Preetum Nakkiran
I'm a Research Scientist at Apple, working on foundations of machine learning.My research builds conceptual tools for understanding learning systems (including deep learning), using both theory and experiment. See the intro of my thesis for more on my motivations and methods.
I completed my PhD at Harvard, and had the unique pleasure of being advised by Madhu Sudan and Boaz Barak. While there, I cofounded the ML Foundations Group. In my postdoc I worked with Misha Belkin, as part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning. Go Bears!
[publications]
[CV]
[twitter]
[shortbio]
preetum@nakkiran.org
Interns at Apple (hosted, cohosted, or collaborated):
 Annabelle Carrell. PhD student, University of Cambridge.
 Lunjia Hu. PhD student, Stanford. (hosted by Parikshit Gopalan)
 Shivam Garg. PhD student, Stanford. (hosted by Kunal Talwar)
 Rylee Thompson. MASc student, University of Guelph. (hosted by Shuangfei Zhai)
 Elan Rosenfeld. PhD student, CMU. (hosted by Fartash Faghri)
Acknowledgements: I have had the pleasure of collaborating with the following excellent students & postdocs (partial list):
News and Olds:
 Sept 2022: 3 out of 6 papers accepted to NeurIPS 2022! The resubmissions will continue until rates improve.
 Aug 2022: I coorganized the Deep Learning Theory Workshop at the Simons Institute in Berkeley, CA.
 May 2022: I've joined Apple ML Research! Job talk [slides]
 Nov 2021: New manuscript on TuringUniversal Learners with Optimal Scaling Laws (freelunch.org). Also other papers.
 Sept 2021: I have moved to University of California, San Diego.
 July 2021: I defended my thesis! View the [slides], and read the [thesis]. I suggest the Introduction, which is written for general scientific audience.
 1994: Born
Recent/Upcoming Invited Talks
(I'm taking a sabbatical from preparing slides  please ask one of my excellent coauthors 😀) 7 Feb 2022: Stanford talk (Percy Liang group), on Distributional Generalization.
 15 Dec 2021: Reinforcement Learning Reading Group talk (Marcus Hutter group).
 7 Dec 2021: Simons Institute talk, on "Is Overfitting Actually Benign?." [slides]
 2 Dec 2021: Princeton talk (Sanjeev Arora group).
 30 Nov 2021: CMU ML Faculty Seminar talk.
 17 Nov 2021: MIT talk (Sasha Rakhlin group), on Distributional Generalization.
 15 Nov 2021: MIT talk (Poggio Lab), on The Deep Bootstrap Framework.
 Oct 2021: UCSD Theory Seminar talk, on "Theory for Deep Learning, and Deep Learning for Theory." [slides]
 Sept 2021: Deep Learning Classics & Trends talk, on Distributional Generalization. [slides]
 Aug 2021: UToronto talk (Roger Grosse group), on The Deep Bootstrap Framework. [slides]
 June 2021: Deep Learning Classics & Trends talk, on The Deep Bootstrap Framework. [slides]
 Apr 2021: Guest Lecture in ML Theory Course (Boaz Barak), speaking on scaling laws. [slides]
 Apr 2021: UPenn Seminar (Weijie Su group), on Distributional Generalization. [slides]
 Aug 2020: UCLA Big Data and Machine Learning Seminar, on Distributional Generalization.
 Feb 2021: Simons Collaboration Monthly Meeting, speaking on The Deep Bootstrap. [slides]
 Aug 2020: Max Planck+UCLA Math ML Seminar, speaking on Distributional Generalization. [video]
Research
I take a scientific approach to machine learning— trying to advance understanding through basic experiments and foundational theory.See [publications] for full list of papers.
Theses

Towards an Empirical Theory of Deep Learning
Preetum Nakkiran
Doctoral dissertation, Harvard University Graduate School of Arts and Sciences. July 2021. [cite]
Machine Learning Theory

