BEGIN:VCALENDAR
VERSION:2.0
PRODID:-// - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://tilos.ai
X-WR-CALDESC:Events for 
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20230312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20231105T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230419T100000
DTEND;TZID=America/Los_Angeles:20230419T110000
DTSTAMP:20260405T230740
CREATED:20250903T184737Z
LAST-MODIFIED:20250903T184902Z
UID:7365-1681898400-1681902000@tilos.ai
SUMMARY:TILOS Seminar: ML Training Strategies Inspired by Humans’ Learning Skills
DESCRIPTION:Pengtao Xie\, Assistant Professor\, UC San Diego \nAbstract: Humans\, as the most powerful learners on the planet\, have accumulated a lot of learning skills\, such as learning through tests\, interleaving learning\, self-explanation\, active recalling\, to name a few. These learning skills and methodologies enable humans to learn new topics more effectively and efficiently. We are interested in investigating whether humans’ learning skills can be borrowed to help machines to learn better. Specifically\, we aim to formalize these skills and leverage them to train better machine learning (ML) models. To achieve this goal\, we develop a general framework\, Skillearn\, which provides a principled way to represent humans’ learning skills mathematically and use the formally-represented skills to improve the training of ML models. In two case studies\, we apply Skillearn to formalize two learning skills of humans: learning by passing tests and interleaving learning\, and use the formalized skills to improve neural architecture search. \n\nPengtao Xie is an assistant professor at UC San Diego. He received his PhD from the Machine Learning Department at Carnegie Mellon University in 2018. His research interests lie in machine learning inspired by human learning and its applications in healthcare. His research outcomes have been adopted by medical device companies\, medical imaging centers\, hospitals\, etc. and have been published at top-tier artificial intelligence conferences and journals including ICML\, NeurIPS\, ACL\, ICCV\, TACL\, etc. He is the recipient of the Tencent AI-Lab Faculty Award\, Tencent WeChat Faculty Award\, the Innovator Award presented by the Pittsburgh Business Times\, the Siebel Scholars award\, and the Goldman Sachs Global Leader Scholarship.
URL:https://tilos.ai/event/tilos-seminar-ml-training-strategies-inspired-by-humans-learning-skills/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2023/10/xie-pengtao-scaled-e1696371691928.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230426T090000
DTEND;TZID=America/Los_Angeles:20230426T100000
DTSTAMP:20260405T230740
CREATED:20250828T204254Z
LAST-MODIFIED:20250828T204254Z
UID:7361-1682499600-1682503200@tilos.ai
SUMMARY:TILOS-OPTML++ Seminar: Sums of Squares: from Algebra to Analysis
DESCRIPTION:Francis Bach\, NRIA\, ENS\, and PSL Paris \nAbstract: The representation of non-negative functions as sums of squares has become an important tool in many modeling and optimization tasks. Traditionally applied to polynomial functions\, it requires rich tools from algebraic geometry that led to many developments in the last twenty years. In this talk\, I will look at this problem from a functional analysis point of view\, leading to new applications and new results on the performance of sum-of-squares optimization. \n\nFrancis Bach is a researcher at Inria\, leading since 2011 the machine learning team which is part of the Computer Science department at Ecole Normale Supérieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005\, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris\, then he joined the computer vision project-team at Inria/Ecole Normale Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in sparse methods\, kernel-based learning\, large-scale optimization\, computer vision and signal processing. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council\, and received the Inria young researcher prize in 2012\, the ICML test-of-time award in 2014 and 2019\, as well as the Lagrange prize in continuous optimization in 2018\, and the Jean-Jacques Moreau prize in 2019. He was elected in 2020 at the French Academy of Sciences. In 2015\, he was program co-chair of the International Conference in Machine learning (ICML)\, and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research.
URL:https://tilos.ai/event/tilos-optml-seminar-sums-of-squares-from-algebra-to-analysis/
LOCATION:Virtual
CATEGORIES:TILOS - OPTML++ Seminar Series,TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/francis_bach_septembre_2016_small-e1711659265321-yFIGFR.jpg
END:VEVENT
END:VCALENDAR