An underlying structure in several sampling-based methods for continuous multi-robot motion planning (MRMP) is the tensor roadmap (TR), which emerges from combining multiple PRM graphs constructed for the individual robots via a tensor product. We study the conditions under which the TR encodes a near-optimal solution for MRMP -- satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and CBS when applied to the continuous domain. We develop the first finite-sample analysis of this kind, which specifies the number of samples, their deterministic distribution, and magnitude of the connection radii that should be used by each individual PRM graph, to guarantee near-optimality using the TR. This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity. Our new finite sample-size analysis supports guaranteed high-quality solutions in practice within finite time. To achieve our new result, we first develop a sampling scheme, which we call the staggered grid, for finite-sample motion planning for individual robots, which requires significantly fewer samples than previous work. We then extend it to the much more involved MRMP setting which requires to account for interactions among multiple robots. Finally, we report on a few experiments that serve as a verification of our theoretical findings and raise interesting questions for further investigation.

Eran Halperin, SVP of AI and Machine Learning in Optum Labs and a professor in the departments of Computer Science, Computational Medicine, Anaesthesiology, and Human Genetics at UCLA, gave a keynote talk on using whole-genome methylation patterns as a biomarker for electronic health record (EHR) imputation. Dr Halperin showed that methylation provides a better imputation performance when compared to genetic or EHR data. This approach uses a new tensor deconvolution of bulk DNA methylation to obtain cell-type-specific methylation that is in turn used for imputation. Irene Chen from the Massachusetts Institute of Technology (MIT) gave a keynote describing how to leverage machine learning towards equitable healthcare. Dr Chen demonstrated how to adapt disease progression modeling to account for differences in access to care.

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications.

The financial sector has been among the fastest adaptors of AI algorithms, which are well suited to the industry's complex and fast-moving environment. At last week's Re•Work AI in Finance Conference in New York, researchers and engineers from banks and academia alike shared their thoughts on current AI research and applications in the finance world. IBM has built a blockchain-based infrastructure for federated AI, enabling institutions to leverage transaction data across branches to improve decision making. Alan King is an IBM AI and Blockchain Solutions engineer. In his presentation King spoke of the advantages of using federated AI on transaction data.

Josh Fischel, the founder of last weekend's inaugural Music Tastes Good Festival in Long Beach, died Thursday afternoon of liver disease, according to festival organizers. The news came as a shock to the Long Beach and Southern California music communities, who just days ago saw Fischel overseeing the culmination of a life's work in local music. Though family and festival organizers knew he had been sick, no one knew how rapidly his disease would progress after the festival. Music Tastes Good was a three-day event in downtown Long Beach headlined by the likes of the Specials, Warpaint and the Squeeze, among many others. "He couldn't go more than a few feet in his golf cart without somebody stopping him to say'Hey, Josh!' " said Jon Halperin, the talent buyer and co-promoter of Music Tastes Good.

Schools are on the front lines in coping with attention deficit hyperactivity disorder (ADHD). More school-age kids are getting diagnosed with it each year (more than one in 10, according to the most recent National Survey of Children's Health) and the classroom is where kids often have their biggest problems with impulse control and an inability to sit still and focus. Some kids take medicine to control these symptoms, but many do not. And so principals and teachers are tremendously interested in non-medical therapies they can use at school to help children. Fortunately, it's an exciting time in ADHD research, thanks to developments in neuroscience, and psychologists hope they will find new tools for schools.