Last week at ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and Dipendra Misra had a paper on a new reinforcement learning (RL) algorithm that solves three key problems in RL: (i) global exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. Their ICML poster is here.
At ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and Dipendra Misra published a paper presenting a new reinforcement learning (RL) algorithm called HOMER that addresses three main problems in real-world RL problem: (i) exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. ArXiv version of the paper can be found here, and the ICML version would be released soon.
MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and into the real world. One of those researchers is Dr. Akshay Krishnamurthy and today, he explains how his work on feedback-driven data collection and provably efficient reinforcement learning algorithms is helping to move the RL…
As the recently released GPT-3 and several recent studies demonstrate, racial bias, as well as bias based on gender, occupation, and religion, can be found in popular NLP language models. But a team of AI researchers wants the NLP bias research community to more closely examine and explore relationships between language, power, and social hierarchies like racism in their work. That’s one of three major recommendations for NLP bias researchers a recent study makes.
Dr. Siddhartha Sen is a Principal Researcher in MSR’s New York City lab, and his research interests are, if not impossible, at least impossible sounding: optimal decision making, universal data structures, and verifiably safe AI. Today, he tells us how he’s using reinforcement learning and HAIbrid algorithms to tap the best of both human and machine intelligence and develop AI that’s minimally disruptive, synergistic with human solutions, and safe.
Microsoft and University of Washington researchers have teamed up on a new app, CovidSafe, that promises to alert people automatically if they’ve been in close proximity to someone infected by COVID-19, seeking to strike a balance between the sometimes competing interests of personal privacy and public health.
If you haven't ever been in a casino, you may have found yourself asking one very pertinent question: On which slot machine am I going to hit the jackpot? Standing in front of a bank of identical-looking machines, you have only instinct to go on. It isn’t until you start putting your money into these one-armed bandits, as they’re also known, that you get a sense of which are hot and which are not, and…
Susan Dumais, Technical Fellow and Director of the Microsoft Research Labs in New England, New York City and Montréal, and adjunct professor at the University of Washington, received the SIGCHI Lifetime Research Award. This is the highest honor awarded in the Human Computer Interaction community and is a tribute to a lifetime of deep and broad contributions to the intellectual core of the field.
A common theme that runs through her work is the importance of understanding and improving information systems from an interdisciplinary and user-centered perspective. She is a co-inventor of Latent Semantic Analysis, a well-known word embedding technique, which was designed to mitigate the disagreement between the words that authors use writing and those that searchers use to find information.