Yuan Li, Garvesh Raskutti, Rebecca Willett, Graph-based regularization for regression problems with highly-correlated designs, https://arxiv.org/abs/1803.07658, March, 2018, submitted.
Clément W. Royer, Michael O’Neill, Stephen J. Wright, A Newton-CG Algorithm with Complexity Guarantees for Smooth Unconstrained Optimization, https://arxiv.org/abs/1803.02924, March, 2018. Submitted.
Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Network Estimation from Point Process Data, https://arxiv.org/abs/1802.04838, February 2018, submitted to IEEE Transactions on Information Theory.
Xiaomin Zhang, Xuezhou Zhang, Xiaojin Zhu, Po-Ling Loh, Theoretical support of machine learning debugging via weighted M-estimation, January 2018, submitted.
Zachary Charles, Amin Jalali, Rebecca Willett, Subspace Clustering with Missing and Corrupted Data, January 2018, https://arxiv.org/abs/1707.02461
Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright, Training set debugging using trusted items, January, 2018, https://arxiv.org/abs/1801.08019 , AAAI 2018.
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett, Online Learning for Changing Environments using Coin Betting November, 2017, https://arxiv.org/abs/1711.02545 . To appear in Online Journal of Statistics.
Royer, C. and Wright, S. J., Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization, November, 2017. https://arxiv.org/abs/1706.03131 SIAM Journal on Optimization.
O’Neill, M. and Wright, S. J., Behavior of accelerated gradient methods near critical points of nonconvex functions, https://arxiv.org/abs/1706.07993, June 2017 submitted.