BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Mathieu Blondel (Google Research\, Brain team\, Paris) DTSTART:20210604T130000Z DTEND:20210604T140000Z DTSTAMP:20260423T003256Z UID:MPML/48 DESCRIPTION:Title: Ef ficient and Modular Implicit Differentiation\nby Mathieu Blondel (Goog le Research\, Brain team\, Paris) as part of Mathematics\, Physics and Mac hine Learning (IST\, Lisbon)\n\n\nAbstract\nAutomatic differentiation (aut odiff) has revolutionized machine learning. It allows expressing complex c omputations by composing elementary ones in creative ways and removes the burden of computing their derivatives by hand. More recently\, differentia tion of optimization problem solutions has attracted widespread attention with applications such as optimization as a layer\, and in bi-level proble ms such as hyper-parameter optimization and meta-learning. However\, the f ormulas for these derivatives often involve case-by-case tedious mathemati cal derivations. In this work\, we propose a unified\, efficient and modul ar approach for implicit differentiation of optimization problems. In our approach\, the user defines (in Python in the case of our implementation) a function F capturing the optimality conditions of the problem to be diff erentiated. Once this is done\, we leverage autodiff of F and implicit dif ferentiation to automatically differentiate the optimization problem. Our approach thus combines the benefits of implicit differentiation and autodi ff. It is efficient as it can be added on top of any state-of-the-art solv er and modular as the optimality condition specification is decoupled from the implicit differentiation mechanism. We show that seemingly simple pri nciples allow to recover many recently proposed implicit differentiation m ethods and create new ones easily. We demonstrate the ease of formulating and solving bi-level optimization problems using our framework. We also sh owcase an application to the sensitivity analysis of molecular dynamics.\n LOCATION:https://researchseminars.org/talk/MPML/48/ END:VEVENT END:VCALENDAR