Vitaly Feldman

I'm a research scientist at Google Brain.

I work on theoretical aspects of machine learning. Recent topics include adaptive data analysis, complexity of learning with constrained access to data, and privacy-preserving learning. I also worked on understanding of natural learning systems: learning by the brain and evolution as learning.

Academic activities

I serve as a director and treasurer on the steering committee of the Association for Computational Learning

Recent program committees:
Recent/selected works
  1. Learning without Interaction Requires Separation.
    With Amit Daniely. 2018.
  2. Amplification by Shuffling: From Local to Central Differential Privacy.
    With Ulfar Erlingsson, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta. SODA 2019.
  3. Generalization Bounds for Uniformly Stable Algorithms.
    With Jan Vondrak. NIPS 2018 (spotlight).
  4. Privacy Amplification by Iteration.
    With Ilya Mironov, Kunal Talwar and Abhradeep Thakurta. FOCS 2018 .
  5. Privacy-preserving Prediction.
    With Cynthia Dwork. COLT 2018 .
  6. The Everlasting Database: Statistical Validity at a Fair Price.
    With Blake Woodworth, Saharon Rosset and Nathan Srebro. NIPS 2018.
  7. Calibrating Noise to Variance in Adaptive Data Analysis.
    With Thomas Steinke. COLT 2018 .
  8. Generalization for Adaptively-chosen Estimators via Stable Median.
    With Thomas Steinke. COLT 2017 .
  9. A General Characterization of the Statistical Query Complexity.
    COLT 2017 .
  10. Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back.
    NIPS 2016 (oral presentation).
  11. Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization.
    With Cristobal Guzman and Santosh Vempala. SODA 2017.
  12. The reusable holdout: Preserving validity in adaptive data analysis.
    With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. Science, 2015.
    IBM Research 2015 Best Paper Award.
    Based on STOC and NIPS papers below. See also my post on this work at IBM Research blog (republished by KDnuggets).
  13. Generalization in Adaptive Data Analysis and Holdout Reuse.
    With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. NIPS, 2015.
  14. Preserving Statistical Validity in Adaptive Data Analysis.
    With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. STOC 2015.
    Invited to SICOMP special issue on STOC
  15. Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS Functions.
    With Jan Vondrak. FOCS 2015 .
  16. On the Complexity of Random Satisfiability Problems with Planted Solutions .
    With Will Perkins and Santosh Vempala. STOC 2015 .
  17. Sample Complexity Bounds on Differentially Private Learning via Communication Complexity .
    With David Xiao. COLT 2014, SICOMP 2015.
  18. Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy.
    With Nina Balcan. NIPS 2013 . Algorithmica. Special Issue on New Theoretical Challenges in Machine Learning
  19. Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas.
    With Jan Vondrak. FOCS 2013 . SICOMP 2016, Special issue on FOCS
    IBM Research 2016 Best Paper Award.
  20. Learning using Local Membership Queries.
    With Pranjal Awasthi and Varun Kanade. COLT 2013, Best Student (co-authored) Paper Award
  21. Statistical Algorithms and a Lower Bound for Detecting Planted Cliques.
    With Elena Grigorescu, Lev Reyzin, Santosh Vempala and Ying Xiao. STOC 2013 . JACM 2017 .
  22. Nearly Optimal Solutions for the Chow Parameters Problem and Low-weight Approximation of Halfspaces.
    With Anindya De, Ilias Diakonikolas and Rocco Servedio. STOC 2012; JACM 2014 .
    IBM Research 2014 Best Paper Award.
  23. Distribution-Specific Agnostic Boosting.
    ITCS (formerly ICS) 2010.
  24. A Complete Characterization of Statistical Query Learning with Applications to Evolvability.
    FOCS 2009; JCSS 2012 (Special issue on Learning Theory).
  25. Experience-Induced Neural Circuits That Achieve High Capacity..
    With Leslie Valiant. Neural Computation 21:10, 2009.
  26. New Results for Learning Noisy Parities and Halfspaces.
    With Parikshit Gopalan, Subhash Khot, and Ashok Ponnuswami. FOCS 2006; SICOMP 2009, Special issue on FOCS
  27. Hardness of Approximate Two-level Logic Minimization and PAC Learning with Membership Queries.
    STOC 2006; JCSS 75(1), 2009 (Special issue on Learning Theory)
  28. Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions.
    COLT 2005, Best Student Paper Award; JMLR 2007, Special issue on COLT
  29. The Complexity of Properly Learning Simple Concept Classes.
    With Misha Alekhnovich, Mark Braverman, Adam Klivans, and Toni Pitassi.
    FOCS 2004; JCSS 74(1), 2008 (Special issue on Learning Theory)

Invited articles and surveys:

  1. Guilt-Free Data Reuse (copy).
    With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. Communications of the ACM (Research Highlights), 2017
  2. Hardness of Proper Learning.
    The Encyclopedia of Algorithms. Springer-Verlag, 2015 (2nd ed.) and 2008 (1st ed.)
  3. Statistical Query Learning.
    The Encyclopedia of Algorithms. Springer-Verlag, 2015 (2nd ed.) and 2008 (1st ed.)
  4. Structure and Learning of Valuation Functions
    With Jan Vondrak. ACM SIGecom Exchanges 12.2
Slides for some recent talks
  • Amplification by Shuffling: From Local to Central Differential Privacy. Privacy-preserving Machine Learning workshop (Dec 2018):slides.
  • Privacy-preserving prediction. COLT 2018, Privacy in Graphs workshop (Nov 2018):slides.
  • Generalization bounds for uniformly stable algorithms. Robust and High-Dimensional Statistics workshop (Oct 2018):slides, video. NIPS spotlight video.
  • Stability, Information and Generalization in Adaptive Data Analysis. Google NYC/Princeton/Penn (Apr. 2018): slides.
  • Dealing with Range Anxiety in Mean Estimation. ALT 2017 (Nov 2017): slides.
  • A General Characterization of the Statistical Query Complexity. COLT 2017 (July 2017); NYU (Feb. 2018): slides.
  • Understanding Generalization in Adaptive Data Analysis Computational Challenges in Machine Learning workshop, EPFL, and Bertinoro (2017):slides, video.
  • On the power of learning from k-wise queries. ITCS 2017: slides, video.
  • Lower bounds against convex relaxations via the statistical query complexity. Caltech/UCLA/Stanford/Harvard/MIT, 2017: slides (with some comments in the notes).
  • Generalization of ERM in stochastic convex optimization. NIPS 2016: slides and video
  • Generalization and adaptivity in stochastic convex optimization. TOCA-SV 2016: slides (with some comments in the notes).
  • Generalization in Adaptive Data Analysis via Max-Information. Simons Institute workshop on Information Theory, 2016: slides.
  • Preserving Validity in Adaptive Data Analysis. National Academy of Engineering, 2016: slides.
  • Adaptive Data Analysis without Overfitting. Workshop on Learning. NUS, 2015: slides.
  • Preserving statistical validity in adaptive data analysis. STOC 2015: slides.
  • Approximate resilience, monotonicity, and the complexity of agnostic learning. SODA 2015: slides.
  • Sample complexity bounds on differentially private learning via communication complexity. COLT 2014; ITA 2015: slides.
  • Using data privacy for better adaptive predictions. Foundations of Learning Theory workshop @ COLT 2014 : slides.
  • On the power and the limits of evolvability. Simons Institute workshop on Computational Theories of Evolution, 2014: slides.
  • Optimal bounds on approximation of submodular and XOS functions by juntas. Simons Institute workshop on Real Analysis at @FOCS 2013 : slides.

Contact: firstname.edu@gmail.com