Books
 Introduction to Derivative Free Optimization with A. R. Conn and L. N. Vicente. Available from SIAM Series on Mathematical Programming. December 2008 / approx. xii + 277 pages / Softcover / ISBN: 9780898716680. The errata is available here
Papers

Linear interpolation gives better gradients than Gaussian smoothing in derivativefree optimization, with Albert S Berahas, Liyuan Cao, and Krzysztof Choromanski, 2019.
 A Theoretical and Empirical Comparison of Gradient Approximations in DerivativeFree Optimization, with Albert S Berahas, Liyuan Cao, and Krzysztof Choromanski, 2019.

Novel and Efficient Approximations for ZeroOne Loss of Linear Classifiers with Hiva Ghanbari, and Minhan Li, 2019.

A Stochastic Line Search Method with Convergence Rate Analysis, with Courtney Paquette, 2018.

Inexact SARAH Algorithm for Stochastic Optimization, with Lam M. Nguyen and Martin Takáč, 2018.

Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning, with Frank E. Curtis, Informs TutORials, pages 89–114, 2017.
 Stochastic Recursive Gradient Algorithm for Nonconvex Optimization, with L. Nguyen, Jie Liu and M. Takac, Technical Report, 2017.
 SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient, with L. Nguyen, Jie Liu and M. Takac, to appear in ICML 2017.
 Optimal Generalized Decision Trees via Integer Programming, with O. Gunluk, M. Menickelly and J. Kalagnanam, Technical Report, 2017.
 Blackbox Optimization in Machine Learning with TrustRegion Based Derivative Free Algorithms, with H. Ghanbari, Technical Report, 2017.
 Proximal QuasiNewton Methods for Convex Optimization, with H. Ghanbari, submitted, 2016
 Convergence Rate Analysis of a Stochastic Trust Region Method via Submartingales with Jose Blanchet, Coralia Cartis and Matt Menickelly, 2018.
 Stochastic Optimization Using a TrustRegion Method and Random Models, with R. Chen and M. Menickelly, Mathematical Programming, 2018.
 Global convergence rate analysis of unconstrained optimization methods based on probabilistic models, with C. Cartis, Mathematical Programming, 2018.
 Alternating direction methods for non convex optimization with applications to secondorder leastsquares and risk parity portfolio selection, with Xi Bai, 2015, technical report.
 Practical Inexact Proximal QuasiNewton Method with Global Complexity Analysis, with Xiaocheng Tang, Mathematical Programming, 2016, 160(12) pp 495–529
 Leastsquares approach to risk parity in portfolio selection, with X. Bai and R. Tutuncu, Quantitative Finance, 2016, 16(3), pp 357376.
 Aligning ligand binding cavities by optimizing superposed volume, with B. Chen and R. Chen, in BIBM 2012.
 Convergence of trustregion methods based on probabilistic models, with A. Bandeira and L.N. Vicente, SIOPT, 14(3), (2014), pp. 12381264.
 Fast firstorder methods for composite convex optimization with backtracking, with D. Goldfarb, FOCM, 2014, 14: 389417.
 Efficient Blockcoordinate Descent Algorithms for the Group Lasso. with Z. Qin, and D. Goldfarb. Math Prog. Comp., 2013, Volume 5, Issue 2, pp 143169.
 Computation of sparse low degree interpolating polynomials and their application to derivativefree optimization, with A. Bandeira and L.N. Vicente, Math. Prog., Series B, (2012), 134, pp 223257.
 On partially sparse recovery, with A. Bandeira and L.N. Vicente, 2011
 Fast alternating linearization methods for minimizing the sum of two convex functions, with D. Goldfarb and S. Ma, 2013, Math. Prog. Series A, 141: pp 349382.
 Sparse Inverse Covariance Selection via Alternating Linearization Methods, with D. Goldfarb and S. Ma, NIPS 2010
 SINCO – a greedy coordinate ascent method for sparse inverse covariance selection problem, with I Rish, 2009
 Optimization Methods for Sparse Inverse Covariance Selection Problem. with S. Ma, In S. Sra, S. Nowozin, and S. J. Wright editors: Optimization for Machine Learning, MIT Press, 2010
