Online Learning with an Almost Perfect Expert

We study the multiclass online learning problem where a forecaster makes a sequence of predictions using the advice of $n$ experts. Our main contribution is to analyze the regime where the best expert makes at most $b$ mistakes and to show that when $b = o(\log_4{n})$, the expected number of mistakes made by the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe an adversary strategy showing that this bound is tight and that the worst case is attained for binary prediction

Estimating Uncertainty Online Against an Adversary

Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their modeling assumptions are not met, e.g. when the data distribution differs from the one seen at training time. Here, we propose techniques that assess a classification algorithm's uncertainty via calibrated probabilities (i.e. probabilities that match empirical outcome frequencies in the long run) and which are guaranteed to be reliable (i.e. accurate and calibrated) on out-of-distribution input, including input generated by an adversary. This represents an extension of classical online learning that handles uncertainty in addition to guaranteeing accuracy under adversarial assumptions. We establish formal guarantees for our methods, and we validate them on two real-world problems: question answering and medical diagnosis from genomic data

Vision of a Visipedia

The web is not perfect: while text is easily searched and organized, pictures (the vast majority of the bits that one can find online) are not. In order to see how one could improve the web and make pictures first-class citizens of the web, I explore the idea of Visipedia, a visual interface for Wikipedia that is able to answer visual queries and enables experts to contribute and organize visual knowledge. Five distinct groups of humans would interact through Visipedia: users, experts, editors, visual workers, and machine vision scientists. The latter would gradually build automata able to interpret images. I explore some of the technical challenges involved in making Visipedia happen. I argue that Visipedia will likely grow organically, combining state-of-the-art machine vision with human labor

On the organisation of program verification competitions

In this paper, we discuss the challenges that have to be addressed when organising program verification competitions. Our focus is on competitions for verification systems where the participants both formalise an informally stated requirement and (typically) provide some guidance for the tool to show it. The paper draws its insights from our experiences with organising a program verification competition at FoVeOOS 2011. We discuss in particular the following aspects: challenge selection, on-site versus online organisation, team composition and judging. We conclude with a list of recommendations for future competition organisers

Self-regulated learning and knowledge sharing in the workplace

This study explores how experts in a global multinational company self-regulate their learning. It investigates experts‟ perceptions of the impact of knowledge sharing on their learning and work. Findings indicate that self-regulated learning (SRL) is a highly social process that is structured by and deeply integrated with work tasks. Experts tend to draw heavily upon their personal networks of trusted colleagues in the process of diagnosing and attaining their learning goals. In contradiction to existing models, SRL in the workplace does not appear to be a clearly delineated, linear process comprised of discrete stages. Further research is needed to understand tacit practices of SRL in the workplace