Current research indicates that the role of generative AI in education is a double-edged sword: on one hand it can assist teachers for designing effective learning tasks and resources, on the other hand it can have negative effects on student learning and raises a range of ethical questions.
\r\nIn our new workshop series “Responsible AI skills for education” we will try to navigate these questions by focusing on specific teaching and learning activities and identifying practical skills you can develop for your everyday practice. Through a blend of concise theoretical input and hands-on activities, we will explore both the benefits and the risks of generative AI in education in short 1-hour sessions on campus. The first two workshops in the series focus on quiz design and critical analysis skills.
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\r\nEffective quiz design:
\r\nIn this 1-hour workshop, we will focus on the design of effective quiz questions for your class with the assistance of LLM tools. We will explore both the ethical and practical questions associated with LLM use for this task through theoretical inputs and hands-on activities.
Current research indicates that the role of generative AI in education is a double-edged sword: on one hand it can assist teachers for designing effective learning tasks and resources, on the other hand it can have negative effects on student learning and raises a range of ethical questions.
\r\nIn our new workshop series “Responsible AI skills for education” we will try to navigate these questions by focusing on specific teaching and learning activities and identifying practical skills you can develop for your everyday practice. Through a blend of concise theoretical input and hands-on activities, we will explore both the benefits and the risks of generative AI in education in short 1-hour sessions on campus. The first two workshops in the series focus on quiz design and critical analysis skills.
\r\n
\r\nTeaching critical analysis:
\r\nIn this 1-hour workshop, we will focus on how to help students develop critical analysis skills for reviewing LLM outputs in the context of learning tasks. The workshop will combine theoretical inputs and hands-on activities, drawing on your disciplinary expertise and fact-checking strategies.
\r\n
Current research indicates that the role of generative AI in education is a double-edged sword: on one hand it can assist teachers for designing effective learning tasks and resources, on the other hand it can have negative effects on student learning and raises a range of ethical questions.
\r\nIn our new workshop series “Responsible AI skills for education” we will try to navigate these questions by focusing on specific teaching and learning activities and identifying practical skills you can develop for your everyday practice. Through a blend of concise theoretical input and hands-on activities, we will explore both the benefits and the risks of generative AI in education in short 1-hour sessions on campus. The first two workshops in the series focus on quiz design and critical analysis skills.
\r\n
\r\nEffective quiz design:
\r\nIn this 1-hour workshop, we will focus on the design of effective quiz questions for your class with the assistance of LLM tools. We will explore both the ethical and practical questions associated with LLM use for this task through theoretical inputs and hands-on activities.
\r\n
Current research indicates that the role of generative AI in education is a double-edged sword: on one hand it can assist teachers for designing effective learning tasks and resources, on the other hand it can have negative effects on student learning and raises a range of ethical questions.
\r\nIn our new workshop series “Responsible AI skills for education” we will try to navigate these questions by focusing on specific teaching and learning activities and identifying practical skills you can develop for your everyday practice. Through a blend of concise theoretical input and hands-on activities, we will explore both the benefits and the risks of generative AI in education in short 1-hour sessions on campus. The first two workshops in the series focus on quiz design and critical analysis skills.
\r\n
\r\nTeaching critical analysis:
\r\nIn this 1-hour workshop, we will focus on how to help students develop critical analysis skills for reviewing LLM outputs in the context of learning tasks. The workshop will combine theoretical inputs and hands-on activities, drawing on your disciplinary expertise and fact-checking strategies.
\r\n
Triple S (Super Sandwich Seminar) will feature 3 speakers per week, each presenting for 15 minutes, followed by a 5 minutes Q&A session.
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\r\nProgram
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\r\n12:00 - 13:15 - Seminar\r\n
First-order methods are widely used in optimization and machine learning, and their behavior is often analyzed through the spectrum of worst case convergence rates. Obtaining such guarantees is often difficult and both time consuming and error-prone. Starting with the work of Drori and Teboulle (2014), novel techniques have been used to gain numerical insights, leading to the release of various performance estimation (PE) software.
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\r\nIn this talk, I will show how various computer-aided techniques can be used to study first-order optimization methods in a systematic way. From performance estimation problems with automated Lyapunov discovery, to symbolic regression and computer algebra systems, novel tools completely reshape the way we approach theory of optimization.
\r\nAs a main example, I will focus on error feedback methods used with compressed communication in distributed optimization. While error feedback has been widely studied, existing theory often provides untight (thus unreliable) bounds. I will present tight analyses with matching lower bounds that allow a fair comparison between error feedback schemes and standard compressed gradient descent, and help explain when error feedback is useful and when it is not.
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\r\nOverall, the talk aims to show how various computer-aided proofs can lead to clearer and more reliable insights into first-order optimization methods.
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\r\nBased on :
\r\nA Tight Theory of Error Feedback Algorithms in Distributed Optimization, DB Thomsen, A Taylor, A Dieuleveut
\r\nInternational Conference on Machine Learning (ICML 2026)
\r\nTight analyses of first-order methods with error feedback, DB Thomsen, A Taylor, A Dieuleveut
\r\nAdvances in Neural Information Processing Systems (NeurIPS) (2025).
\r\nPEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
\r\nB Goujaud, C Moucer, F Glineur, JM Hendrickx, AB Taylor, A Dieuleveut
\r\nMathematical Programming Computation 16 (3), 337-367 (2024)
\r\n(eventually - as a detour) Provable non-accelerations of the heavy-ball method B Goujaud, A Taylor, A Dieuleveut, Mathematical Programming, 1-59 (2025)
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