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I am a postdoctoral researcher at the
MIT Institute for Foundations of Data Science.
I was previously (08/2014-01/2023) a PhD student in the Department of Computer Science at Cornell University advised by shadowsock安卓版设置. I received my BS and MS in electrical engineering from the University of Southern California in 2014.

I work on theory for machine learning.

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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.
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statistical learning, especially agnostic learning.
online learning and sequential prediction.
adaptivity and instance dependence.
high-dimensional statistics.
concentration inequalities and empirical process theory.

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In summer 2023 I will be a long-term visitor at the 安卓手机shadowsock教程 for the Foundations of Deep Learning program.
I have officially completed my PhD and started my postdoc at MIT!
I was featured on the shadowsock苹果手机版.
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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 shadowsock 安卓手机.
Adaptive Online Learning accepted into NIPS 2015.

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Beyond UCB: Optimal and Efficient Contextual Bandits
with Regression Oracles

Dylan J. Foster and Alexander Rakhlin.
ICML 2023.
Best spotlight talk, 14th Annual New York Academy of Science ML Symposium.
Logarithmic Regret for Adversarial Online Control

Dylan J. Foster and Max Simchowitz.*
ICML 2023.
Naive Exploration is Optimal for Online LQR

Max Simchowitz and Dylan J. Foster.*
ICML 2023.
Improved Bounds on Minimax Regret under Logarithmic Loss
via Self-Concordance

Blair Bilodeau, Dylan J. Foster, and Daniel Roy.
ICML 2023.
Second-Order Information in Non-Convex Stochastic Optimization:
Power and Limitations

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster,
Ayush Sekhari, and Karthik Sridharan.
COLT 2023.
Open Problem: Model Selection for Contextual Bandits

Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
COLT 2023 Open Problem.
Learning Nonlinear Dynamical Systems from a Single Trajectory

Dylan J. Foster, Alexander Rakhlin, and Tuhin Sarkar.
L4DC 2023. Full oral presentation.
Lower Bounds for Non-Convex Stochastic Optimization

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster,
Nathan Srebro, and Blake Woodworth.
2023. Submitted.
Orthogonal Statistical Learning

Dylan J. Foster and Vasilis Syrgkanis.
Preprint 2023.
Conference version appeared at COLT 2023 as "Statistical Learning with a Nuisance Component".

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Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
NeurIPS 2023. Spotlight presentation.
Paper, shadowsock手机版

Distributed Learning with Sublinear Communication

Jayadev Acharya, Christopher De Sa, Dylan J. Foster, and Karthik Sridharan.
ICML 2023. shadowsock安卓版设置.
Best spotlight talk, 13th Annual New York Academy of Science ML Symposium.

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 2023. Best Student Paper Award.
Statistical Learning with a Nuisance Component

Dylan J. Foster and Vasilis Syrgkanis.
苹果手机shadowsock. Best Paper Award.
Extended abstract for Orthogonal Statistical Learning.
Short version will also appear in Sister Conference Best Paper Track at IJCAI 2023.
Contextual Bandits with Surrogate Losses: Margin Bounds
and Efficient Algorithms

Dylan J. Foster and Akshay Krishnamurthy.
NeurIPS 2018.
shadowsock苹果手机版, Poster

Practical Contextual Bandits with Regression Oracles

Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert E. Schapire.*
苹果手机shadowsock. Long talk.
Paper, Poster
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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.
苹果手机shadowsock, Poster

Parameter-Free Online Learning via Model Selection

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

Peter Bartlett, Dylan J. Foster, and Matus Telgarsky.
NeurIPS 2017. 苹果手机shadowsock.
Paper, Poster
ZigZag: A New Approach to Adaptive Online Learning

Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
COLT 2017.
Paper, Poster

Adaptive Online Learning

Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
shadowsock安卓版设置. Spotlight presentation.
Paper, Poster

*denotes contribution order.

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Adaptive Learning: Algorithms and Complexity

Dylan J. Foster
手机shadowsock教程 Department of Computer Science, Cornell University, 2023.

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ℓ_∞ Vector Contraction for Rademacher Complexity

Dylan J. Foster and Alexander Rakhlin
Technical note.
2023.

© Dylan Foster 2015.

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