Dylan Foster

Research

I am interested in all aspects of generalization, sample complexity, and related algorithmic problems, especially for real-world problems such as interactive learning, deep learning, and non-convex learning. Specific topics include

interactive learning (contextual bandits, reinforcement learning,...).
deep learning.
stochastic/non-convex/distributed/... optimization.
statistical learning, especially agnostic learning.
online learning and sequential prediction.
adaptivity and instance dependence.
high-dimensional statistics.
concentration inequalities and empirical process theory.

Selected awards

2018 Best Student Paper Award at COLT 2018
2018 Facebook PhD Fellowship
2016 NDSEG PhD Fellowship

News

In summer 2019 I will be a long-term visitor at the Simons Institute for the Foundations of Deep Learning program.
I have officially completed my PhD and started my postdoc at MIT!
I was featured on the Facebook research blog.
I am on the PC for ALT 2019. Submit your best work!
In summer 2018 I will be interning with Vasilis Syrgkanis at MSR New England.
Two new papers at NIPS 2017 with spotlight presentations!
New paper ZigZag on online learning, decoupling, and Burkholder functions.
Spending summer 2017 with Rob Schapire and Miroslav Dudik at MSR NYC.
In Berkeley until May 2017 at the Simons Institute for the Foundations of Machine Learning program.
Learning in Games: Robustness of Fast Convergence at NIPS 2016
Spending summer in Sanjiv Kumar's group at Google Research NYC.
I am a 2016 NDSEG Fellow.
Gave invited talk at Learning Faster from Easy Data II at NIPS 2015.
Attending NIPS 2015 in Montreal to present Adaptive Online Learning.
Adaptive Online Learning accepted into NIPS 2015.

Preprints

Model Selection for Contextual Bandits

Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo
2019.
Orthogonal Statistical Learning

Dylan J. Foster and Vasilis Syrgkanis.
2019.

Publications

Distributed Learning with Sublinear Communication

Jayadev Acharya, Christopher De Sa, Dylan J. Foster, and Karthik Sridharan.
ICML 2019. Long talk.
Best spotlight talk, 13th Annual New York Academy of Science ML Symposium.
Sum-of-Squares Meets Square Loss:
Fast Rates for Agnostic Tensor Completion

Dylan J. Foster and Andrej Risteski.
COLT 2019.
The Complexity of Making the Gradient Small in
Stochastic Convex Optimization

Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan,
and Blake Woodworth.
COLT 2019.
Statistical Learning with a Nuisance Component

Dylan J. Foster and Vasilis Syrgkanis.
COLT 2019.
Extended abstract for Orthogonal Statistical Learning.
Contextual Bandits with Surrogate Losses: Margin Bounds
and Efficient Algorithms

Dylan J. Foster and Akshay Krishnamurthy.
NeurIPS 2018.
Paper, Poster
Uniform Convergence of Gradients for Non-Convex
Learning and Optimization

Dylan J. Foster, Ayush Sekhari, and Karthik Sridharan.
NeurIPS 2018. Short version at ICML 2018 Nonconvex Optimization workshop.
Paper, Poster
Practical Contextual Bandits with Regression Oracles

Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert E. Schapire.
ICML 2018. Long talk.
Paper, Poster
Logistic Regression: The Importance of Being Improper

Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, and Karthik Sridharan.
COLT 2018. Best Student Paper Award.
Paper, Poster
Online Learning: Sufficient Statistics and the Burkholder Method

Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
COLT 2018.
Paper, Poster
Inference in Sparse Graphs with Pairwise Measurements
and Side Information

Dylan J. Foster, Daniel Reichman, and Karthik Sridharan.
AISTATS 2018.
Paper, Poster
Parameter-Free Online Learning via Model Selection

Dylan J. Foster, Satyen Kale, Mehryar Mohri, and Karthik Sridharan.
NIPS 2017. Spotlight presentation.
Paper, Poster
Spectrally-Normalized Margin Bounds for Neural Networks

Peter Bartlett, Dylan J. Foster, and Matus Telgarsky.
NIPS 2017. Spotlight presentation.
Paper, Poster
ZigZag: A New Approach to Adaptive Online Learning

Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
COLT 2017.
Paper, Poster
Learning in Games: Robustness of Fast Convergence

Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, and Éva Tardos.
NIPS 2016. Preliminary version at Ad Auctions workshop at EC 2016.
Paper, Poster
Adaptive Online Learning

Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
NIPS 2015. Spotlight presentation.
Paper, Poster

Thesis

Adaptive Learning: Algorithms and Complexity

Dylan J. Foster
Ph.D. Thesis. Department of Computer Science, Cornell University, 2019.

Technical reports, notes, etc.

Hypothesis Set Stability and Generalization

Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri,
and Karthik Sridharan.
Technical report, 2019.

Service

Reviewing: COLT, NeurIPS, ICML, STOC, FOCS, SODA, JMLR, ALT, AISTATS, AAAI.
Program Committee: ALT 2019.

Teaching

Machine Learning Theory
Cornell University, Spring 2018.
Professor Karthik Sridharan.
Introduction to Analysis of Algorithms
Cornell University, Spring 2015.
Professors Éva Tardos and David Steurer.
Received outstanding teaching award.
Foundations of Artificial Intelligence
Cornell University, Fall 2014.
Professor Bart Selman.

Contact

I can be reached at dylanf at mit dot edu. My office is E17-481 in IDSS.