 Row by row method for semidefinite programming, with Z. Wen, D. Goldfarb and S. Ma, submitted, 2009
 A DerivativeFree Algorithm for the Leastsquare minimization, with H. Zhang and A.R. Conn, submitted, 2009
 Selfcorrecting geometry in modelbased algorithms for derivativefree unconstrained optimization with Ph. L. Toint, to appear, 2009.
 A MAP approach to learning sparse gaussian markov networks. with N. Bani Asadi, I. Rish, D. Kanevsky, B. Ramabhadran, ICCASP 2009.
 Global Convergence of General DerivativeFree TrustRegion Algorithms to First and Second Order Critical Points , with A.R. Conn and L.N. Vicente, SIAM J. on Optimization, (2009).
 Geometry of Sample Sets in Derivative Free Optimization: polynomial regression and incomplete interpolation., with A.R. Conn and L.N. Vicente, IMA Journal of Numerical Analysis 2008 28(4):721748.
 Geometry of Interpolation Sets in Derivative Free Optimization., with A.R. Conn and L.N. Vicente, Mathematical Programming, 111 (2008), 141172.
 ProductForm LDL^T Factorizations in InteriorPoint Methods for Convex Quadratic , with D. Goldfarb, IMA Journal of Numerical Analysis 2008 28(4):806826.
 IBM Research TRECVID2006 Video Retrieval System with M. Campbell, S. Ebadollahi, D. Joshi, M. Naphade, A. Natsev, J. Seidl, J. R. Smith, J. Tesic and L. Xie.
 Detecting Generic Visual Events with Temporal Cues. with Lexing Xie, Dong Xu, Shahram Ebadollahi, ShihFu Chang, John R. Smith. In Proc. 40th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October 2006.
 An Efficient Implementation of an Active Set Method for SVM, J. of Machine Learning Research 7 (2006) 22372257.
 (Conference version: Incas: An incremental active set method for SVM, with Shai Fine (2002) ).
 Productform Cholesky factorization in interior point methods for secondorder cone programming, with D. Goldfarb, Mathematical Programming, v. 103 (2005), pp. 153179.
 A productform Cholesky factorization method for handling dense columns in interior point methods for linear programming, with D. Goldfarb, Mathematical Programming, v. 99 (2004), pp. 134.
 Incremental learning and selective sampling via parametric optimization framework for SVM, with S. Fine, in “Advances in Neural Information Processing Systems” 14, MIT Press, (2002) 705711.
 Efficient SVM training using lowrank Kernel representations, with S. Fine, J. of Machine Learning Research, Special issue on Kernel methods, 2(2001) 243264.
 Manual for FORTRAN software package DFO v1.2, (2001).
 Parametric linear semidefinite programming, , In “Handbook on Semidefinite Programming”, eds. H. Wolkowicz, R. Saigal, L. Vandenberghe, pp 92–110. Kluwer, 2000.
 On parametric semidefinite programming, with D. Goldfarb, Applied Numerical Mathematics, v. 29(3), pp. 361377, 1999.
 Modified barrierpenalty functions for constrained minimization problems, with Goldfarb D., Polyak R. and Yusefovich B. Computational Optimization and Applications, v. 14(1), pp. 5574, 1999.
 A derivative free optimization algorithm in practice, with Conn A. R. and Toint Ph. L., Proceedings of 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, MO, 1998.
 Interior point trajectories in semidefinite programming, with D. Goldfarb, SIAM J. on Optimization v. 8(4), pp. 871886, 1998.
 Recent progress in unconstrained nonlinear optimization without derivatives, with A.R. Conn and Ph. L. Toint, Mathematical Programming v. 79 (1997) 397414.
 On the convergence of derivativefree methods for unconstrained optimization, with A.R. Conn and Ph. L. Toint, “Approximation Theory and Optimization: Tributes to M. J. D. Powell”, eds. A. Iserles and M. Buhmann, pp 83–108, 1997.
 Issues related to interior point methods for linear and semidefinite programming., PhD Thesis, Dept. of IEOR, Columbia University, 1997.
 Extension of Karmarkar’s algorithm to convex quadratically constrained quadratic , with A. Nemirovskii, Mathematical Programming, v. 72 (1996) 273289.