When it comes to research in new-age technologies, Microsoft has been striving hard to stay ahead of its competitors. From recommendations to gaming, the tech giant has been using popular techniques like reinforcement learning to create efficient products for customers that match their interests.
Countless companies use online recommendation engines to show customers products and experiences that match their interests. And yet, traditional machine learning models that predict what people might prefer are often based on data from past experience.
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.
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.
New research focuses on the challenges of machine learning biases, and provides a provably and empirically sound method for turning any common classifier into a 'fair' classifier according to any of a wide range of fairness definitions.