I am a final-year PhD candidate in the Statistics department at UCLA and a
member of the Statistical Machine
Learning Lab led by Quanquan
Gu.

Starting in August of 2021, I will be a postdoctoral fellow at UC Berkeley working with
Peter Bartlett and Bin Yu as a part of the NSF/Simons program Collaboration on the Theoretical Foundations of Deep Learning.

### Research Interests

* Theory of deep learning: optimization, generalization, etc.

* Statistical learning theory

* Applications of deep learning: natural language understanding, audio
analysis, etc.

**News**

__2021__
* I'm giving a talk at the

ETH Zurich Young Data Science Researcher Seminar on April 14th.

* I'm reviewing for the

Theory of Overparameterized Machine Learning Workshop.

* I'm giving a talk at the

UCLA Big Data and Machine Learning Seminar on April 16th.

* I'm giving a talk at the

Max-Planck-Insitute (MPI) MiS Machine Learning Seminar on March 11th.

* New

paper showing SGD-trained neural networks of any width generalize in the presence of adversarial label noise.

* I'm reviewing for

ICML 2021.

__2020__
* New

paper on agnostic
learning of halfspaces using gradient descent is now on arXiv.

* My

single neuron paper
was accepted at NeurIPS 2020.

* I received a Best Reviewer Award for ICML 2020.

* I will be attending the

IDEAL
Special Quarter on the Theory of Deep Learning hosted by
TTIC/Northwestern for the fall quarter.

* I'm reviewing for

AISTATS
2021.

* I've been awarded a

Dissertation
Year Fellowship by UCLA's Graduate Division.

* New

paper on agnostic
PAC learning of a single neuron using gradient descent is now on arXiv.

* New

paper
accepted at

*Brain Structure and Function* from work with
researchers at UCLA School of Medicine.

* I'll be (remotely) working at Amazon's

Alexa
AI group for the summer as a research intern, working on natural
language understanding.

* I'm reviewing for

NeurIPS 2020.

* I'm reviewing for

ICML 2020.

*2019*
* My paper with Yuan Cao and Quanquan Gu, "Algorithm-dependent
Generalization Bounds for Overparameterized Deep Residual Networks", was
accepted at NeurIPS 2019 (

arXiv
version,

NeurIPS
version).

I am currently a PhD candidate in the Statistics department at UCLA
and a member of the Statistical
Machine Learning Lab. I am supervised by Ying
Nian Wu from the Department of Statistics and Quanquan
Gu from the Department of Computer Science. I completed my
masters in mathematics at the University of British Columbia,
Vancouver, in May 2015. I was a member of the Probability
Group, and Ed
Perkins was my supervisor. Before that, I completed my
undergraduate degree in mathematics at McGill University in 2013.

You may find more information about me on my CV
(last updated February 2021).

For 2020-2021, I have a UCLA Dissertation Year Fellowship and will
not be teaching.

Past teaching positions:

Spring 2020: Stats 100C, Linear Models with Arash Amini.

Fall 2019: Stats 102C, Monte Carlo Methods with Qing Zhou.

Summer 2016, Session C: Stats 10, Intro Statistics with Juana Sanchez.

Summer 2016, Session A: Stats 10, Intro Statistics with Miles Chen.

Fall 2016: Stats 100A, Introduction to Probability Theory with Ying
Nian Wu.

Winter 2016: Stats 100B, Introduction to Mathematical Statistics with
Jessica Li.

#### Preprints

1.

**S. Frei**, Y. Cao, and Q. Gu. Provable generalization of SGD-trained neural networks of any width in the presence of adversarial label noise. Preprint,

*arXiv*:2101.01152.

[arxiv]
2.

**S. Frei**, Y. Cao, and Q. Gu. Agnostic learning of halfspaces
with gradient descent via soft margins. Preprint,

*arXiv*:2010.00539.

[arxiv]
#### Refereed Conference Publications

3.

**S. Frei**, Y. Cao, and Q. Gu. Agnostic
learning of a single neuron with gradient descent. In

* Advances in Neural
Information Processing Systems (NeurIPS)*, 2020.

[arxiv]
4.

**S. Frei**, Y. Cao, and Q. Gu. Algorithm-dependent
generalization bounds for overparameterized deep residual networks. In

*Advances in Neural Information Processing Systems (NeurIPS)*, 2019.

[arxiv],

[camera ready]
#### Journal Publications

5. A.E. Anderson, M. Diaz-Santos,

**S. Frei** *et al.*
Hemodynamic latency is associated with reduced intelligence across the
lifespan: an fMRI DCM study of aging, cerebrovascular integrity, and
cognitive ability.

*Brain Structure and Function*, 2020.

[link]
6.

**S. Frei** and E. Perkins. A
lower bound for

$p_c$ in range-

$R$
bond percolation in two and three dimensions.

*Electronic Journal of Probability*
21(56), 2016.

[link]
7.

**S. Frei**, K. Lockwood, G. Stewart, J. Boyer, and B.S. Tilley. On
thermal resistance in concentric residential geothermal heat
exchangers.

*Journal of Engineering Mathematics* 86(1),
2014.

[link]