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• 2019 Best Paper Award at COLT 2019

• 2019 Best Student Paper Award at COLT 2019

• 2018 Best Student Paper Award at COLT 2018

• 2018 shadowsock 安卓apk

• 2016 NDSEG PhD Fellowship

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• In summer 2019 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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1. Beyond UCB: Optimal and Efficient Contextual Bandits
   with Regression Oracles
   

   Dylan J. Foster and Alexander Rakhlin.
   ICML 2020.
   Best spotlight talk, 14th Annual New York Academy of Science ML Symposium.

2. Logarithmic Regret for Adversarial Online Control
   

   Dylan J. Foster and Max Simchowitz.*
   ICML 2020.

3. Naive Exploration is Optimal for Online LQR
   

   Max Simchowitz and Dylan J. Foster.*
   ICML 2020.

4. Improved Bounds on Minimax Regret under Logarithmic Loss
   via Self-Concordance
   

   Blair Bilodeau, Dylan J. Foster, and Daniel Roy.
   ICML 2020.
   

5. 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 2020.
   

6. Open Problem: Model Selection for Contextual Bandits
   

   Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
   COLT 2020 Open Problem.
   

7. Learning Nonlinear Dynamical Systems from a Single Trajectory
   

   Dylan J. Foster, Alexander Rakhlin, and Tuhin Sarkar.
   L4DC 2020. Full oral presentation.
   

8. Lower Bounds for Non-Convex Stochastic Optimization
   

   Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster,
   Nathan Srebro, and Blake Woodworth.
   2019. Submitted.

9. Orthogonal Statistical Learning
   

   Dylan J. Foster and Vasilis Syrgkanis.
   Preprint 2019.
   Conference version appeared at COLT 2019 as "Statistical Learning with a Nuisance Component".

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   Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
   NeurIPS 2019. Spotlight presentation.
   Paper, shadowsock手机版

Hypothesis Set Stability and Generalization


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


2. Distributed Learning with Sublinear Communication
   

   Jayadev Acharya, Christopher De Sa, Dylan J. Foster, and Karthik Sridharan.
   ICML 2019. shadowsock安卓版设置.
   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.
苹果手机shadowsock.


3. 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. Best Student Paper Award.
   

4. 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 2020.

5. 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

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.
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6. 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

8. Online Learning: Sufficient Statistics and the Burkholder Method
   

   Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan.
   COLT 2018.
   苹果手机shadowsock, Poster

Inference in Sparse Graphs with Pairwise Measurements
and Side Information


Dylan J. Foster, Daniel Reichman, and Karthik Sridharan.
shadowsock手机版.
Paper, Poster

9. Parameter-Free Online Learning via Model Selection
   

   Dylan J. Foster, Satyen Kale, Mehryar Mohri, and Karthik Sridharan.
   NeurIPS 2017. Spotlight presentation.
   Paper, Poster

10. Spectrally-Normalized Margin Bounds for Neural Networks
    

    Peter Bartlett, Dylan J. Foster, and Matus Telgarsky.
    NeurIPS 2017. 苹果手机shadowsock.
    Paper, Poster

11. 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.
手机shadowsock教程. Preliminary version at Ad Auctions workshop at EC 2016.
手机shadowsock教程, 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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1. Adaptive Learning: Algorithms and Complexity
   

   Dylan J. Foster
   手机shadowsock教程 Department of Computer Science, Cornell University, 2019.
   

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1. ℓ_∞ Vector Contraction for Rademacher Complexity
   

   Dylan J. Foster and Alexander Rakhlin
   Technical note.
   2019.

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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 2020 (Senior PC), NeurIPS 2020 (Area Chair), ALT (2019,2020), Learning for Dynamics and Control (L4DC) 2020.

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.