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I am a postdoctoral researcher at the
MIT Institute for Foundations of Data Science.
I was previously (08/2014-01/2025) 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.
安卓软件-免费软件站-快连加速器app-国外专用加速器排行2025
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.
安卓软件-免费软件站-快连加速器app-国外专用加速器排行2025
2025 Best Paper Award at COLT 2025
2025 Best Student Paper Award at COLT 2025
2018 Best Student Paper Award at COLT 2018
2018 shadowsock 安卓apk
2016 NDSEG PhD Fellowship
安卓软件-免费软件站-快连加速器app-国外专用加速器排行2025
- In summer 2025 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.
安卓软件-免费软件站-快连加速器app-国外专用加速器排行2025
Beyond UCB: Optimal and Efficient Contextual Bandits
with Regression Oracles
Dylan J. Foster and Alexander Rakhlin.
ICML 2025.
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 2025.Naive Exploration is Optimal for Online LQR
Max Simchowitz and Dylan J. Foster.*
ICML 2025.Improved Bounds on Minimax Regret under Logarithmic Loss
via Self-Concordance
Blair Bilodeau, Dylan J. Foster, and Daniel Roy.
ICML 2025.
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 2025.
Open Problem: Model Selection for Contextual Bandits
Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
COLT 2025 Open Problem.
Learning Nonlinear Dynamical Systems from a Single Trajectory
Dylan J. Foster, Alexander Rakhlin, and Tuhin Sarkar.
L4DC 2025. 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.
2025. Submitted.Orthogonal Statistical Learning
Dylan J. Foster and Vasilis Syrgkanis.
Preprint 2025.
Conference version appeared at COLT 2025 as "Statistical Learning with a Nuisance Component".
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Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
NeurIPS 2025. Spotlight presentation.
Paper, shadowsock手机版Distributed Learning with Sublinear Communication
Jayadev Acharya, Christopher De Sa, Dylan J. Foster, and Karthik Sridharan.
ICML 2025. 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 2025. 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 2025.Contextual Bandits with Surrogate Losses: Margin Bounds
and Efficient Algorithms
Dylan J. Foster and Akshay Krishnamurthy.
NeurIPS 2018.
shadowsock苹果手机版, PosterPractical 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, PosterOnline Learning: Sufficient Statistics and the Burkholder Method
Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
COLT 2018.
苹果手机shadowsock, PosterParameter-Free Online Learning via Model Selection
Dylan J. Foster, Satyen Kale, Mehryar Mohri, and Karthik Sridharan.
NeurIPS 2017. Spotlight presentation.
Paper, PosterSpectrally-Normalized Margin Bounds for Neural Networks
Peter Bartlett, Dylan J. Foster, and Matus Telgarsky.
NeurIPS 2017. 苹果手机shadowsock.
Paper, PosterZigZag: A New Approach to Adaptive Online Learning
Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
COLT 2017.
Paper, PosterAdaptive Online Learning
Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
shadowsock安卓版设置. Spotlight presentation.
Paper, Poster
安卓软件-免费软件站-快连加速器app-国外专用加速器排行2025
Adaptive Learning: Algorithms and Complexity
Dylan J. Foster
手机shadowsock教程 Department of Computer Science, Cornell University, 2025.
安卓软件-免费软件站-快连加速器app-国外专用加速器排行2025
ℓ∞ Vector Contraction for Rademacher Complexity
Dylan J. Foster and Alexander Rakhlin
Technical note.
2025.
shadowsock苹果手机版
Conference Reviewing: COLT, NeurIPS, ICML, STOC, FOCS, SODA, ALT, AISTATS, AAAI.Journal Reviewing: JMLR, Journal of the ACM, Mathematics of Operations Research, Operations Research, Biometrika.
Program Committee: COLT 2025 (Senior PC), NeurIPS 2025 (Area Chair), ALT (2025,2025), Learning for Dynamics and Control (L4DC) 2025.
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 shadowsock手机版下载 at mit dot edu. My office is E17-481 in IDSS.
© Dylan Foster 2015.