BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Volkan Cevher (Laboratory for Information and Inference Systems – LIONS\, EPFL) DTSTART:20210930T160000Z DTEND:20210930T170000Z DTSTAMP:20260423T003236Z UID:MPML/56 DESCRIPTION:Title: Op timization Challenges in Adversarial Machine Learning\nby Volkan Cevhe r (Laboratory for Information and Inference Systems – LIONS\, EPFL) as p art of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbst ract\nThanks to neural networks (NNs)\, faster computation\, and massive d atasets\, machine learning (ML) is under increasing pressure to provide au tomated solutions to even harder real-world tasks beyond human performance with ever faster response times due to potentially huge technological and societal benefits. Unsurprisingly\, the NN learning formulations present a fundamental challenge to the back-end learning algorithms despite their scalability\, in particular due to the existence of traps in the non-conve x optimization landscape\, such as saddle points\, that can prevent algori thms from obtaining “good” solutions.\n\nIn this talk\, we describe ou r recent research that has demonstrated that the non-convex optimization d ogma is false by showing that scalable stochastic optimization algorithms can avoid traps and rapidly obtain locally optimal solutions. Coupled with the progress in representation learning\, such as over-parameterized neur al networks\, such local solutions can be globally optimal.\n\nUnfortunate ly\, this talk will also demonstrate that the central min-max optimization problems in ML\, such as generative adversarial networks (GANs)\, robust reinforcement learning (RL)\, and\ndistributionally robust ML\, contain sp urious attractors that do not include any stationary points of the origina l learning formulation. Indeed\, we will describe how algorithms are subje ct to a grander challenge\, including unavoidable convergence failures\, w hich could explain the stagnation in their progress despite the impressive earlier demonstrations.\n LOCATION:https://researchseminars.org/talk/MPML/56/ END:VEVENT END:VCALENDAR