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    I am a postdoctoral researcher at the
    MIT Institute for Foundations of Data Science.
    I was previously (08/2014-01/2024) 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

    快连加速器 官网

    快连加速器 官网

    快连加速器 官网

    1. Beyond UCB: Optimal and Efficient Contextual Bandits
      with Regression Oracles

      Dylan J. Foster and Alexander Rakhlin.
      ICML 2024.
      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 2024.

    3. Naive Exploration is Optimal for Online LQR

      Max Simchowitz and Dylan J. Foster.*
      ICML 2024.

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

      Blair Bilodeau, Dylan J. Foster, and Daniel Roy.
      ICML 2024.

    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 2024.

    6. Open Problem: Model Selection for Contextual Bandits

      Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
      COLT 2024 Open Problem.

    7. Learning Nonlinear Dynamical Systems from a Single Trajectory

      Dylan J. Foster, Alexander Rakhlin, and Tuhin Sarkar.
      L4DC 2024. 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.
      2024. Submitted.

    9. Orthogonal Statistical Learning

      Dylan J. Foster and Vasilis Syrgkanis.
      Preprint 2024.
      Conference version appeared at COLT 2024 as "Statistical Learning with a Nuisance Component".

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      Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.
      NeurIPS 2024. Spotlight presentation.
      Paper, shadowsock手机版

    2. Distributed Learning with Sublinear Communication

      Jayadev Acharya, Christopher De Sa, Dylan J. Foster, and Karthik Sridharan.
      ICML 2024. shadowsock安卓版设置.
      Best spotlight talk, 13th Annual New York Academy of Science ML Symposium.

    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 2024. 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 2024.

    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

    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

    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

    12. 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, 2024.

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    1. ℓ∞ Vector Contraction for Rademacher Complexity

      Dylan J. Foster and Alexander Rakhlin
      Technical note.
      2024.

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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 2024 (Senior PC), NeurIPS 2024 (Area Chair), ALT (2024,2024), Learning for Dynamics and Control (L4DC) 2024.

    Teaching

    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.

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