A. Stevens, R. Willett, A. Mamalakis, E. Foufoula-Georgiou, A. Tejedor, J. Randerson, P. Smyth, and S. J. Wright, “Graph-guided regularized regression of pacific ocean climate variables to increase predictive skill of southwestern us winter precipitation,” submitted, 2020.

D. Gilton, R. Luo, R. Willett, and G. Shakhnarovich, “Detection and description of change invisual streams,” submitted, 2020.

D. Wang, Y. Yu, A. Rinaldo, and R. Willett, “Localizing changes in high-dimensional vector autoregressive processes,” submitted, 2020.

Daren Wang, Kevin Lin, and Rebecca Willett, “Statistically and computationally efficient change point localization in regression settings,” arXiv preprint arXiv:1906.11364, 2019.

Fangzhou Mu, Yin Li, Yingyu Liang. Gradients as Features for Deep Representation Learning
International Conference on Learning Representations (ICLR), 2020.

Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu, and Adish Singla. Preference-based batch and sequential teaching: Towards a unified view of models. In Advances in Neural Information Processing Systems (NeurIPS), 2019.

G. Ongie, C. Metzler, A. Jalal, A. Dimakis, R. Baraniuk, and R. Willett, “Deep learning techniquesfor inverse problems in imaging,” submitted, 2020.

Greg Ongie, Rebecca Willett, Daniel Soudry, and Nathan Srebro, “A function space view of 
bounded norm infinite width relu nets: The multivariate case,” in ICLR, 2019, arXiv preprint

G. Ongie, D. Pimentel-Alarcon, L. Balzano, R. Nowak, and R. Willett, “Tensor methods for 
nonlinear matrix completion,” submitted, 2020.

Hui Yuan, Yingyu Liang. Learning Entangled Single-Sample Distributions via Iterative Trimming. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

L. Zheng, R. Willett, and G. Raskutti, “Context-dependent self-exciting point processes:models, methods, and risk bounds in high dimensions,” submitted, 2020.

M Karzand, RD Nowak. MaxiMin Active Learning in Overparameterized Model Classes IEEE Journal on Selected Areas in Information Theory.

M Karzand, RD Nowak. Maximin Active Learning with Data-Dependent Norms. 2019 57th Annual Allerton Conference on Communication, Control, and Computing.

ML Malloy, A Tripathy, RD Nowak. Optimal Confidence Regions for the Multinomial Parameter. arXiv preprint arXiv:2002.01044

R. M. Willett, “Response to “artificial intelligence—the revolution hasn’t happened yet”,” Harvard Data Science Review, 2019.

R Nowak, E Tánczos. Tighter Confidence Intervals for Rating Systems. arXiv preprint arXiv:1912.03528

R Parhi, RD Nowak. Minimum” Norm” Neural Networks are Splines arXiv preprint arXiv:1910.02333

Rungang Han, Rebecca Willett, and Anru Zhang, “An optimal statistical and computational framework for generalized tensor estimation,” arXiv preprint arXiv:2002.11255, 2020.

Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu. Teaching a black-box learner. In The 36th International Conference on Machine Learning (ICML), 2019.

W. J. Marais, R. E. Holz, J. S. Reid, and R. M. Willett, “Leveraging spatial textures, through 
machine learning, to identify aerosol and distinct cloud types from multispectral observations,”
submitted, 2020.

W. Wang, Q. Tang, and K. Livescu. Unsupervised pre-training of bidirectional speech encoders via masked reconstructionICASSP 2020.

Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, and Cho-Jui Hsieh. A unified framework for data poisoning attack to graph-based semi-supervised learning. In Advances in Neural Information Processing Systems (NeurIPS), 2019.

Y. Li, B. Mark, G. Raskutti, R.Willett, H. Song, and D. Neiman, “Graph-based regularization for regression problems with alignment and highly-correlated designs,” accepted to SIAM Journal on Mathematics of Data Science, arXiv:1803.07658 , 2020.

Yiding Chen and Xiaojin Zhu. Optimal attack against autoregressive models by manipulating the environment. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang, Xin Yang. Sketching Transformed Matrices with Applications to Natural Language Processing. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

Yuzhe Ma, Xuezhou Zhang, Wen Sun, and Xiaojin Zhu. Policy poisoning in batch reinforcement learning and control. In Advances in Neural Information Processing Systems (NeurIPS), 2019.

Zhongkai Sun, Prathusha Kameswara Sarma, William Sethares, Yingyu Liang. Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis. AAAI Conference on Artificial Intelligence (AAAI), 2020.

S Rajput, H Wang, Z Charles, D Papailiopoulos
. DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. 2019.