1. Dillon T. Fitch, James Sharpnack, and Susan L. Handy. Psychological stress of bicycling with traffic: examining heart rate variability of bicyclists in natural urban environments. Transportation Research Part F: Traffic Psychology and Behaviour, 70:81 – 97, 2020. (doi:https://doi.org/10.1016/j.trf.2020.02.015)
  2. Oscar Hernan Madrid Padilla, James Sharpnack, Yanzhen Chen, and Daniela M Witten. Adaptive nonparametric regression with the k -nearest neighbour fused lasso. Biometrika, 2020.
  3. Kirill Paramonov, Dmitry Shemetov, and James Sharpnack. Estimating graphlet statistics via lifting. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 587–595. ACM, 2019.
  4. Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James L Sharpnack. Stochastic shared embeddings: Data-driven regularization of embedding layers. In Advances in Neural Information Processing Systems, pages 24–34, 2019.
  5. Robert Bassett and James Sharpnack. Fused density estimation: Theory and methods. Journal of the Royal Statistical Society Series B, 81(5):839–860, November 2019.
  6. James Sharpnack. Learning patterns for detection with multiscale scan statistics. In Proceedings of Machine Learning Research (31st Annual Conference on Learning Theory), volume 75, 2018.
  7. Michael F Sharpnack, Nilini Ranbaduge, Arunima Srivastava, Ferdinando Cerciello, Simona G Codreanu, Daniel C Liebler, Celine Mascaux, Wayne O Miles, Robert Morris, Jason E McDermott, James Sharpnack, and others. Proteogenomic analysis of surgically resected lung adenocarcinoma. Journal of Thoracic Oncology, 2018.
  8. Liwei Wu, Cho-Jui Hsieh, and James Sharpnack. Sql-rank: A listwise approach to collaborative ranking. In Proceedings of Machine Learning Research (35th International Conference on Machine Learning), volume 80, 2018.
  9. Xiaoyue Li and James Sharpnack. Compression of spatio-temporal networks via point-to-point process models. In Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG), 2017.
  10. Kevin Lin, James L Sharpnack, Alessandro Rinaldo, and Ryan J Tibshirani. A sharp error analysis for the fused lasso, with application to approximate changepoint screening. In Advances in Neural Information Processing Systems, pages 6887–6896, 2017.
  11. Oscar Hernan Madrid Padilla, James G Scott, James Sharpnack, and Ryan J Tibshirani. The dfs fused lasso: Linear-time denoising over general graphs. The Journal of Machine Learning Research, 18(1):6410–6445, 2017.
  12. Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L Sharpnack, and Ryan J Tibshirani. Higher-order total variation classes on grids: Minimax theory and trend filtering methods. In Advances in Neural Information Processing Systems, pages 5802–5812, 2017.
  13. Liwei Wu, Cho-Jui Hsieh, and James Sharpnack. Large-scale collaborative ranking in near-linear time. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 515–524. ACM, 2017.
  14. James Sharpnack, Ery Arias-Castro, and others. Exact asymptotics for the scan statistic and fast alternatives. Electronic Journal of Statistics, 10(2):2641–2684, 2016.
  15. James Sharpnack, Alessandro Rinaldo, and Aarti Singh. Detecting anomalous activity on networks with the graph fourier scan statistic. Signal Processing, IEEE Transactions on, 64(2):364–379, 2016.
  16. Yu-Xiang Wang, James Sharpnack, Alexander J Smola, and Ryan J Tibshirani. Trend filtering on graphs. The Journal of Machine Learning Research, 17(1):3651–3691, 2016.
  17. Akshay Krishnamuthy, James Sharpnack, and Aarti Singh. Recovering graph-structured activations using adaptive compressive measurements. In Signals, Systems and Computers, 2013 Asilomar Conference on, pages 765–769. IEEE, 2013.
  18. James Sharpnack. A path algorithm for localizing anomalous activity in graphs. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, pages 341–344. IEEE, 2013.
  19. James Sharpnack and Aarti Singh. Near-optimal and computationally efficient detectors for weak and sparse graph-structured patterns. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, pages 443–446. IEEE, 2013.
  20. James Sharpnack, Akshay Krishnamurthy, and Aarti Singh. Detecting activations over graphs using spanning tree wavelet bases. International Conference on Artificial Intelligence and Statistics, JMLR W&CPJournal of, 31:536–544, 2013.
  21. James L Sharpnack, Akshay Krishnamurthy, and Aarti Singh. Near-optimal anomaly detection in graphs using lovász extended scan statistic. In Advances in Neural Information Processing Systems, pages 1959–1967, 2013.
  22. Mladen Kolar and James Sharpnack. Variance function estimation in high-dimensions. International Conference of Machine Learning, 12:1447–1454, 2012.
  23. James Sharpnack, Alessandro Rinaldo, and Aarti Singh. Changepoint detection over graphs with the spectral scan statistic. International Conference on Artificial Intelligence and Statistics, JMLR W&CP, 31:545–553, 2012.
  24. James Sharpnack, Alessandro Rinaldo, and Aarti Singh. Sparsistency of the edge lasso over graphs. International Conference on Artificial Intelligence and Statistics, JMLR W&CP, 22:1028–1036, 2012.
  25. James Sharpnack and Aarti Singh. Identifying graph-structured activation patterns in networks. In Advances in Neural Information Processing Systems, pages 2137–2145, 2010.