Before joining IBM in Aug 2007 I spent 5 very enjoyable years at Harvard University as a PhD student advised by Leslie Valiant and as a postdoc. Previously I studied at the Technion from which I received BA and MSc in CS (advised by Nader Bshouty) and worked at IBM Research in Haifa.
My research interests are primarily in computational and statistical aspects of Machine Learning Theory. Recent work focuses on developing foundations for adaptive data analysis, complexity of learning via statistical queries, and privacy-preserving learning. I also work on understanding of natural learning systems: learning by the brain and evolution as learning.
- Co-organizer of Data Privacy: Foundations and Applications program at Simons Institute for the Theory of Computing, UC Berkeley. Jan-May 2019 (with Katrina Ligett, Kobbi Nissim and Adam Smith).
- Jan-May 2018: visiting scientist at Simons Institute. Brain and Computation program.
- July 2017: visiting Centrum Wiskunde & Informatica (CWI), Amsterdam (hosted by Peter Grunwald).
- May 2017: Co-organizer of Data Privacy: Planning Workshop at Simons Institute.
- March-May 2017: visiting scientist at Simons Institute. Foundations of Machine Learning program.
- Dec 2016: Co-organizer of workshop on Adaptive Data Analysis (WADAPT) at NIPS 2016 (with Aaditya Ramdas, Aaron Roth and Adam Smith).
- June 2016: invited speaker at the National Academy of Engineering Frontiers of Engineering Symposium.
- Dec 2015: Co-organizer of workshop on Adaptive Data Analysis (WADAPT) at NIPS 2015 (with Moritz Hardt, Aaron Roth and Adam Smith).
- July 2015: instructor at Berkeley summer course in mining and modeling of neuroscience data (with Christos Papadimitriou).
- March-May 2015: visiting scientist at Simons Institute. Information Theory program.
Recent/upcoming conference program committees:
- COLT 2016: Co-chair (with Sasha Rakhlin).
- COLT 2018, STOC 2018, NIPS 2017 , AISTATS 2017, NIPS 2016, IJCAI-ML 2015, ALT 2015, COLT 2015, ITCS 2015 , NIPS 2014, ALT 2014, COLT 2014 (publication chair).
Slides for some recent talks:
- A General Characterization of the Statistical Query Complexity. @COLT 2017 (July 2017): slides.
- Understanding Generalization in Adaptive Data Analysis @Computational Challenges in Machine Learning workshop and @EPFL (May/June, 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 and 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.
Selected works (biased toward recent). Full list can be found here.
- Generalization for Adaptively-chosen Estimators via Stable Median.
With Thomas Steinke. COLT 2017 .
- Guilt-Free Data Reuse.
With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. CACM Research Highlights, 2017.
A high level overview of STOC 2015 and NIPS 2015 papers below.
- A General Characterization of the Statistical Query Complexity.
COLT 2017 .
- On the Power of Learning from k-Wise Queries.
With Badih Ghazi. ITCS 2017 .
- Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back.
NIPS 2016 (oral presentation).
- Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization.
With Cristobal Guzman and Santosh Vempala. SODA 2017.
- 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 Pat Goldberg Memorial Best Paper Award.
Based on STOC and NIPS papers below. See also my post on this work at IBM Research blog (republished by KDnuggets).
- Generalization in Adaptive Data Analysis and Holdout Reuse.
With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. NIPS, 2015.
- 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
- Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's .
With Will Perkins and Santosh Vempala. NIPS 2015.
- Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS Functions.
With Jan Vondrak. FOCS 2015 .
- On the Complexity of Random Satisfiability Problems with Planted Solutions .
With Will Perkins and Santosh Vempala. STOC 2015 .
- Sample Complexity Bounds on Differentially Private Learning via Communication Complexity .
With David Xiao. COLT 2014, SICOMP 2015.
- 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
- Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas.
With Jan Vondrak. FOCS 2013 . SICOMP 2016, Special issue on FOCS
- Learning using Local Membership Queries.
With Pranjal Awasthi and Varun Kanade. COLT 2013, Best Student (co-authored) Paper Award
- Statistical Algorithms and a Lower Bound for Detecting Planted Cliques.
With Elena Grigorescu, Lev Reyzin, Santosh Vempala and Ying Xiao. STOC 2013 . JACM 2017 .
- Learning DNF Expressions from Fourier Spectrum.
- 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 Pat Goldberg Memorial Best Paper Award.
- Distribution-Specific Agnostic Boosting.
ITCS (formerly ICS) 2010.
- A Complete Characterization of Statistical Query Learning with Applications to Evolvability.
FOCS 2009; JCSS 2012 (Special issue on Learning Theory).
- Experience-Induced Neural Circuits That Achieve High Capacity..
With Leslie Valiant. Neural Computation 21:10, 2009.
- New Results for Learning Noisy Parities and Halfspaces.
With Parikshit Gopalan, Subhash Khot, and Ashok Ponnuswami. FOCS 2006; SICOMP 2009, Special issue on FOCS
- Hardness of Approximate Two-level Logic Minimization and PAC Learning with Membership Queries.
STOC 2006; JCSS 75(1), 2009 (Special issue on Learning Theory)
- Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions.
COLT 2005, Best Student Paper Award; JMLR 2007, Special issue on COLT
- 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:
- Guilt Free Data Reuse.
With Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth. Communications of the ACM (Research Highlights), 2017
- Hardness of Proper Learning.
The Encyclopedia of Algorithms. Springer-Verlag, 2015 (2nd ed.) and 2008 (1st ed.)
- Statistical Query Learning.
The Encyclopedia of Algorithms. Springer-Verlag, 2015 (2nd ed.) and 2008 (1st ed.)
- Structure and Learning of Valuation Functions
With Jan Vondrak. ACM SIGecom Exchanges 12.2
Ph.D. thesis: Efficiency and Computational Limitations of Learning Algorithms. Harvard University. January 2007
A bit more about me