The Calibration Generalization Gap
A. Michael Carrell, Neil Mallinar, James Lucas, Preetum Nakkiran
ICML 2022 DFUQ Workshop 
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Neil Mallinar*, Jamie Simon*, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, Preetum Nakkiran
NeurIPS 2022. 
Deconstructing Distributions: A Pointwise Framework of Learning
[demo]
Gal Kaplun*, Nikhil Ghosh*, Saurabh Garg, Boaz Barak, Preetum Nakkiran
Preprint. 2022. 
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning
Bogdan Kulynych*, YaoYuan Yang*, Yaodong Yu, Jarosław Błasiok, Preetum Nakkiran
NeurIPS 2022. 
Knowledge Distillation: Bad Models Can Be Good Role Models
Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai ShalevShwartz
NeurIPS 2022. 
Limitations of the NTK for Understanding Generalization in Deep Learning
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Preprint. 2022. 
Limitations of Neural Collapse for Understanding Generalization in Deep Learning
[tweet]
Like Hui, Mikhail Belkin, Preetum Nakkiran
Preprint. 2022. 
TuringUniversal Learners with Optimal Scaling Laws
Preetum Nakkiran
Manuscript. 2021. 
Revisiting Model Stitching to Compare Neural Representations
[tweet]
Yamini Bansal, Preetum Nakkiran, Boaz Barak
NeurIPS 2021. 
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
[slides]
[tweet]
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi
ICLR 2021. 
Distributional Generalization: A New Kind of Generalization
[10m talk]
[slides]
Preetum Nakkiran*, Yamini Bansal*
 DeskRejected from NeurIPS 2020.
 Rejected from ICLR 2021. [reviews]
 Rejected from ICML 2021. [reviews] [rebuttal] [meta]
 Rejected from NeurIPS 2021. [reviews]
 Rejected from ICLR 2022. [reviews] 
Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems
[tweet]
Preetum Nakkiran
OPT2020 Workshop (Best Student Paper) 
Optimal Regularization Can Mitigate Double Descent
Preetum Nakkiran, Prayaag Venkat, Sham Kakade, Tengyu Ma
ICLR 2021. 
Deep Double Descent: Where Bigger Models and More Data Hurt
Preetum Nakkiran, Gal Kaplun*, Yamini Bansal*, Tristan Yang, Boaz Barak, Ilya Sutskever
ICLR 2020. 
More Data Can Hurt for Linear Regression:
Samplewise Double Descent
Preetum Nakkiran
Manuscript. 2019. 
SGD on Neural Networks Learns Functions of Increasing Complexity
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak
NeurIPS 2019 (Spotlight). 
Adversarial Examples are Just Bugs, Too
Preetum Nakkiran
Distill 2019. 
Adversarial Robustness May Be at Odds With Simplicity
Preetum Nakkiran
(Merged appears in COLT 2019). 
The Generic Holdout:
Preventing FalseDiscoveries in Adaptive Data Science
Preetum Nakkiran, Jarosław Błasiok
Manuscript. 2018.
Theory

Algorithmic Polarization for Hidden Markov Models
Venkatesan Guruswami, Preetum Nakkiran, Madhu Sudan
ITCS 2019. 
General Strong Polarization
Jarosław Błasiok, Venkatesan Guruswami, Preetum Nakkiran, Atri Rudra, Madhu Sudan
STOC 2018. 
Tracking the L2 Norm with Constant Update Time
ChiNing Chou, Zhixian Lei, Preetum Nakkiran
APPROXRANDOM 2018. 
NearOptimal UGChardness of Approximating Max kCSP_R
Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan
APPROXRANDOM 2016.
Machine Learning

Compressing Deep Neural Networks Using a RankConstrained Topology
Preetum Nakkiran, Raziel Alvarez, Rohit Prabhavalkar, Carolina Parada
INTERSPEECH 2015. 
Automatic Gain Control and Multistyle Training for Robust SmallFootprint Keyword Spotting
with Deep Neural Networks
Rohit Prabhavalkar, Raziel Alvarez, Carolina Parada, Preetum Nakkiran, and Tara Sainath
ICASSP 2015.
About Me
For talks, you can use this [bio].I did my undergrad in EECS at UC Berkeley. I'm broadly interested in theory and science. In the past, I have interned at OpenAI (with Ilya Sutskever) Google Research (with Raziel Alvarez), Google Brain (with Behnam Neyshabur, Hanie Sedghi), and have also done research in errorcorrecting codes, distributed storage, and cryptography. I am grateful for past support from NSF GRFP and the Google PhD Fellowship.
See also my old website for more. This version borrowed in part from Luca Trevisan and Jon Barron.
What People are Saying
a "highlevel" scientist —colleague (ML)
makes plots and draws lines through them
—colleague (TCS)
has merits that outweigh flaws —reviewer 2
a complainer —my partner (among others)
Selected Tweets
 on science for science's sake
 the "definitional obstacle" to DL theory
 the "Natural Distributions" obstacle to generalization
 resources on causality
 how "causally explaining generalization" is not even wrong
 traps of defining objects which don't exist
 measures of dependence between RVs
 complaints about DL that are not actually about DL
 complaints about "science of ML" missing from ICML
 on calibration and overparameterization