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ORCIDJames Sharpnack
Person information
- affiliation: University of California, Davis
- affiliation (former): University of California, San Diego, Department of Mathematics
- affiliation (former): Carnegie Mellon University, Machine Learning Department
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2020 – today
- 2024
[i19]Xiawei Wang, James Sharpnack, Thomas C. M. Lee:
Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging. CoRR abs/2408.09367 (2024)- [i18]James Sharpnack, Phoebe Mulcaire, Klinton Bicknell, Geoffrey T. LaFlair, Kevin P. Yancey:
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning. CoRR abs/2409.08823 (2024) - [i17]Yong-Siang Shih, Zach Zhao, Chenhao Niu, Bruce Iberg, James Sharpnack, Mirza Basim Baig:
AI-assisted Gaze Detection for Proctoring Online Exams. CoRR abs/2409.16923 (2024) - 2023
- [j4]James Sharpnack
:
On L2-Consistency of Nearest Neighbor Matching. IEEE Trans. Inf. Theory 69(6): 3978-3988 (2023) - [c25]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML 2023: 10879-10928 - [i16]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR abs/2302.03020 (2023) - 2022
- [c24]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. AISTATS 2022: 7111-7123 - [c23]Stephen Sheng, Keerthi Vasan G. C, Chi Po P. Choi, James Sharpnack, Tucker Jones:
An Unsupervised Hunt for Gravitational Lenses. AISTATS 2022: 9827-9843 - [c22]Nick Erickson, Xingjian Shi, James Sharpnack, Alexander J. Smola:
Multimodal AutoML for Image, Text and Tabular Data. KDD 2022: 4786-4787 - [c21]Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. NeurIPS 2022 - 2021
- [c20]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. AISTATS 2021: 1585-1593 - [i15]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. CoRR abs/2106.02978 (2021) - [i14]Qin Ding, Yi-Wei Liu, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. CoRR abs/2106.02979 (2021) - 2020
- [c19]Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. AISTATS 2020: 776-787 - [c18]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
SSE-PT: Sequential Recommendation Via Personalized Transformer. RecSys 2020: 328-337 - [i13]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Multiscale Non-stationary Stochastic Bandits. CoRR abs/2002.05289 (2020) - [i12]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. CoRR abs/2006.04012 (2020)
2010 – 2019
- 2019
- [c17]Kirill Paramonov, Dmitry Shemetov, James Sharpnack:
Estimating Graphlet Statistics via Lifting. KDD 2019: 587-595 - [c16]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. NeurIPS 2019: 24-34 - [i11]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. CoRR abs/1905.10630 (2019) - [i10]Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. CoRR abs/1905.12217 (2019) - [i9]Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack:
Temporal Collaborative Ranking Via Personalized Transformer. CoRR abs/1908.05435 (2019) - [i8]Weitang Liu, Lifeng Wei, James Sharpnack, John D. Owens:
Unsupervised Object Segmentation with Explicit Localization Module. CoRR abs/1911.09228 (2019) - 2018
- [c15]James Sharpnack:
Learning Patterns for Detection with Multiscale Scan Statistics. COLT 2018: 950-969 - [c14]Liwei Wu, Cho-Jui Hsieh, James Sharpnack:
SQL-Rank: A Listwise Approach to Collaborative Ranking. ICML 2018: 5311-5320 - [i7]James Sharpnack:
Learning Patterns for Detection with Multiscale Scan Statistics. CoRR abs/1802.06054 (2018) - [i6]Liwei Wu, Cho-Jui Hsieh, James Sharpnack:
SQL-Rank: A Listwise Approach to Collaborative Ranking. CoRR abs/1803.00114 (2018) - [i5]Shitong Wei, Oscar Hernan Madrid Padilla, James Sharpnack:
Distributed Cartesian Power Graph Segmentation for Graphon Estimation. CoRR abs/1805.09978 (2018) - 2017
- [j3]Oscar Hernan Madrid Padilla, James Sharpnack, James G. Scott, Ryan J. Tibshirani:
The DFS Fused Lasso: Linear-Time Denoising over General Graphs. J. Mach. Learn. Res. 18: 176:1-176:36 (2017) - [c13]Liwei Wu, Cho-Jui Hsieh, James Sharpnack:
Large-scale Collaborative Ranking in Near-Linear Time. KDD 2017: 515-524 - [c12]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, Ryan J. Tibshirani:
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. NIPS 2017: 5800-5810 - [c11]Kevin Lin, James Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani:
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. NIPS 2017: 6884-6893 - 2016
- [j2]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. J. Mach. Learn. Res. 17: 105:1-105:41 (2016) - [j1]James Sharpnack, Alessandro Rinaldo, Aarti Singh:
Detecting Anomalous Activity on Networks With the Graph Fourier Scan Statistic. IEEE Trans. Signal Process. 64(2): 364-379 (2016) - 2015
- [c10]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. AISTATS 2015 - 2014
- [i4]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. CoRR abs/1410.7690 (2014) - 2013
- [b1]James Sharpnack:
Graph Structured Normal Means Inference. Carnegie Mellon University, USA, 2013 - [c9]Akshay Krishnamurthy, James Sharpnack, Aarti Singh:
Recovering graph-structured activations using adaptive compressive measurements. ACSSC 2013: 765-769 - [c8]James Sharpnack, Aarti Singh, Akshay Krishnamurthy:
Detecting Activations over Graphs using Spanning Tree Wavelet Bases. AISTATS 2013: 536-544 - [c7]James Sharpnack, Aarti Singh, Alessandro Rinaldo:
Changepoint Detection over Graphs with the Spectral Scan Statistic. AISTATS 2013: 545-553 - [c6]James Sharpnack:
A path algorithm for localizing anomalous activity in graphs. GlobalSIP 2013: 341-344 - [c5]James Sharpnack, Aarti Singh:
Near-optimal and computationally efficient detectors for weak and sparse graph-structured patterns. GlobalSIP 2013: 443-446 - [c4]James Sharpnack, Akshay Krishnamurthy, Aarti Singh:
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic. NIPS 2013: 1959-1967 - [i3]Akshay Krishnamurthy, James Sharpnack, Aarti Singh:
Recovering Graph-Structured Activations using Adaptive Compressive Measurements. CoRR abs/1305.0213 (2013) - 2012
- [c3]Mladen Kolar, James Sharpnack:
Variance Function Estimation in High-dimensions. ICML 2012 - [c2]James Sharpnack, Aarti Singh, Alessandro Rinaldo:
Sparsistency of the Edge Lasso over Graphs. AISTATS 2012: 1028-1036 - [i2]James Sharpnack, Alessandro Rinaldo, Aarti Singh:
Changepoint Detection over Graphs with the Spectral Scan Statistic. CoRR abs/1206.0773 (2012) - [i1]Akshay Krishnamurthy, James Sharpnack, Aarti Singh:
Detecting Activations over Graphs using Spanning Tree Wavelet Bases. CoRR abs/1206.0937 (2012) - 2010
- [c1]James Sharpnack, Aarti Singh:
Identifying graph-structured activation patterns in networks. NIPS 2010: 2137-2145
Coauthor Index
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