IBM Research


Submitted papers:

  1. Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management, with Mingli Chen and Victor Chernozhukov (pdf)
  2. Pivotal Estimation via Self-Normalization for High Dimensional Linear Models with Error-in-variables, with Victor Chernozhukov, Abhishek Kaul, Alexandre Tsybakov, Mathieu Rosenbaum (pdf)
  3. Confidence Bands for Coefficients in High Dimensional Linear Models with Error-in-variables, with Victor Chernozhukov and Abhishek Kaul (pdf)
  4. Computation of Optimal Dynamic Mechanism with Participation Requirements, with B. Chen and P. Sun
  5. Conditional Quantile Processes based on Series or Many Regressors, with Victor Chernozhukov, Denis Chetverikov and Ivan Fernandez-Val (pdf)
  6. Lasso Methods for Gaussian Instrumental Variables Models, with Victor Chernozhukov and Chris Hansen (pdf)
Published papers:
  1. Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models, with V. Chernozhukov and K. Kato (accepted at JASA, pdf)
  2. Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework, with Victor Chernozhukov, Denis Chetverikov, and Ying Wei (accepted at The Annals of Statistics, pdf)
  3. Mechanism and Network Design with Private Negative Externalities, with C. Deng and S. Pekec (accepted at Operations Research, 2016)
  4. An R Package for Performing Nonparametric Series Quantile Regression, with Victor Chernozhukov, Ivan Fernandez-Val, Michael Lipsitz (accepted at R Journal, 2016)
  5. Resource Allocation Under Demand Uncertainty and Private Information, with Giuseppe Lopomo and Shouqiang Wang (accepted at Management Science, 2016)
  6. Linear and Conic Programming Approaches to High-Dimensional Errors-in-variables Models, with M. Rosenbaum and A. Tsybakov (accepted at Journal of the Royal Statistical Society: Series B, 2016, matlab code)
  7. Approximate group context tree, with Roberto I. Oliveira (pdf, accepted at The Annals of Statistics, 2016)
  8. Program Evaluation and Causal Inference with High-Dimensional Data, with Victor Chernozhukov, Ivan Fernandez-Val and Chris Hansen (pdf, accepted at Econometrica 2016)
  9. Post-Selection Inference for Generalized Linear Models with Many Controls, with Victor Chernozhukov and Ying Wei (pdf, accepted at Journal of Business & Economic Statistics 2016)
  10. An {L1, L2, Linf}-Approach to High-Dimensional Errors-in-variables Models, with Mathieu Rosenbaum and Alexandre B. Tsybakov (pdf, Electronic Journal of Statistics 2016, Vol. 10, No. 2, 1729-1750)
  11. Inference in High Dimensional Panel Models with an Application to Gun Control, with Victor Chernozhukov, Christian Hansen, and Damian Kozbur (pdf, accepted at Journal of Business & Economic Statistics, 2015)
  12. Comments on An Adaptive Resampling Test for Detecting the Presence of Significant Predictors, with Victor Chernozhukov (accepted at Journal of American Statistical Association, 2015)
  13. On the Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results, with Victor Chernozhukov, Denis Chetverikov and Kengo Kato (pdf, accepted at Journal of Econometrics, 2015)
  14. Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions, Tengyuan Liang, Hariharan Narayanan and Alexander Rakhlin (pdf, COLT 2015, pp. 240-265)
  15. Uniform Post Selection Inference for LAD Regression Models and Other Z-estimators, with Victor Chernozhukov and Kengo Kato (pdf, Biometrika 102, 77-94, 2015, arXiv:1304.0282)
  16. Pivotal Estimation via Square-root Lasso in Nonparametric Regression, with Victor Chernozhukov and Lie Wang (pdf, The Annals of Statistics 42 (2), 757-788, 2014, arXiv:1105.1475)
  17. High-Dimensional Methods and Inference on Treatment and Structural Effects in Economics, with Alexandre Belloni and Chris Hansen (The Journal of Economic Perspectives 28 (2), 29-50, 2014)
  18. Posterior Inference in Curved Exponential Families under Increasing Dimensions, with Victor Chernozhukov (pdf, The Econometrics Journal 17, no. 2, S75-S100, 2014)
  19. Inference on Treatment Effects After Selection Amongst High-Dimensional Controls, with Victor Chernozhukov and Christian Hansen (pdf, supplementary material, The Review of Economic Studies 81 (2), 608-650, 2014, arXiv:1201.0224)
  20. Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain, with Daniel Chen, Victor Chernozhukov, and Chris Hansen (pdf, Econometrica, 2012, 80(6), 2369-2430)
