Identifying graph-structured activation patterns in networks. - James Sharpnack; Aarti Singh (NIPS, 2010)

Variance Function Estimation in High-dimensions. - Mladen Kolar; James Sharpnack (ICML, 2012)

Sparsistency of the Edge Lasso over Graphs. - James Sharpnack; Aarti Singh; Alessandro Rinaldo (AISTATS, 2012)

Recovering graph-structured activations using adaptive compressive measurements. - Akshay Krishnamurthy; James Sharpnack; Aarti Singh (ACSSC, 2013)

Detecting Activations over Graphs using Spanning Tree Wavelet Bases. - James Sharpnack; Aarti Singh; Akshay Krishnamurthy (AISTATS, 2013)

Changepoint Detection over Graphs with the Spectral Scan Statistic. - James Sharpnack; Aarti Singh; Alessandro Rinaldo (AISTATS, 2013)

A path algorithm for localizing anomalous activity in graphs. - James Sharpnack (GlobalSIP, 2013)

Near-optimal and computationally efficient detectors for weak and sparse graph-structured patterns. - James Sharpnack; Aarti Singh (GlobalSIP, 2013)

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic. - James Sharpnack; Akshay Krishnamurthy; Aarti Singh (NIPS, 2013)

Graph Structured Normal Means Inference. - James Sharpnack (2013)

Trend Filtering on Graphs. - Yu-Xiang Wang 003; James Sharpnack; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2016)

Detecting Anomalous Activity on Networks With the Graph Fourier Scan Statistic. - James Sharpnack; Alessandro Rinaldo; Aarti Singh (IEEE Trans. Signal Process., 2016)

Large-scale Collaborative Ranking in Near-Linear Time. - Liwei Wu; Cho-Jui Hsieh; James Sharpnack (KDD, 2017)

A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. - Kevin Lin; James Sharpnack; Alessandro Rinaldo; Ryan J. Tibshirani (NIPS, 2017)

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; James Sharpnack; Ryan J. Tibshirani (NIPS, 2017)

The DFS Fused Lasso: Linear-Time Denoising over General Graphs. - Oscar Hernan Madrid Padilla; James Sharpnack; James G. Scott; Ryan J. Tibshirani (J. Mach. Learn. Res., 2017)

Learning Patterns for Detection with Multiscale Scan Statistics. - James Sharpnack (COLT, 2018)

SQL-Rank: A Listwise Approach to Collaborative Ranking. - Liwei Wu; Cho-Jui Hsieh; James Sharpnack (ICML, 2018)

Distributed Cartesian Power Graph Segmentation for Graphon Estimation. - Shitong Wei; Oscar Hernan Madrid Padilla; James Sharpnack (CoRR, 2018)

Estimating Graphlet Statistics via Lifting. - Kirill Paramonov; Dmitry Shemetov; James Sharpnack (KDD, 2019)

Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. - Liwei Wu; Shuqing Li; Cho-Jui Hsieh; James L. Sharpnack (NeurIPS, 2019)

Temporal Collaborative Ranking Via Personalized Transformer. - Liwei Wu; Shuqing Li; Cho-Jui Hsieh; James Sharpnack (CoRR, 2019)

Unsupervised Object Segmentation with Explicit Localization Module. - Weitang Liu; Lifeng Wei; James Sharpnack; John D. Owens (CoRR, 2019)

Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. - Liwei Wu; Hsiang-Fu Yu; Nikhil Rao 001; James Sharpnack; Cho-Jui Hsieh (AISTATS, 2020)

SSE-PT: Sequential Recommendation Via Personalized Transformer. - Liwei Wu; Shuqing Li; Cho-Jui Hsieh; James Sharpnack (RecSys, 2020)

Multiscale Non-stationary Stochastic Bandits. - Qin Ding 002; Cho-Jui Hsieh; James Sharpnack (CoRR, 2020)

An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. - Qin Ding 002; Cho-Jui Hsieh; James Sharpnack (AISTATS, 2021)

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. - Qin Ding 002; Yi-Wei Liu; Cho-Jui Hsieh; James Sharpnack (CoRR, 2021)

Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. - Qin Ding 002; Cho-Jui Hsieh; James Sharpnack (AISTATS, 2022)

An Unsupervised Hunt for Gravitational Lenses. - Stephen Sheng; Keerthi Vasan G. C; Chi Po P. Choi; James Sharpnack; Tucker Jones (AISTATS, 2022)

Multimodal AutoML for Image, Text and Tabular Data. - Nick Erickson; Xingjian Shi; James Sharpnack; Alexander J. Smola (KDD, 2022)

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. - Qin Ding 002; Yue Kang 002; Yi-Wei Liu; Thomas Chun Man Lee; Cho-Jui Hsieh; James Sharpnack (NeurIPS, 2022)

RLSbench: Domain Adaptation Under Relaxed Label Shift. - Saurabh Garg; Nick Erickson; James Sharpnack; Alex Smola; Sivaraman Balakrishnan; Zachary Chase Lipton (ICML, 2023)

On L2-Consistency of Nearest Neighbor Matching. - James Sharpnack (IEEE Trans. Inf. Theory, 2023)

Detecting LLM-Assisted Cheating on Open-Ended Writing Tasks on Language Proficiency Tests. - Chenhao Niu; Kevin P. Yancey; Ruidong Liu; Mirza Basim Baig; André Horie; James Sharpnack (EMNLP (Industry Track), 2024)

Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging. - Xiawei Wang; James Sharpnack; Thomas C. M. Lee (CoRR, 2024)

AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning. - James Sharpnack; Phoebe Mulcaire; Klinton Bicknell; Geoffrey T. LaFlair; Kevin P. Yancey (CoRR, 2024)

AI-assisted Gaze Detection for Proctoring Online Exams. - Yong-Siang Shih; Zach Zhao; Chenhao Niu; Bruce Iberg; James Sharpnack; Mirza Basim Baig (CoRR, 2024)

BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration. - James Sharpnack; Kevin Hao; Phoebe Mulcaire; Klinton Bicknell; Geoffrey T. LaFlair; Kevin P. Yancey; Alina A. von Davier (CoRR, 2024)