
Solving the Empirical Bayes Normal Means Problem with Correlated Noise
The Normal Means problem plays a fundamental role in many areas of moder...
read it

Style Transfer by Relaxed Optimal Transport and SelfSimilarity
Style transfer algorithms strive to render the content of one image usin...
read it

Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders
The success of enhanced sampling molecular simulations that accelerate a...
read it

Fingerspelling recognition in the wild with iterative visual attention
Sign language recognition is a challenging gesture sequence recognition ...
read it

Deep Learning for Automated Classification and Characterization of Amorphous Materials
It is difficult to quantify structureproperty relationships and to iden...
read it

Domainindependent Dominance of Adaptive Methods
From a simplified analysis of adaptive methods, we derive AvaGrad, a new...
read it

Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
read it

Scalable Data Augmentation for Deep Learning
Scalable Data Augmentation (SDA) provides a framework for training deep ...
read it

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Materials discovery is crucial for making scientific advances in many do...
read it

Generating Diverse Story Continuations with Controllable Semantics
We propose a simple and effective modeling framework for controlled gene...
read it

Are Pretrained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
With the recent success and popularity of pretrained language models (L...
read it

Phase Transitions in Approximate Ranking
We study the problem of approximate ranking from observations of pairwis...
read it

Machine learning in sentiment reconstruction of the simulated stock market
In this paper we continue the study of the simulated stock market framew...
read it

Stochastic Strictly Contractive PeacemanRachford Splitting Method
In this paper, we propose a couple of new Stochastic Strictly Contractiv...
read it

RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning
Anonymized electronic medical records are an increasingly popular source...
read it

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output var...
read it

The Impact of Local Geometry and Batch Size on the Convergence and Divergence of Stochastic Gradient Descent
Stochastic smallbatch (SB) methods, such as minibatch Stochastic Gradi...
read it

Sparse Regularization in Marketing and Economics
Sparse alphanorm regularization has many datarich applications in mark...
read it

Evaluation of a Treebased Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an everin...
read it

Bayesian l_0 Regularized Least Squares
Bayesian l_0regularized least squares provides a variable selection tec...
read it

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
We propose the Gaussian attention model for contentbased neural memory ...
read it

Interpreting Neural Networks to Improve Politeness Comprehension
We present an interpretable neural network approach to predicting and un...
read it

LogitBoost autoregressive networks
Multivariate binary distributions can be decomposed into products of uni...
read it

A unified treatment of multiple testing with prior knowledge using the pfilter
A significant literature studies ways of employing prior knowledge to im...
read it

Stochastic Canonical Correlation Analysis
We tightly analyze the sample complexity of CCA, provide a learning algo...
read it

Dynamic Partition Models
We present a new approach for learning compact and intuitive distributed...
read it

On SGD's Failure in Practice: Characterizing and Overcoming Stalling
Stochastic Gradient Descent (SGD) is widely used in machine learning pro...
read it

Colorization as a Proxy Task for Visual Understanding
We investigate and improve selfsupervision as a dropin replacement for...
read it

A Geometrical Approach to Topic Model Estimation
In the probabilistic topic models, the quantity of interesta lowrank...
read it

Ensemble preconditioning for Markov chain Monte Carlo simulation
We describe parallel Markov chain Monte Carlo methods that propagate a c...
read it

Machine Learning for Antimicrobial Resistance
Biological datasets amenable to applied machine learning are more availa...
read it

Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Similarity between objects is multifaceted and it can be easier for hum...
read it

Completely random measures for modeling power laws in sparse graphs
Network data appear in a number of applications, such as online social n...
read it

Edgeexchangeable graphs and sparsity
A known failing of many popular random graph models is that the AldousH...
read it

Identifiability of an Xrank decomposition of polynomial maps
In this paper, we study a polynomial decomposition model that arises in ...
read it

Examining the Impact of Blur on Recognition by Convolutional Networks
Stateoftheart algorithms for many semantic visual tasks are based on ...
read it

Kalmanbased Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning
Modern proximal and stochastic gradient descent (SGD) methods are believ...
read it

AUCmaximized Deep Convolutional Neural Fields for Sequence Labeling
Deep Convolutional Neural Networks (DCNN) has shown excellent performanc...
read it

Mixtures of Sparse Autoregressive Networks
We consider highdimensional distribution estimation through autoregress...
read it

Distributed Multitask Learning
We consider the problem of distributed multitask learning, where each m...
read it

A Statistical Theory of Deep Learning via Proximal Splitting
In this paper we develop a statistical theory and an implementation of d...
read it

Directional Decision Lists
In this paper we introduce a novel family of decision lists consisting o...
read it

FractalNet: UltraDeep Neural Networks without Residuals
We introduce a design strategy for neural network macroarchitecture bas...
read it

Quantized Nonparametric Estimation over Sobolev Ellipsoids
We formulate the notion of minimax estimation under storage or communica...
read it

Phase Transitions for High Dimensional Clustering and Related Problems
Consider a twoclass clustering problem where we observe X_i = ℓ_i μ + Z...
read it

Proximal Algorithms in Statistics and Machine Learning
In this paper we develop proximal methods for statistical learning. Prox...
read it

Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
We consider the problem of removing and replacing clouds in satellite im...
read it

Cohomology of CryoElectron Microscopy
The goal of cryoelectron microscopy (EM) is to reconstruct the 3dimens...
read it

Compact Compositional Models
Learning compact and interpretable representations is a very natural tas...
read it

Faithful Variable Screening for HighDimensional Convex Regression
We study the problem of variable selection in convex nonparametric regre...
read it
The University of Chicago
Student Government is the main representative body for all graduate and undergraduate students at UChicago. It disburses more than $2 million each year in support of student life.