Reinforcement Learning
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AI Frontiers: Rethinking intelligence with Ashley Llorens and Ida Momennejad
| Ashley Llorens and Ida Momennejad
Principal Researcher Ida Momennejad brings her expertise in cognitive neuroscience and computer science to this in-depth conversation about general intelligence and what the evolution of the brain across species can teach us about building AI.
Abstracts: January 25, 2024
| Gretchen Huizinga, Jordan Ash, and Dipendra Misra
On “Abstracts,” Jordan Ash & Dipendra Misra discuss the parameter reduction method LASER. Tune in to learn how selective removal of stored data alone can boost LLM performance, then sign up for Microsoft Research Forum for more on LASER &…
Research Focus: Week of December 4, 2023
Research Focus: Using LLMs in a Rust-based formal verification framework; Rethinking network measurements with user feedback; 3D telemedicine using HoloportationTM communication technology could enhance overseas surgical visits.
Awards | International World Wide Web Conference
John Langford, Rob Schapire and co-authors receive the 2023 Seoul Test of Time Award
The International World Wide Web Conference Committee (IW3C2) announced today that the 2023 Seoul Test of Time Award will be presented to the authors of the paper “A Contextual-Bandit Approach to Personalized News Article Recommendation;” Wei Chu, (Ant Group), Lihong…
Automatic post-deployment management of cloud applications
| Nagarajan Natarajan, Lei Zhao, Rodrigo Fonseca, Tatiana Racheva, and Yogesh Bansal
In the first two blog posts in this series, we presented our vision for Cloud Intelligence/AIOps (AIOps) research, and scenarios where innovations in AI technologies can help build and operate complex cloud platforms and services effectively and efficiently at scale.…
Research Collection – Shall we play a game?
From a research point of view, games offer an amazing environment in which to develop new machine learning algorithms and techniques. And we hope, in due course, that those new algorithms will feed back not just into gaming, but into…
In the news | Machine Learning (Theory)
HOMER: Provable Exploration in Reinforcement Learning
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…
In the news | Medium | Machine Learning
HOMER: Provable Exploration in Reinforcement Learning
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…
Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy
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…