[Lisa_seminaires] [Tea Talk] Jacob Steinhardt (Stanford) Fri Feb 16 10:30AM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Ven 16 Fév 10:22:38 EST 2018


Reminder, this is in 10 minutes!

On Mon, Feb 12, 2018 at 8:52 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> This week we have *Jacob Steinhardt* from Stanford giving a special DIRO
> talk on *Fri Feb 16* at* 10:30AM* in room *AA1360*.
>
> Don't be fooled, this talk should be the real deal!
> Michael
>
> *TITLE *Provably Secure Machine Learning
>
> *KEYWORDS *Security, Optimization, AI Safety, Adversarial
>
> *ABSTRACT*
> The widespread use of machine learning systems creates a new class of
> computer security vulnerabilities where, rather than attacking the
> integrity of the software itself, malicious actors exploit the statistical
> nature of the learning algorithms. For instance, attackers can add fake
> data (e.g. by creating fake user accounts), or strategically manipulate
> inputs to the system once it is deployed. So far, attempts to defend
> against these attacks have focused on empirical performance against known
> sets of attacks. I will argue that this is a fundamentally inadequate
> paradigm for achieving meaningful security guarantees. Instead, we need
> algorithms that are provably secure by design, in line with best practices
> for traditional computer security. To achieve this goal, we take
> inspiration from robust statistics and robust optimization, but with an eye
> towards the security requirements of modern machine learning systems.
> Motivated by the trend towards models with thousands or millions of
> features, we investigate the robustness of learning algorithms in high
> dimensions. We show that most algorithms are brittle to even small
> fractions of adversarial data, and then develop new algorithms that are
> provably robust. Additionally, to accommodate the increasing use of deep
> learning, we develop an algorithm for certifiably robust optimization of
> non-convex models such as neural networks.
>
>
> *BIO*Jacob Steinhardt is a graduate student in artificial intelligence at
> Stanford University working with Percy Liang. His main research interest is
> in designing machine learning algorithms with the reliability properties of
> good software. So far this has led to the study of provably secure machine
> learning systems, as well as the design of learning algorithms that can
> detect their own failures and generalize predictably in new situations.
> Outside of research, Jacob is a technical advisor to the Open
> Philanthropy Project, and mentors gifted high school students through the
> USACO and SPARC summer programs.
>
>
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