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DTSTAMP:20260403T233859
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SUMMARY:Optimization for ML and AI Seminar: Transformers Learn Generalizable Chain-of-Thought Reasoning via Gradient Descent
DESCRIPTION:Yuejie Chi\, Yale \nAbstract: Transformers have demonstrated remarkable chain-of-thought reasoning capabilities\, yet\, the underlying mechanisms by which they acquire and extrapolate these capabilities remain limited. This talk presents a theoretical analysis of transformers trained via gradient descent for symbolic reasoning and state tracking tasks with increasing problem complexity. Our analysis reveals the coordination of multi-head attention to solve multiple subtasks in a single autoregressive path\, and the bootstrapping of inherently sequential reasoning through recursive self-training curriculum. Our optimization-based guarantees demonstrate that even shallow multi-head transformers\, with chain-of-thought\, can be trained to effectively solve problems that would otherwise require deeper architectures. \n\nYuejie Chi is the Charles C. and Dorothea S. Dilley Professor of Statistics and Data Science at Yale University\, with a secondary appointment in Computer Science\, and a member of the Yale Institute for Foundations of Data Science. Before joining Yale\, Dr. Chi was the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Melon University\, with affiliation in MLD and CyLab. She also spent some time as a visiting researcher at Meta’s Fundamental AI Research (FAIR). Dr. Yue’s research interests lie in the theoretical and algorithmic foundations of data science\, generative AI\, reinforcement learning\, and signal processing\, motivated by applications in scientific and engineering domains. Her current focus is on improving the performance\, efficiency and reliability of generative AI and decision making\, driven by data-intensive but resource-constrained scenarios.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-transformers-learn-generalizable-chain-of-thought-reasoning-via-gradient-descent/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/chi-yuejie-e1760472307997.jpeg
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DTSTART;TZID=America/Los_Angeles:20260325T110000
DTEND;TZID=America/Los_Angeles:20260325T120000
DTSTAMP:20260403T233859
CREATED:20260310T175540Z
LAST-MODIFIED:20260326T215133Z
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SUMMARY:TILOS-SDSU Seminar: Autopilots Need Parachutes: Reliability Lessons from LLM-Automated Embedded AI Systems
DESCRIPTION:Roberto Morabito\, EURECOM \nAbstract: Embedded AI systems are becoming increasingly complex to develop and maintain\, requiring specialized workflows that span data processing\, model conversion\, optimization\, and deployment across heterogeneous hardware platforms. Recently\, large language models have emerged as a promising tool to automate parts of this lifecycle. In this talk\, I present recent work investigating the use of generative AI models as orchestration agents for embedded machine learning pipelines. Using an automated system that leverages LLMs to generate and iteratively refine software artifacts for embedded platforms\, we evaluate the feasibility of automating key stages of the AI lifecycle. Our empirical results reveal both the promise and the limitations of this approach. Generative models can significantly accelerate development workflows. However\, they also introduce instability\, iterative failure modes\, and unpredictable operational costs. I will discuss the main failure patterns observed in practice and outline research directions aimed at improving reliability through hybrid reasoning frameworks and system-level feedback mechanisms. \n\nRoberto Morabito is an Assistant Professor in the Networked Systems group of the Communication Systems Department at EURECOM\, France\, and a Docent at the University of Helsinki. Before joining EURECOM\, he was a Senior Researcher in the Department of Computer Science at the University of Helsinki. Earlier in his career\, he spent eight years at Ericsson Research Finland\, where he worked on cloud platforms\, IoT systems\, and cyber-physical systems. He received his PhD in Networking Technology from Aalto University in 2019 and was a postdoctoral researcher at the EDGE Lab\, School of Electrical and Computer Engineering\, Princeton University. His research lies at the intersection of networked systems\, edge computing\, and distributed AI\, focusing on the design and lifecycle management of AI systems operating under computing and networking resource constraints.
URL:https://tilos.ai/event/tilos-sdsu-seminar-autopilots-need-parachutes-reliability-lessons-from-llm-automated-embedded-ai-systems/
LOCATION:Lamden Hall 341 (SDSU) and Virtual\, San Diego\, CA\, 92182\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/03/morabito-roberto-e1773165764846.jpeg
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DTSTART;TZID=America/Los_Angeles:20260327T100000
DTEND;TZID=America/Los_Angeles:20260327T110000
DTSTAMP:20260403T233859
CREATED:20260317T231250Z
LAST-MODIFIED:20260331T142721Z
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SUMMARY:TILOS-Optimization for ML and AI Seminar: Implicit bias results for Muon\, Adam\, and Friends
DESCRIPTION:Matus Telgarsky\, New York University \nAbstract: This talk will give both an empirical overview and a few simple bonds controlling the optimization path\, or implicit bias\, of modern optimization methods such as Adam and Muon (and Friends). The talk will begin with empirical results demonstrating the implicit bias phenomenon with shallow networks and also transformers combined with chain-of-thought. The talk will then briefly survey a few mathematical implicit bias analyses of nonlinear networks\, which unfortunately do not carry through to transformers. As such\, the talk concludes with a technical portion presenting another approach to analyzing these optimization methods in the linear case\, providing generic implicit bias results for them\, and empirically demonstrating hope that this particular methodology can carry over to the nonlinear case. \n\nMatus Telgarsky is an Associate Professor of Computer Science at the Courant Institute of Math at NYU\, specializing in deep learning theory. The highlight of his academic career was completing a PhD under Sanjoy Dasgupta at UC San Diego. Adventures since then include co-chairing the Midwest ML Symposium in 2017 with Po-Ling Loh\, and chairing two semester-long Simons Institute Programs at UC Berkeley. Accolades include a 2018 NSF Career Award and delivering a COLT 2025 keynote.
URL:https://tilos.ai/event/tilos-optimization-for-ml-and-ai-seminar-implicit-bias-results-for-muon-adam-and-friends/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/03/telgarsky-matus-e1773789078482.jpg
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