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09/09/2023
AmbientFlow: Invertible generative models from incomplete, noisy measurements
Generative models have gained popularity for their potential application...
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06/15/2023
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
The Landsat program is the longest-running Earth observation program in ...
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05/05/2023
Neural Exploitation and Exploration of Contextual Bandits
In this paper, we study utilizing neural networks for the exploitation a...
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03/17/2023
Alternate Loss Functions Can Improve the Performance of Artificial Neural Networks
All machine learning algorithms use a loss, cost, utility or reward func...
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10/02/2022
Improved Algorithms for Neural Active Learning
We improve the theoretical and empirical performance of neural-network(N...
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09/29/2022
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
We consider the problem of optimization of deep learning models with smo...
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01/09/2022
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
We study generalization bounds for noisy stochastic mini-batch iterative...
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11/17/2021
TorchGeo: deep learning with geospatial data
Remotely sensed geospatial data are critical for applications including ...
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10/07/2021
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Contextual multi-armed bandits have been studied for decades and adapted...
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02/26/2021
Experiments with Rich Regime Training for Deep Learning
In spite of advances in understanding lazy training, recent work attribu...
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07/07/2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Differentially private SGD (DP-SGD) is one of the most popular methods f...
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06/24/2020
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds
We study differentially private (DP) algorithms for stochastic non-conve...
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06/14/2020
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate...
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02/27/2020
Gradient Boosted Flows
Normalizing flows (NF) are a powerful framework for approximating poster...
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02/26/2020
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Bandit learning algorithms typically involve the balance of exploration ...
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02/23/2020
De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors
In spite of several notable efforts, explaining the generalization of de...
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10/11/2019
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Several important families of computational and statistical results in m...
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07/24/2019
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
While stochastic gradient descent (SGD) and variants have been surprisin...
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07/10/2019
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence
Alternating Direction Method of Multipliers (ADMM) has become a widely u...
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11/03/2018
DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora
Extracting common narratives from multi-author dynamic text corpora requ...
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09/21/2018
Adversarial Recommendation: Attack of the Learned Fake Users
Can machine learning models for recommendation be easily fooled? While t...
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07/16/2018
Time Series Deinterleaving of DNS Traffic
Stream deinterleaving is an important problem with various applications ...
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06/11/2018
High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient
High dimensional structured data enriched model describes groups of obse...
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01/15/2018
Topic Modeling on Health Journals with Regularized Variational Inference
Topic modeling enables exploration and compact representation of a corpu...
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10/16/2017
Sparse Linear Isotonic Models
In machine learning and data mining, linear models have been widely used...
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09/12/2017
High-Dimensional Dependency Structure Learning for Physical Processes
In this paper, we consider the use of structure learning methods for pro...
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09/10/2017
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting
Multivariate time-series modeling and forecasting is an important proble...
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05/30/2017
High Dimensional Structured Superposition Models
High dimensional superposition models characterize observations using pa...
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01/30/2017
Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning
Future projection of climate is typically obtained by combining outputs ...
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01/18/2017
Recommendation under Capacity Constraints
In this paper, we investigate the common scenario where every candidate ...
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12/27/2016
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Data science models, although successful in a number of commercial domai...
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06/29/2016
Alternating Estimation for Structured High-Dimensional Multi-Response Models
We consider learning high-dimensional multi-response linear models with ...
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06/17/2016
Structured Stochastic Linear Bandits
The stochastic linear bandit problem proceeds in rounds where at each ro...
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04/12/2016
Structured Matrix Recovery via the Generalized Dantzig Selector
In recent years, structured matrix recovery problems have gained conside...
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04/12/2016
The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications
While the Matrix Generalized Inverse Gaussian (MGIG) distribution arises...
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03/29/2016
Unified View of Matrix Completion under General Structural Constraints
In this paper, we present a unified analysis of matrix completion under ...
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02/21/2016
Estimating Structured Vector Autoregressive Model
While considerable advances have been made in estimating high-dimensiona...
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02/21/2016
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems
In this work we consider the problem of anomaly detection in heterogeneo...
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05/09/2015
Estimation with Norm Regularization
Analysis of non-asymptotic estimation error and structured statistical r...
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07/12/2014
A Spectral Algorithm for Inference in Hidden Semi-Markov Models
Hidden semi-Markov models (HSMMs) are latent variable models which allow...
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09/26/2013
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference
We consider the problem of maximum a posteriori (MAP) inference in discr...
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06/13/2013
Bregman Alternating Direction Method of Multipliers
The mirror descent algorithm (MDA) generalizes gradient descent by using...
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06/27/2012
Online Alternating Direction Method
Online optimization has emerged as powerful tool in large scale optimiza...
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03/15/2012