Please find links to all of my publications below. The up-to-date list is maintained on my webpage.

Publications

  1. Generalization Bounds in the Predict-then-Optimize Framework, with Othman El Balghiti, Adam N. Elmachtoub, and Ambuj Tewari, Mathematics of Operations Research, forthcoming.

  2. Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques, with Emily B. Franklin, Lindsay D. Yee, Bernard Aumont, Robert J. Weber, and Allen H. Goldstein, Atmospheric Measurement Techniques 15 (12), pp. 3779-3803, 2022.

  3. Smart “Predict, then Optimize,” with Adam N. Elmachtoub, Management Science 68 (1), pp. 9-26, 2022. [code]

  4. Risk Bounds and Calibration for a Smart Predict-then-Optimize Method, with Heyuan Liu, Advances in Neural Information Processing Systems (NeurIPS) 34, 2021.

  5. Joint Online Learning and Decision-making via Dual Mirror Descent, with Alfonso Lobos and Zheng Wen, Proceedings of the 38th International Conference on Machine Learning (ICML) PMLR 139:7080-7089, 2021.

  6. Generalization Bounds in the Predict-then-Optimize Framework, with Othman El Balghiti, Adam N. Elmachtoub, and Ambuj Tewari, Advances in Neural Information Processing Systems (NeurIPS) 32, pp. 14389-14398, 2019.

  7. A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives, with Robert M. Freund and Rahul Mazumder, The Annals of Statistics 45 (6), pp. 2328-2364, 2017.

  8. An Extended Frank-Wolfe Method with “In-Face” Directions, and its Application to Low-Rank Matrix Completion, with Robert M. Freund and Rahul Mazumder, SIAM Journal on Optimization 27 (1), pp. 319-346, 2017. [code]

  9. New Analysis and Results for the Frank-Wolfe Method, with Robert M. Freund, Mathematical Programming 155 (1), pp. 199-230, 2016.

Refereed Workshop Proceedings

  1. Profit Maximization for Online Advertising Demand-Side Platforms, with Alfonso Lobos, Zheng Wen, and Kuang-chih Lee, Proc. AdKDD & TargetAd Workshop at KDD 2017, Halifax, Canada.

  2. Incremental Forward Stagewise Regression: Computational Complexity and Connections to LASSO, with Robert M. Freund and Rahul Mazumder, Proc. International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS), Leuven, Belgium, 2013.

Working Papers

  1. Binary Classification with Instance and Label Dependent Label Noise, with Hyungki Im.

  2. Stochastic First-Order Algorithms for Constrained Distributionally Robust Optimization, with Hyungki Im. [code]

  3. On the Softplus Penalty for Constrained Convex Optimization, with Meng Li and Alper Atamturk.

  4. Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach, with Mo Liu, Heyuan Liu, and Zuo-Jun Max Shen.

  5. Online Contextual Decision-Making with a Smart Predict-then-Optimize Method, with Heyuan Liu.

  6. New Methods for Parametric Optimization via Differential Equations, with Heyuan Liu.

  7. New Penalized Stochastic Gradient Methods for Linearly Constrained Strongly Convex Optimization, with Meng Li and Alper Atamturk.

  8. Integrated Conditional Estimation-Optimization, with Meng Qi and Zuo-Jun Max Shen.

  9. Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform With Generic Auctions, with Alfonso Lobos, Zheng Wen, and Kuang-chih Lee.

Technical Reports

  1. Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training, with Alfonso Lobos and Nathan Vermeersch, 2019.

  2. Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods, with Robert M. Freund and Rahul Mazumder, 2018.

  3. AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods, with Robert M. Freund and Rahul Mazumder, MIT Operations Research Center working paper OR 397-14, 2013.