"Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization."

Kawaguchi, Kenji and Haihao Lu. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics Vol. 108, (2020): 669-679.

"Dual Mirror Descent for Online Allocation Problems."

Balseiro, Santiago, Haihao Lu, and Vahab Mirrokni. Proceedings of the 37th International Conference on Machine Learning Vol. 119, (2020): 613-628.

"Randomized Gradient Boosting Machine."

Lu, Haihao, and Rahul Mazumder. SIAM Journal on Optimization Vol. 30, No. 4 (2020): 2780-2808.

"'Relative Continuity' for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent."

Lu, Haihao. INFORMS Journal on Optimization Vol. 1, No. 4 (2019): 288-303.

"Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems."

Wang, Shuaiwen, Wenda Zhou, Arian Maleki, Haihao Lu, and Vahab Mirrokni, MIT Sloan Working Paper 7128-18. Cambridge, MA: MIT Sloan School of Management, October 2018. arXiv Preprint.

"New Computational Guarantees for Solving Convex Optimization Problems with First Order Methods, Via a Function Growth Condition Measure."

Freund, Robert M. and Haihao Lu. Mathematical Programming Vol. 170, No. 1-2 (2018): 445-477.