  21. Inference Methods for High-Dimensional Sparse Econometric Models, with Victor Chernozhukov and Chris Hansen (Advances in Economics and Econometrics, 10th World Congress of Econometric Society, Volume III, Econometrics, Edited by Daron Acemoglu, Manuel Arellano and Eddie Dekel, 245--295.)
  22. Least Squares After Model Selection in High-dimensional Sparse Models, with Victor Chernozhukov (Bernoulli Volume 19, Number 2 (2013), 521-547, pdf, Former title: Post-L1-Penalized Estimators in High-Dimensional Linear Regression Models.)
  23. Optimal Admission and Scholarship Decisions: Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers, with William Boulding, Richard Staelin, and Mitchell Lovett (pdf, Marketing Science July/August 2012 31:621-636)
  24. Square-root lasso: Pivotal Recovery of Sparse Signals via Conic Programming with Victor Chernozhukov and Lie Wang (Biometrika (2011) 98 (4): 791-806, pdf)
  25. On multivariate quantiles under partial ordering, with Robert L. Winkler (The Annals of Statistics, Volume 39, Number 2, 2011, 1125-1179, pdf).
  26. L1-Penalized Quantile Regression in High-Dimensional Sparse Models, with Victor Chernozhukov (The Annals of Statistics, Volume 39, Number 1, 2011, 82-130, pdf, R code for Monte Carlo, simple MatLab implementation that requires SDPT3).
  27. High-Dimensional Sparse Econometric Models, an Introduction, with Victor Chernozhukov, Inverse Problems and High-Dimensional Estimation, Springer Lecture Notes in Statistics, 2011, pp. 121-156, pdf (Matlab Code).
  28. Multi-dimensional Mechanism Design: Finite Dimensional Approximations and Efficient Computation, with Giuseppe Lopomo and Shouqiang Wang (Operations Research, Volume 58 Issue 4-Part-2, 2010, pp. 1079-1089, pdf).
  29. An Efficient Re-scaled Perceptron Algorithm for Conic Systems, with Robert M. Freund and Santosh Vempala (Mathematics of Operations Research, Volume 34 Issue 3, 2009, pp. 621-641, pdf).
  30. On the Computational Complexity of Monte Carlo Markov Chain Based Estimators in Large Samples, with Victor Chernozhukov (The Annals of Statistics, Vol. 37, No. 4, 2009, pp. 2011-2055, pdf).
  31. On the Behrens-Fisher problem: a globally convergent algorithm and a finite-sample study of the Wald, LR and LM tests, with Gustavo Didier (The Annals of Statistics, 2008, Vol. 36, No. 5, pp. 2377-2408, pdf).
  32. Optimal Product Line Design: Efficient Methods and Comparisons, with Robert M. Freund, Matthew Selove, and Duncan Simester (Management Science Vol. 54, No. 9, September 2008, pp. 1544-1552, pdf).
  33. On the Second-Order Feasibility Cone: Primal-Dual Representation and Efficient Projection, with Robert M. Freund. (SIAM Journal on Optimization, Volume 19, Issue 3, pp. 1073-1092, 2008, pdf).
  34. Dynamic Bundle Methods, with Claudia Sagastizabal (Mathematical Programming, Volume 120, Number 2 / September, 2009, pp. 289-311, pdf).
  35. Norm-Induced Densities and Testing the Boundedness of a Convex Set (Mathematics of Operation Research, Vol. 33, No. 1, February 2008, pp. 235-256, pdf).
  36. A geometric analysis of Renegar`s condition number, and its interplay with conic curvature, with Robert M. Freund (Mathematical Programming, Volume 119, Issue 1, 2009, pp. 95-107, pdf).
  37. Projective Re-normalization for Improving the Behavior of a Homogeneous Conic Convex System, with Robert M. Freund (Mathematical Programming 118, pp. 279-299, 2009, pdf).
  38. On the Symmetry Function of a Convex Set, with Robert M. Freund (Mathematical Programming, Issue: Volume 111, Numbers 1-2 / January, 2008, Pages 57-93, pdf).
  39. Lagrangian Based Heuristic for the Linear Ordering Problem, with Abilio Lucena (pdf). Metaheuristics: Computer Decision-Making, Kluwer Academic Publishers, 2004, pp. 37-64.
  40. Bundle Relaxation and Primal Recovery of Unit Commitment Problems. The Brazilian Case, with Andre Diniz, Maria E. P. Maceira and Claudia Sagastizabal. Annals of Operation Research, 120, pp. 21-44, 2003 (pdf)
Other publications:
  1. Uncertainty Aversion applied to the Electrical Power, with Aloisio Araujo, PSR Technical Report 2011.
  2. Studies Integrating Geometry, Probability, and Optimization under Convexity, PhD Thesis, Massachusetts Institute of Technology, 2006.
  3. Introduction to Bundle Methods, IAP Lecture Notes 2005 (pdf).

Additional information  

    My CV is here

    Some papers are available via ArXiv

        Publications TeachingProfessional ExperienceConferences Links