@collasconf.bsky.social
👏 Join us in celebrating these early-career researchers at #CoLLAs2025!
📍 Aug 11–14 @ University of Pennsylvania
🔗 lifelong-ml.cc/Conferences/...
#AI #ML #LifelongLearning #ContinualLearning #CoLLAs2025
📍 Aug 11–14 @ University of Pennsylvania
🔗 lifelong-ml.cc/Conferences/...
#AI #ML #LifelongLearning #ContinualLearning #CoLLAs2025
CoLLAs
Collas
lifelong-ml.cc
August 5, 2025 at 5:17 PM
👏 Join us in celebrating these early-career researchers at #CoLLAs2025!
📍 Aug 11–14 @ University of Pennsylvania
🔗 lifelong-ml.cc/Conferences/...
#AI #ML #LifelongLearning #ContinualLearning #CoLLAs2025
📍 Aug 11–14 @ University of Pennsylvania
🔗 lifelong-ml.cc/Conferences/...
#AI #ML #LifelongLearning #ContinualLearning #CoLLAs2025
🧩 @jmendezm.bsky.social (Stony Brook University)
Talk: Unlocking Lifelong Robot Learning With Modularity
Jorge shows how modularity in robotics enables scalable lifelong learning for robots.
#RobotLearning #Modularity #EmbodiedAI
Talk: Unlocking Lifelong Robot Learning With Modularity
Jorge shows how modularity in robotics enables scalable lifelong learning for robots.
#RobotLearning #Modularity #EmbodiedAI
August 5, 2025 at 5:17 PM
🧩 @jmendezm.bsky.social (Stony Brook University)
Talk: Unlocking Lifelong Robot Learning With Modularity
Jorge shows how modularity in robotics enables scalable lifelong learning for robots.
#RobotLearning #Modularity #EmbodiedAI
Talk: Unlocking Lifelong Robot Learning With Modularity
Jorge shows how modularity in robotics enables scalable lifelong learning for robots.
#RobotLearning #Modularity #EmbodiedAI
🔍 @tylerlhayes.bsky.social (Georgia Tech)
Talk: Adapting to the Unknown: Lifelong Learning, Novelty Discovery, & Beyond
Tyler presents two complementary paradigms for enabling adaptation in neural networks: dynamic adaptation and static adaptation.
#NoveltyDiscovery #OpenWorld #LifelongLearning
Talk: Adapting to the Unknown: Lifelong Learning, Novelty Discovery, & Beyond
Tyler presents two complementary paradigms for enabling adaptation in neural networks: dynamic adaptation and static adaptation.
#NoveltyDiscovery #OpenWorld #LifelongLearning
August 5, 2025 at 5:17 PM
🔍 @tylerlhayes.bsky.social (Georgia Tech)
Talk: Adapting to the Unknown: Lifelong Learning, Novelty Discovery, & Beyond
Tyler presents two complementary paradigms for enabling adaptation in neural networks: dynamic adaptation and static adaptation.
#NoveltyDiscovery #OpenWorld #LifelongLearning
Talk: Adapting to the Unknown: Lifelong Learning, Novelty Discovery, & Beyond
Tyler presents two complementary paradigms for enabling adaptation in neural networks: dynamic adaptation and static adaptation.
#NoveltyDiscovery #OpenWorld #LifelongLearning
🤖 Jaehong Yoon (NTU Singapore)
Talk: Toward Continually Growing Embodied AIs via Selective and Purposeful Experience
From multimodal LLMs to LLM-generated training environments, Jaehong shows how purposeful experience helps agents grow efficiently.
#EmbodiedAI #ContinualLearning #LLMs
Talk: Toward Continually Growing Embodied AIs via Selective and Purposeful Experience
From multimodal LLMs to LLM-generated training environments, Jaehong shows how purposeful experience helps agents grow efficiently.
#EmbodiedAI #ContinualLearning #LLMs
August 5, 2025 at 5:17 PM
🤖 Jaehong Yoon (NTU Singapore)
Talk: Toward Continually Growing Embodied AIs via Selective and Purposeful Experience
From multimodal LLMs to LLM-generated training environments, Jaehong shows how purposeful experience helps agents grow efficiently.
#EmbodiedAI #ContinualLearning #LLMs
Talk: Toward Continually Growing Embodied AIs via Selective and Purposeful Experience
From multimodal LLMs to LLM-generated training environments, Jaehong shows how purposeful experience helps agents grow efficiently.
#EmbodiedAI #ContinualLearning #LLMs
🧠 @khimya.bsky.social (Google DeepMind)
Talk: Navigating the Affordance Landscape for Continual Agent Adaptation
She propose rethinking the agent-environment interaction through the lens of affordances and using that to develop data-efficient agents.
#RL #Affordances #ContinualLearning
Talk: Navigating the Affordance Landscape for Continual Agent Adaptation
She propose rethinking the agent-environment interaction through the lens of affordances and using that to develop data-efficient agents.
#RL #Affordances #ContinualLearning
August 5, 2025 at 5:17 PM
🧠 @khimya.bsky.social (Google DeepMind)
Talk: Navigating the Affordance Landscape for Continual Agent Adaptation
She propose rethinking the agent-environment interaction through the lens of affordances and using that to develop data-efficient agents.
#RL #Affordances #ContinualLearning
Talk: Navigating the Affordance Landscape for Continual Agent Adaptation
She propose rethinking the agent-environment interaction through the lens of affordances and using that to develop data-efficient agents.
#RL #Affordances #ContinualLearning
📘 Mathematics of Continual Learning
by Liangzu Peng & René Vidal
A deep dive into the mathematical principles behind continual learning and adaptive filtering. Highlights connections between the two fields and proposes directions grounded in rigorous theory.
🔗 arxiv.org/abs/2504.17963
by Liangzu Peng & René Vidal
A deep dive into the mathematical principles behind continual learning and adaptive filtering. Highlights connections between the two fields and proposes directions grounded in rigorous theory.
🔗 arxiv.org/abs/2504.17963
July 31, 2025 at 6:04 PM
📘 Mathematics of Continual Learning
by Liangzu Peng & René Vidal
A deep dive into the mathematical principles behind continual learning and adaptive filtering. Highlights connections between the two fields and proposes directions grounded in rigorous theory.
🔗 arxiv.org/abs/2504.17963
by Liangzu Peng & René Vidal
A deep dive into the mathematical principles behind continual learning and adaptive filtering. Highlights connections between the two fields and proposes directions grounded in rigorous theory.
🔗 arxiv.org/abs/2504.17963
🔍 Information-Theoretic Measures for Multi-Expert FM Adaptation by Yang Li & Shao-Lun Huang
How much should we transfer from each expert model? This tutorial explores info-theoretic tools (KL, HGR, entropy, etc.) to measure and guide expert utility in multi-source & continual learning.
How much should we transfer from each expert model? This tutorial explores info-theoretic tools (KL, HGR, entropy, etc.) to measure and guide expert utility in multi-source & continual learning.
July 31, 2025 at 6:04 PM
🔍 Information-Theoretic Measures for Multi-Expert FM Adaptation by Yang Li & Shao-Lun Huang
How much should we transfer from each expert model? This tutorial explores info-theoretic tools (KL, HGR, entropy, etc.) to measure and guide expert utility in multi-source & continual learning.
How much should we transfer from each expert model? This tutorial explores info-theoretic tools (KL, HGR, entropy, etc.) to measure and guide expert utility in multi-source & continual learning.