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Papers

• Sample-efficient proper PAC learning with approximate differential privacy.
  Badih Ghazi, Noah Golowich, Ravi Kumar, and Pasin Manurangsi.
  Extended abstract at PriML/PPML 2020 workshop (Oral presentation). [workshop link] [talk video] [slides]
  To appear in STOC 2021.
• Near-tight closure bounds for Littlestone and threshold dimensions.
  Badih Ghazi, Noah Golowich, Ravi Kumar, and Pasin Manurangsi.
  To appear in ALT 2021 (Best student paper award). [conf]
• On the power of multiple anonymous messages.
  Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker.
  Extended abstract at FORC 2020. [conf] [talk video] [slides]
  To appear in Eurocrypt 2021.
  
• On Deep Learning with Label Differential Privacy.
  Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang.
  

• Tight last-iterate convergence rates for no-regret learning in multi-player games.
  () Noah Golowich, Sarath Pattathil, and Constantinos Daskalakis.
  In NeurIPS 2020. [conf]
• Independent Policy Gradient Methods for Competitive Reinforcement Learning.
  Constantinos Daskalakis, Dylan Foster, and Noah Golowich.
  In NeurIPS 2020. [conf] [slides]
• Last iterate is slower than averaged iterate in smooth convex-concave saddle point problems.
  () Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, and Asuman Ozdaglar.
  In COLT 2020. [conf] [talk video] [slides]
• Pure differentially private summation from anonymous messages.
  Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker.
  In ITC 2020. [conf]
• Round complexity of common randomness generation: the amortized setting.
  Noah Golowich and Madhu Sudan.
  In SODA 2020. [conf] [slides (ppt)] [slides (pdf)]
• A convergence analysis of gradient descent for deep linear neural networks.
  Sanjeev Arora, Nadav Cohen, Noah Golowich, and Wei Hu.
  In ICLR 2019. [conf]
• Communication-rounds tradeoffs for common randomness and secret key generation.
  Mitali Bafna, Badih Ghazi, Noah Golowich, and Madhu Sudan.
  In SODA 2019. [conf] [slides]
• Machine learning for optimal economic design.
  Paul Dütting, Zhe Feng, Noah Golowich, Harikrishna Narasimhan, and David C. Parkes.
  In The Future of Economic Design, 2019. [journal]
• Deep learning for multi-facility location mechanism design.
  Noah Golowich, Harikrishna Narasimhan, and David C. Parkes.
  In IJCAI 2018. [conf]
• Size-independent sample complexity of neural networks.
  Noah Golowich, Alexander Rakhlin, and Ohad Shamir.
  In COLT 2018. [conf]
  In Information and Inference, 2019. [journal]
• Coloring chains for compression with uncertain priors.
  Noah Golowich.
  In The Electronic Journal of Combinatorics, 2018. [journal]
• The m-degenerate chromatic number of a digraph.
  Noah Golowich.
  In Discrete Mathematics, 2016. [journal]
• Acyclic subgraphs of planar digraphs.
  Noah Golowich and David Rolnick.
  In The Electronic Journal of Combinatorics, 2016. [journal]
• Resolving a conjecture on degree of regularity of linear homogeneous equations.
  Noah Golowich.
  In The Electronic Journal of Combinatorics, 2014. [journal]
• Degree of regularity of linear homogeneous equations.
  Kavish Gandhi, Noah Golowich, and László M. Lovász.
  In Journal of Combinatorics, 2014. [journal]