This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i.e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i.e., robustness). We unify the algorithmic design of both integral and fractional conversion problems, which are also known as the 1-max-search and one-way trading problems, into a class of online threshold-based algorithms (OTA). By incorporating predictions into design of OTA, we achieve the Pareto-optimal trade-off of consistency and robustness, i.e., no online algorithm can achieve a better consistency guarantee given for a robustness guarantee. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion.

Why Are Car Software Updates Still So Bad? Over-the-air upgrades can not only transform your ride, they can help carmakers slash costs. Despite years of effort and the outlay of billions of dollars, none of the world's automakers have yet to match Tesla's prowess in delivering over-the-air (OTA) software updates. Just like with your phone and laptop, these operating system refreshes allow owners to upgrade their cars remotely. Tesla introduced OTAs in 2012, but now Elon Musk's company pumps out these updates like no other automaker. "Tesla once issued 42 updates within six months," Jean-Marie Lapeyre, Capgemini's CTO for automotive, tells WIRED. But for many other automakers, says Lapeyre, OTAs ship "maybe once a year."

By incorporating predictions into design of OT A, we achieve the Pareto-optimal trade-off of consistency and robustness, i.e., no online algorithm can achieve a better consistency guarantee

This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i.e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i.e., robustness). We unify the algorithmic design of both integral and fractional conversion problems, which are also known as the 1-max-search and one-way trading problems, into a class of online threshold-based algorithms (OTA). By incorporating predictions into design of OTA, we achieve the Pareto-optimal trade-off of consistency and robustness, i.e., no online algorithm can achieve a better consistency guarantee given for a robustness guarantee. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion.

Federated Learning (FL) is a decentralized learning paradigm, enabling parties to collaboratively train models while keeping their data confidential. Within autonomous driving, it brings the potential of reducing data storage costs, reducing bandwidth requirements, and to accelerate the learning. FL is, however, susceptible to poisoning attacks. In this paper, we introduce two novel poisoning attacks on FL tailored to regression tasks within autonomous driving: FLStealth and Off-Track Attack (OTA). FLStealth, an untargeted attack, aims at providing model updates that deteriorate the global model performance while appearing benign. OTA, on the other hand, is a targeted attack with the objective to change the global model's behavior when exposed to a certain trigger. We demonstrate the effectiveness of our attacks by conducting comprehensive experiments pertaining to the task of vehicle trajectory prediction. In particular, we show that, among five different untargeted attacks, FLStealth is the most successful at bypassing the considered defenses employed by the server. For OTA, we demonstrate the inability of common defense strategies to mitigate the attack, highlighting the critical need for new defensive mechanisms against targeted attacks within FL for autonomous driving.

This paper leverages machine learned predictions to design online algorithms for the k-max and k-min search problems. Our algorithms can achieve performances competitive with the offline algorithm in hindsight when the predictions are accurate (i.e., consistency) and also provide worst-case guarantees when the predictions are arbitrarily wrong (i.e., robustness). Further, we show that our algorithms have attained the Pareto-optimal trade-off between consistency and robustness, where no other algorithms for k-max or k-min search can improve on the consistency for a given robustness. To demonstrate the performance of our algorithms, we evaluate them in experiments of buying and selling Bitcoin.

Imagine this – You find out that you'll need to travel to an international location for a business meeting in the next 2 months. You enter the dates into your phone's calendar and go on with your day. While you go through your daily schedules and meetings, your virtual assistant goes ahead and books flight tickets according to the travel dates you entered, while also taking into consideration your preferred airline and time to fly. Furthermore, it also builds a tentative itinerary based on the experiences that you are likely to enjoy. Although surreal, this scenario is pretty close to the reality we're living in today.

Congress is finally turning its attention to Silicon Valley. And it's not hard to understand why: Technology impinges upon every part of our civic sphere. We've got police using AI to determine which neighborhoods to patrol, Facebook filtering the news, and automation eroding the job market. Smart policy could help society adapt. But to tackle these issues, congressfolk will first have to understand them.

A quartet of tech experts arrived at a little-noticed hearing at the U.S. Capitol in May with a message: Quantum computing is a bleeding-edge technology with the potential to speed up drug research, financial transactions and more. To Rep. Adam Kinzinger, though, their highly technical testimony might as well have been delivered in a foreign language. "I can understand about 50 percent of the things you say," the Illinois Republican confessed. Kinzinger's quip drew chuckles from his peers on the House Energy and Commerce Committee, but it also illustrated an unavoidable challenge on Capitol Hill. Increasingly, members of Congress are confronting a wide array of complex policy debates posed by inventions like artificial intelligence and problems like the rise of Russian propaganda online.