical fairness constraints (such as equalizing false positive or negative rates --- introduced as "equal opportunity" in Hardt et al. (2016)) at every round, with respect to the underlying distribution. We give upper and lower bounds characterizing the cost of this constraint in terms of the regret rate (and show that it is mild), and give an oracle efficient algorithm that achieves the upper bound.

The kernel null-space technique and its regression-based formulation (called one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an effective and computationally attractive one-class classification framework. Despite its outstanding performance, the applicability of kernel null-space method is limited due to its susceptibility to possible training data corruptions and inability to rank training observations according to their conformity with the model. This work addresses these shortcomings by studying the effect of regularising the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation (OC-KSR). In this respect, first, the effect of a Tikhonov regularisation in the Hilbert space is analysed where the one-class learning problem in presence of contaminations in the training set is posed as a sensitivity analysis problem. Next, driven by the success of the sparse representation methodology, the effect of a sparsity regularisation on the solution is studied. For both alternative regularisation schemes, iterative algorithms are proposed which recursively update label confidences and rank training observations based on their fit with the model. Through extensive experiments conducted on different data sets, the proposed methodology is found to enhance robustness against contamination in the training set as compared with the baseline kernel null-space technique as well as other existing approaches in a one-class classification paradigm while providing the functionality to rank training samples effectively.

The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning. This process, referred to as distillation, has been used to great success, for example, by enhancing the optimisation of agents, leading to stronger performance faster, on harder domains [26, 32, 5, 8]. Despite the widespread use and conceptual simplicity of distillation, many different formulations are used in practice, and the subtle variations between them can often drastically change the performance and the resulting objective that is being optimised. In this work, we rigorously explore the entire landscape of policy distillation, comparing the motivations and strengths of each variant through theoretical and empirical analysis. Our results point to three distillation techniques, that are preferred depending on specifics of the task. Specifically a newly proposed expected entropy regularised distillation allows for quicker learning in a wide range of situations, while still guaranteeing convergence.

"The financial markets are ahead of others in this," said John Micklethwait, the editor in chief of Bloomberg. In addition to covering company earnings for Bloomberg, robot reporters have been prolific producers of articles on minor league baseball for The Associated Press, high school football for The Washington Post and earthquakes for The Los Angeles Times. MANCHESTER, N.H. (AP) -- Jonathan Davis hit for the cycle, as the New Hampshire Fisher Cats topped the Portland Sea Dogs 10-3 on Tuesday. Last week, The Guardian's Australia edition published its first machine-assisted article, an account of annual political donations to the country's political parties. And Forbes recently announced that it was testing a tool called Birdie to provide reporters with rough drafts and story templates.

Presentation made by Andreas Schleicher, Director for the OECD Directorate of Education and Skills, at the Education World Forum, 21st January 2019, London Did you ever wonder whether education has a role to play in preparing our societies for an age of artificial intelligence? Or what the impact of climate change might be on our schools, families and communities? While the trends are robust, the questions raised in this book are suggestive, and aim to inform strategic thinking and stimulate reflection on the challenges facing education – and on how and whether education can influence these trends. This book covers a rich array of topics related to globalisation, democracy, security, ageing and modern cultures. The content for this 2019 edition has been updated and also expanded with a wide range of new indicators.

Displaced workers transition to new jobs, some of which are created by automation. The government helps to facilitate this transition via investments in training and education. Increased productivity raises incomes, lowers work hours (average work time in the U.S. has fallen more than 50% since the early 1900s5), and lowers prices, creating more demand for goods and services, leading to more jobs and broader economic growth. How well do we expect this pattern to hold with AI-enabled automation in the near future, and will they replace jobs faster than they create them?

Machine learning is an incredibly broad and diverse field, with a non-stop increase on research, along a multitude of applications. Thus writing a list enlisting the best machine learning researchers on the field proves challenging for a number of reasons. Please mind that this list encompasses researchers who are currently working on the field. Also, please mind that this list is by no means ranked. Everyone listed below has done extraordinary work to advance humanity's state of AI further.

This blog post provides highlights and a full written transcript from the panel, "Data Science Versus Engineering: Does It Really Have To Be This Way?" with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points. Recently, I had the opportunity to moderate the panel, "Data Science Versus Engineering: Does It Really Have To Be This Way?" with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. As Domino's Head of Content, it is my responsibility to ensure that our content provides a high degree of value, density, and analytical rigor that sparks respectful candid public discourse from multiple perspectives. Discourse that directly addresses challenges, including unsolved problems with high stakes. Discourse that is also anchored in the intention of helping accelerate data ...

Follow the Retail category and get a newsletter with content from AI companies developing technologies to improve the retail experience. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library.