Volkan Cevher Epfl Cis Optimization Challenges In Robust Machine Learning

Center For Intelligent Systems CIS EPFL - YouTube

Center For Intelligent Systems CIS EPFL - YouTube 1 k.parameswaran,y t.huang,y p.hsieh,p.rolland,c.shi,andv.cevher,“robustreinforcementlearningviaadversarialtrainingwithlangevindynamics"inneurips, 2020. optimizationchallengesinadversarialml|prof. volkancevher,volkan.cevher@epfl.ch slide87/101. Abstract: “thanks to neural networks (nns), faster computation, and massive datasets, machine learning (ml) is under increasing pressure to provide automated solutions to even harder real world.

Volkan Cevher Elected IEEE Fellow - EPFL

Volkan Cevher Elected IEEE Fellow - EPFL Optimization challenges in adversarial machine learning epfl cis riken aip joint seminar series volkan cevher https://lions.epfl.ch @cevherlions laboratory for information and inference systems (lions). Volkan cevher associate professor, lions, epfl. amazon scholar (agi foundations). Volkan cevher received the b.sc. (valedictorian) in electrical engineering from bilkent university in ankara, turkey, in 1999 and the ph.d. in electrical and computer engineering from the georgia institute of technology in atlanta, ga in 2005. In this talk, we describe our recent research that has demonstrated that the non convex optimization dogma is false by showing that scalable stochastic optimization algorithms can avoid traps and rapidly obtain locally optimal solutions.

CSE DSI Machine Learning Seminar With Volkan Cevher (EPFL) | CSE Data Science Initiative ...

CSE DSI Machine Learning Seminar With Volkan Cevher (EPFL) | CSE Data Science Initiative ... Volkan cevher received the b.sc. (valedictorian) in electrical engineering from bilkent university in ankara, turkey, in 1999 and the ph.d. in electrical and computer engineering from the georgia institute of technology in atlanta, ga in 2005. In this talk, we describe our recent research that has demonstrated that the non convex optimization dogma is false by showing that scalable stochastic optimization algorithms can avoid traps and rapidly obtain locally optimal solutions. This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. we review recent learning formulations and models as well as their guarantees, describe scalable solution techniques and algorithms, and illustrate the trade offs involved. Researchers at epfl have uncovered a fundamental flaw in the training of machine learning systems and elaborated a new formulation for strengthening them against adversarial attacks. We will then explain our solutions to some of these challenges, focusing mostly on robustness aspects. in particular, we will show how the existing theory and methodology for robust training misses the mark and how we can bridge the theory and the practice. We propose a new adaptive optimization algorithm based on mirror descent for a class of possibly non convex smooth bilevel optimization problems.

Volkan Cevher — People - EPFL

Volkan Cevher — People - EPFL This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. we review recent learning formulations and models as well as their guarantees, describe scalable solution techniques and algorithms, and illustrate the trade offs involved. Researchers at epfl have uncovered a fundamental flaw in the training of machine learning systems and elaborated a new formulation for strengthening them against adversarial attacks. We will then explain our solutions to some of these challenges, focusing mostly on robustness aspects. in particular, we will show how the existing theory and methodology for robust training misses the mark and how we can bridge the theory and the practice. We propose a new adaptive optimization algorithm based on mirror descent for a class of possibly non convex smooth bilevel optimization problems.

Epfl | PDF | Mathematical Optimization | Mathematical Analysis

Epfl | PDF | Mathematical Optimization | Mathematical Analysis We will then explain our solutions to some of these challenges, focusing mostly on robustness aspects. in particular, we will show how the existing theory and methodology for robust training misses the mark and how we can bridge the theory and the practice. We propose a new adaptive optimization algorithm based on mirror descent for a class of possibly non convex smooth bilevel optimization problems.

EPFL-CIS & RIKEN-AIP Joint Workshop On Machine Learning - EPFL

EPFL-CIS & RIKEN-AIP Joint Workshop On Machine Learning - EPFL

Volkan Cevher (EPFL-CIS): “Optimization Challenges in Robust Machine Learning”