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

Michael Noukhovitch mnoukhov at gmail.com
Lun 12 Fév 20:52:58 EST 2018


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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