Search Results for author:
Found 34 papers, 10 papers with code
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning.
Gradient Coding through Iterative Block Leverage Score Sampling
This is then used to derive an approximate coded computing approach for first-order methods; known as gradient coding, to accelerate linear regression in the presence of failures in distributed computational networks, \textit{i. e.} stragglers.
Robustness-preserving Lifelong Learning via Dataset Condensation
Most work in this learning paradigm has focused on resolving the problem of 'catastrophic forgetting,' which refers to a notorious dilemma between improving model accuracy over new data and retaining accuracy over previous data.
Incorporating Polar Field Data for Improved Solar Flare Prediction
In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models.
A Graphical Model for Fusing Diverse Microbiome Data
We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model.
Resolution Limits of Non-Adaptive 20 Questions Search for a Moving Target
Our task is to query the oracle as few times as possible to accurately estimate the location of the target at any specified time.
Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours.
Multiway Ensemble Kalman Filter
In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs).
Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning
Deep learning-based Multi-Task Classification (MTC) is widely used in applications like facial attributes and healthcare that warrant strong privacy guarantees.
Multi-Trigger-Key: Towards Multi-Task Privacy-Preserving In Deep Learning
Deep learning-based Multi-Task Classification (MTC) is widely used in applications like facial attribute and healthcare that warrant strong privacy guarantees.
Deep Adversarially-Enhanced k-Nearest Neighbors
Recent works have theoretically and empirically shown that deep neural networks (DNNs) have an inherent vulnerability to small perturbations.
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense
K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability.
RAILS: A Robust Adversarial Immune-inspired Learning System
Initializing a population of exemplars that is balanced across classes, RAILS starts from a uniform label distribution that encourages diversity and uses an evolutionary optimization process to adaptively adjust the predictive label distribution in a manner that emulates the way the natural immune system recognizes novel pathogens.
Immuno-mimetic Deep Neural Networks (Immuno-Net)
This immuno-mimetic model leads to a new computational biology framework for robustification of deep neural networks against adversarial attacks.
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data.
RAILS: A Robust Adversarial Immune-inspired Learning System
RAILS incorporates an Adaptive Immune System Emulation (AISE), which emulates in silico the biological mechanisms that are used to defend the host against attacks by pathogens.
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications.
The Sylvester Graphical Lasso (SyGlasso)
We apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects.
Solar Flare Intensity Prediction with Machine Learning Models
We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP).
Solar and Stellar Astrophysics
Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data
In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier.
Time-Varying Interaction Estimation Using Ensemble Methods
Directed information (DI) is a useful tool to explore time-directed interactions in multivariate data.
Minimum Volume Topic Modeling
We propose a new topic modeling procedure that takes advantage of the fact that the Latent Dirichlet Allocation (LDA) log likelihood function is asymptotically equivalent to the logarithm of the volume of the topic simplex.
Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks.
Robust SAR STAP via Kronecker Decomposition
This paper proposes a spatio-temporal decomposition for the detection of moving targets in multiantenna SAR.
Nonstationary Distance Metric Learning
Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints.
Multimodal MRI Neuroimaging with Motion Compensation Based on Particle Filtering
Head motion in fMRI acquired using slice-based Echo Planar Imaging (EPI) can be estimated and compensated by aligning the images onto a reference volume through image registration.
Statistical Estimation and Clustering of Group-invariant Orientation Parameters
We treat the problem of estimation of orientation parameters whose values are invariant to transformations from a spherical symmetry group.
Coercive Region-level Registration for Multi-modal Images
We propose a coercive approach to simultaneously register and segment multi-modal images which share similar spatial structure.
Two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS)
In the first stage we collect a few ($n$) expensive samples $\{y_i,\mathbf x_i\}_{i=1}^n$, at the full dimension $p\gg n$ of $\mathbf X$, winnowing the number of variables down to a smaller dimension $l < p$ using a type of cross-correlation or regression coefficient screening.
Multivariate f-divergence Estimation With Confidence
The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics.
Parameter estimation in spherical symmetry groups
This paper considers statistical estimation problems where the probability distribution of the observed random variable is invariant with respect to actions of a finite topological group.
Shrinkage Optimized Directed Information using Pictorial Structures for Action Recognition
In this paper, we propose a novel action recognition framework.
Detection of Anomalous Crowd Behavior Using Spatio-Temporal Multiresolution Model and Kronecker Sum Decompositions
Our approach is to estimate the covariance using parameter reduction and sparse models.
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design.
