Publications (36)
Exact Asymptotics for the Scan Statistic and Fast Alternatives
James Sharpnack, Ery Arias-Castro
We consider the problem of detecting a rectangle of activation in a grid of sensors in d-dimensions with noisy measurements. This has applications to massive surveillance projects…
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
Qin Ding, Cho-Jui Hsieh, James Sharpnack
We consider the contextual bandit problem, where a player sequentially makes decisions based on past observations to maximize the cumulative reward. Although many algorithms have b…
Variance function estimation in high-dimensions
Mladen Kolar, James Sharpnack
We consider the high-dimensional heteroscedastic regression model, where the mean and the log variance are modeled as a linear combination of input variables. Existing literature o…
Distributed Cartesian Power Graph Segmentation for Graphon Estimation
Shitong Wei, Oscar Hernan Madrid-Padilla, James Sharpnack
We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models. The power graph fuse…
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
James Sharpnack, Kevin Hao, Phoebe Mulcaire +4
In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Cal…
Changepoint Detection over Graphs with the Spectral Scan Statistic
James Sharpnack, Alessandro Rinaldo, Aarti Singh
We consider the change-point detection problem of deciding, based on noisy measurements, whether an unknown signal over a given graph is constant or is instead piecewise constant o…
Adaptive Non-Parametric Regression With the -NN Fused Lasso
Oscar Hernan Madrid Padilla, James Sharpnack, Yanzhen Chen +1
The fused lasso, also known as total-variation denoising, is a locally-adaptive function estimator over a regular grid of design points. In this paper, we extend the fused lasso to…
Learning Item Embeddings and Hyperparameters for IRT Calibration via Monte Carlo EM
James Sharpnack, Kai-Ling Lo
High-stakes computerized adaptive tests (CATs) must continually calibrate new items in their item bank. When an item is new, few responses are available, so item parameter estimate…
AI-assisted Gaze Detection for Proctoring Online Exams
Yong-Siang Shih, Zach Zhao, Chenhao Niu +3
For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether te…
On -consistency of nearest neighbor matching
James Sharpnack
Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning. We often correct for biased sampling of summary statistic…
Multiscale Non-stationary Stochastic Bandits
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Classic contextual bandit algorithms for linear models, such as LinUCB, assume that the reward distribution for an arm is modeled by a stationary linear regression. When the linear…
Trend Filtering on Graphs
Yu-Xiang Wang, James Sharpnack, Alex Smola +1
We introduce a family of adaptive estimators on graphs, based on penalizing the norm of discrete graph differences. This generalizes the idea of trend filtering [Kim et al…
Fused Density Estimation: Theory and Methods
Robert Bassett, James Sharpnack
In this paper we introduce a method for nonparametric density estimation on geometric networks. We define fused density estimators as solutions to a total variation regularized max…
SQL-Rank: A Listwise Approach to Collaborative Ranking
Liwei Wu, Cho-Jui Hsieh, James Sharpnack
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to pre…
The DFS Fused Lasso: Linear-Time Denoising over General Graphs
Oscar Hernan Madrid Padilla, James G. Scott, James Sharpnack +1
The fused lasso, also known as (anisotropic) total variation denoising, is widely used for piecewise constant signal estimation with respect to a given undirected graph. The fused…
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell +2
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally,…
S2A3: Thompson Sampling and Stochastic Exposure Control for High-Stakes CATs
James Sharpnack, Alexander Tsigler, J. R. Lockwood +2
High-stakes computerized adaptive tests (CATs) require a continuous supply of calibrated items, yet traditional item piloting is slow, expensive, and operationally hazardous. We in…
Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging
Xiawei Wang, James Sharpnack, Thomas C. M. Lee
Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ…
Detecting Anomalous Activity on Networks with the Graph Fourier Scan Statistic
James Sharpnack, Alessandro Rinaldo, Aarti Singh
We consider the problem of deciding, based on a single noisy measurement at each vertex of a given graph, whether the underlying unknown signal is constant over the graph or there…
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
Qin Ding, Yue Kang, Yi-Wei Liu +3
The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online adverti…
Mean and variance estimation in high-dimensional heteroscedastic models with non-convex penalties
James Sharpnack, Mladen Kolar
Despite its prevalence in statistical datasets, heteroscedasticity (non-constant sample variances) has been largely ignored in the high-dimensional statistics literature. Recently,…
Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey
Keerthi Vasan G. C., Stephen Sheng, Tucker Jones +2
Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test…
Recovering Graph-Structured Activations using Adaptive Compressive Measurements
Akshay Krishnamurthy, James Sharpnack, Aarti Singh
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that…
Exponential Family Trend Filtering on Lattices
Veeranjaneyulu Sadhanala, Robert Bassett, James Sharpnack +1
Trend filtering is a modern approach to nonparametric regression that is more adaptive to local smoothness than splines or similar basis procedures. Existing analyses of trend filt…
Approximate Recovery in Changepoint Problems, from Estimation Error Rates
Kevin Lin, James Sharpnack, Alessandro Rinaldo +1
In the 1-dimensional multiple changepoint detection problem, we prove that any procedure with a fast enough error rate, in terms of its estimation of the underlying piecew…
Learning Patterns for Detection with Multiscale Scan Statistics
James Sharpnack
This paper addresses detecting anomalous patterns in images, time-series, and tensor data when the location and scale of the pattern is unknown a priori. The multiscale scan statis…
Detecting Activations over Graphs using Spanning Tree Wavelet Bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
We consider the detection of activations over graphs under Gaussian noise, where signals are piece-wise constant over the graph. Despite the wide applicability of such a detection…
Unsupervised Object Segmentation with Explicit Localization Module
Weitang Liu, Lifeng Wei, James Sharpnack +1
In this paper, we propose a novel architecture that iteratively discovers and segments out the objects of a scene based on the image reconstruction quality. Different from other ap…
Temporal Collaborative Ranking Via Personalized Transformer
Liwei Wu, Shuqing Li, Cho-Jui Hsieh +1
The collaborative ranking problem has been an important open research question as most recommendation problems can be naturally formulated as ranking problems. While much of collab…
Estimating Graphlet Statistics via Lifting
Kirill Paramonov, Dmitry Shemetov, James Sharpnack
Exploratory analysis over network data is often limited by the ability to efficiently calculate graph statistics, which can provide a model-free understanding of the macroscopic pr…
RLSbench: Domain Adaptation Under Relaxed Label Shift
Saurabh Garg, Nick Erickson, James Sharpnack +3
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.…
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Stochastic linear contextual bandit algorithms have substantial applications in practice, such as recommender systems, online advertising, clinical trials, etc. Recent works show t…
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity moni…
An Unsupervised Hunt for Gravitational Lenses
Stephen Sheng, Keerthi Vasan G. C, Chi Po Choi +2
Strong gravitational lenses allow us to peer into the farthest reaches of space by bending the light from a background object around a massive object in the foreground. Unfortunate…
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
Liwei Wu, Shuqing Li, Cho-Jui Hsieh +1
In deep neural nets, lower level embedding layers account for a large portion of the total number of parameters. Tikhonov regularization, graph-based regularization, and hard param…
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
Liwei Wu, Hsiang-Fu Yu, Nikhil Rao +2
In this paper, we consider recommender systems with side information in the form of graphs. Existing collaborative filtering algorithms mainly utilize only immediate neighborhood i…