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SUMMARY:Inaugural Lectures - Prof. Caglar Gulcehre and Prof. Nicolas Flamm
 arion
DTSTART:20240312T180000
DTEND:20240312T193000
DTSTAMP:20260922T020824Z
UID:9a4f89da2cd9b17554478ba27aa626a442928dad9eb8ca246303e6b1
CATEGORIES:Inaugural lectures - Honorary Lecture
DESCRIPTION:Prof. Caglar Gulcehre\, Prof. Nicolas Flammarion\nDate: Tuesd
 ay 12 March 2024\n\nProgram: \n\n	18:00-18:05: Introduction by Prof. Rü
 diger Urbanke\, Dean of the IC School\n	18:05-18:35: Inaugural Lecture Pr
 of. Caglar Gulcehre\n	18:35-18:45: Q & A\n	18:45-18:50: Introduction by P
 rof. Rüdiger Urbanke\, Dean of the IC School\n	18:50-19:20: Inaugural Lec
 ture Prof. Nicolas Flammarion\n	19:20-19:30: Q & A\n	19:30-21:00: Apériti
 f in the FoodLab Alpine restaurant\n\nLocation:  CE 1 4\n\nRegistration:
  Click here\n\n*********************************************************
 *******\n\nProf. Caglar Gulcehre\n\nBridging Generative AI and Reinforceme
 nt Learning Towards a Safer and Brighter Future\n\nAbstract\nGenerative AI
  algorithms and foundation models such as ChatGPT are having an all-encomp
 assing impact on society and science. Focusing on the imperative of buildi
 ng safe and efficient AI models\, this lecture will unveil the synergies b
 etween reinforcement learning and generative AI approaches\, presenting op
 portunities for enhanced adaptability and ethical considerations to enable
  positive impacts on essential and challenging applications such as AI for
  science\, and robotics. In this talk\, I will introduce generative AI and
  reinforcement learning algorithms with a concise exploration of cutting-e
 dge developments – shedding light on the transformative potential of bri
 dging generative AI and reinforcement learning for responsible and impactf
 ul AI systems. I will conclude the talk with interesting future challenges
  in AI research with a positive outlook into what might be possible in the
  future with safe AI integration.\n \nAbout the speaker\nCaglar Gulcehre 
 is an assistant professor at EPFL\, leading the CLAIRE lab. His research r
 evolves around building intelligent agents through the lens of efficiency\
 , safety\, and robustness for challenging real-world environments. He is m
 otivated by solving real-world problems that would have a positive societa
 l impact\, such as the applications of AI for science and robotics. He fin
 ished his PhD under Yoshua Bengio at Mila (Quebec AI Institute). He previo
 usly worked at Google DeepMind\, Microsoft Research\, and IBM Research on 
 AI and Machine Learning. After more than 6 years at DeepMind as a staff re
 search scientist\, he recently transitioned into becoming a professor at E
 PFL. His work has been published in journals such as Nature\, JMLR\, TMLR\
 , and Neurocomputing and at conferences like ICML\, NeurIPS\, ICLR\, AISTA
 TS\, ACL\, and EMNLP. He has won the Best Paper Award at NeurIPS and the B
 est Paper runner-up at ICML. His work has been featured in several media o
 utlets such as Verge\, MIT News\, BBC\, Forbes\, and the New York Times.\n
 \n\n****************************************************************\n\nPr
 of. Nicolas Flammarion\n\nFollow the Gradient: A Tour of Neural Network Th
 eory\n\nAbstract\nWith ChatGPT and the latest advances in Large Language M
 odels\, artificial intelligence is the talk of the town. However\, the the
 oretical foundations of such large machine learning models remain unclear.
  In this talk\, I will discuss recent results that shed light on one of th
 e mysteries behind this success: why gradient methods converge to models t
 hat generalize well. We will begin our journey by discussing simple linear
  regression\, then move on to explore one-hidden-layer neural networks\, a
 nd finally\, investigate similar behavior in deep neural networks.\n\nAbou
 t the speaker\nNicolas Flammarion is a tenure-track assistant professor in
  computer science at EPFL. Prior to that\, he was a postdoctoral fellow at
  UC Berkeley\, hosted by Michael I. Jordan. He received his PhD in 2017 fr
 om Ecole Normale Supérieure in Paris\, where he was advised by Alexandre 
 d’Aspremont and Francis Bach. In 2018 he received the Fondation Mathéma
 tique Jacques Hadamard prize for the best PhD thesis in the field of optim
 ization and in 2021 a NeurIPS Outstanding Paper Award. His research focuse
 s primarily on learning problems at the interface of machine learning\, st
 atistics and optimization.
LOCATION:CE 1 4 https://plan.epfl.ch/?room==CE%201%204
STATUS:CONFIRMED
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