Machine Learning & AI | NYC
Nouvelles et reportages
Ideas: Community building, machine learning, and the future of AI
| Jenn Wortman Vaughan et Hanna Wallach
As the Women in Machine Learning Workshop (WiML) marks its 20th annual gathering, cofounders, friends, and collaborators Jenn Wortman Vaughan and Hanna Wallach reflect on WiML’s evolution, navigating the field of ML, and their work in responsible AI.
AI Frontiers: Rethinking intelligence with Ashley Llorens and Ida Momennejad
| Ashley Llorens et 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, et 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 &…
NeurIPS 2023 highlights breadth of Microsoft’s machine learning innovation
We’re proud to have 100+ accepted papers At NeurIPS 2023, plus 18 workshops. Several submissions were chosen as oral presentations and spotlight posters, reflecting groundbreaking concepts, methods, or applications. Here’s an overview of those submissions.
Inferring rewards through interaction
| Jessica Maghakian, Akanksha Saran, Cheng Tan, et Paul Mineiro
In reinforcement learning, handcrafting reward functions is difficult and can yield algorithms that don’t generalize well. IGL-P, an interaction-grounded learning strategy, learns personalized rewards for different people in recommender system scenarios.
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…
Machine Learning for fair decisions
| Miro Dudík, John Langford, Hanna Wallach, et Alekh Agarwal
Over the past decade, machine learning systems have begun to play a key role in many high-stakes decisions: Who is interviewed for a job? Who is approved for a bank loan? Who receives parole? Who is admitted to a school?…
‘Contextual bandit’ breakthrough enables deeper personalization
| Miro Dudík
News portals that simultaneously personalize every part of the landing page for every visitor and mobile health apps that adaptively tweak every part of an exercise regimen to maximize the benefit of every user are becoming plausible due to an…