Lucas Maystre
@lucasmaystre
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Interacting with robots just got easier with the agentic capabilities of Gemini Robotics 1.5. Talk to the robot or show it things! See how the ER model reads a handwritten list on paper and packs the tools for the job.
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Meet Gemini Robotics 1.5! Our ER model brings embodied reasoning to the real world. It understands high-level goals like "pack ingredients for mushroom risotto" with planning & success detection. Also, check out the cool "active vision" behavior – observing the action up close!🔍
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🚀Announcing the Workshop on Computer Use Agents at #ICML2025 in July, Vancouver! Join us, to advance research on AI agents performing real-world computer tasks. 🤖Call for Papers & Demos: Deadline May 18, 2025 🎙️Exciting speaker lineup announced! ✍️Interested in
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We are looking for research scientist interns in the fields of AI, ML, economics, LLMs, recommender systems, speech processing, NLP, or IR. These internships are based in our London office. Apply at
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Our upcoming #KDD2023 paper presents our journey on prototyping Reinforcement Learning based playlist personalization at Spotify. You can watch a short video of our work at https://t.co/hYKcv8xrxd and read our latest blog post @SpotifyResearch. https://t.co/gXccXPddff
research.atspotify.com
Automatic Music Playlist Generation via Simulation-based Reinforcement Learning | Spotify Research
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How can we optimize recommendations for long-term user satisfaction, but avoid cold-start for new content? You can watch a short video of our upcoming #KDD2023 paper addressing this on https://t.co/SMBl9UGOra … and check our blog post @SpotifyResearch.
research.atspotify.com
Optimizing for the Long-Term Without Delay | Spotify Research
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How can we optimize recommendations for long-term user satisfaction, but avoid cold-start for new content? We address this in our #KDD2023 paper: "Impatient Bandits: Optimizing for the Long-Term Without Delay" Work by @tomcd_ @lucasmaystre @mounialalmas @DanielRuss0 & @MLciosek.
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Are your perceptions of the #carbon emissions you generate on a daily basis aligned with reality? Take the Climpact test to find out! @VictorKristof @lucasmaystre #CO2 #ClimateAction @EPFL
https://t.co/BUEjHKQmbo
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How can we develop more generalisable reward models for agent behaviours? Excited to share my @deepmind internship project, where we investigate finetuning Flamingo🦩w/ human reward annotations to train success detectors in 3 different domains! 📜 https://t.co/kuFH2SCNEG 🧵1/
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Are you a PhD student interested in tackling real-world problems using ML? My team (@SpotifyResearch) is hiring interns for next summer.
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What ideas can RL bring to survival analysis? And vice-versa? @DanielRuss0 and I explore this in our #NeurIPS2022 paper. Check out our post on the @SpotifyResearch blog to get a quick overview of our work.
research.atspotify.com
Survival Analysis Meets Reinforcement Learning | Spotify Research
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We work on cutting-edge research in Machine Learning, so we are excited to be sponsoring this year’s NeurIPS conference and the WiML workshop. If you are attending #NeurIPS2022 in New Orleans, please come by the Spotify booth and say hello! https://t.co/YjR1dFhOsx
research.atspotify.com
Spotify’s Contributions to NeurIPS 2022 | Spotify Research
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Also happy to announce that "Multistate Analysis with Infinite Mixtures of Markov Chains" has also been accepted to #UAI2022. Work by @lucasmaystre, @tiffanytywu, Roberto Sanchis & @TonyJebara
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New work shows that active offline policy selection (A-OPS) can accelerate policy development in real-world applications like robotics. A-OPS helps to quickly identify the best policy even when evaluation time on the robot is very limited: https://t.co/slBMBiIc5U 1/
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We look forward to presenting our paper Active Offline Policy Selection at #NeurIPS2021! paper: https://t.co/Ky8gFjPVsJ code: https://t.co/15JeFblV4z website: https://t.co/SexAyrq6SX
@yutianc @TomLePaine @caglarml @CauseMean @DJ_Mankowitz @notmisha @NandoDF
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Happy to share that our paper "The Dynamics of Exploration on Spotify" was accepted for #ICWSM22. Work led by @SpotifyResearch intern, Lillio Mok, with @samfway, @lucasmaystre, and @ashton1anderson. Congrats, Lillio and team!
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Tomorrow @fraspass will present our work on modeling the long-term evolution of user preferences at #TheWebConf . Paper will be available soon, in the meantime check out our blog post!
#TheWebConf paper "Where To Next? A Dynamic Model of User Preferences" will be presented tomorrow (22 April, Personalization 13:40 CEST). Work by @fraspass (research intern) @lucasmaystre Dmitrii Moor @ashton1anderson & @mounialalmas. See our blog post. https://t.co/YQ3243zhKD
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Looking forward to present our paper at AISTATS this afternoon. Hope to see you there!
Join us tomorrow 2pm (UK time) to hear about our #AISTATS2021 paper "Collaborative Classification from Noisy Labels" where @lucasmaystre will be there for Q&A. https://t.co/npJTllQaZC
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Our paper "Collaborative Classification from Noisy Labels" has been accepted to #AISTATS2021. It introduces a Bayesian model that fixes errors in metadata by taking advantage of user-item interactions. With @lucasmaystre, N Kumarappan & J Bütepage. Final version to come soon!
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Happy to share that our work on “Semi-supervised reward learning for offline reinforcement learning” is now available on arxiv! https://t.co/hZaBThXqjn
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