
Fast Graph Learning with Unique Optimal Solutions
Graph Representation Learning (GRL) has been advancing at an unprecedent...
read it

Improved Brain Age Estimation with Slicebased Set Networks
Deep Learning for neuroimaging data is a promising but challenging direc...
read it

Graph Traversal with Tensor Functionals: A MetaAlgorithm for Scalable Learning
Graph Representation Learning (GRL) methods have impacted fields from ch...
read it

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Controlling bias in training datasets is vital for ensuring equal treatm...
read it

Likelihood Ratio Exponential Families
The exponential family is well known in machine learning and statistical...
read it

Annealed Importance Sampling with qPaths
Annealed importance sampling (AIS) is the gold standard for estimating p...
read it

Compressing Deep Neural Networks via Layer Fusion
This paper proposes layer fusion  a model compression technique that di...
read it

Robust Classification under ClassDependent Domain Shift
Investigation of machine learning algorithms robust to changes between t...
read it

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
The recently proposed Thermodynamic Variational Objective (TVO) leverage...
read it

Overview of Scanner Invariant Representations
Pooled imaging data from multiple sources is subject to bias from each s...
read it

Event Cartography: Latent Point Process Embeddings
Many important phenomena arise naturally as temporal point processes wit...
read it

Improving Generalization by Controlling LabelNoise Information in Neural Network Weights
In the presence of noisy or incorrect labels, neural networks have the u...
read it

Discovery and Separation of Features for Invariant Representation Learning
Supervised machine learning models often associate irrelevant nuisance f...
read it

Invariant Representations through Adversarial Forgetting
We propose a novel approach to achieving invariance for deep neural netw...
read it

NearlyUnsupervised Hashcode Representations for Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully...
read it

Efficient Covariance Estimation from Temporal Data
Estimating the covariance structure of multivariate time series is a fun...
read it

MixHop: HigherOrder Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semisupervised learning with Graph Neural ...
read it

Exact RateDistortion in Autoencoders via Echo Noise
Compression is at the heart of effective representation learning. Howeve...
read it

Scanner Invariant Representations for Diffusion MRI Harmonization
Pooled imaging data from multiple sources is subject to variation betwee...
read it

Identifying and Analyzing Cryptocurrency Manipulations in Social Media
Interest surrounding cryptocurrencies, digital or virtual currencies tha...
read it

Maximizing Multivariate Information with ErrorCorrecting Codes
Multivariate mutual information provides a conceptual framework for char...
read it

Measures of Tractography Convergence
In the present work, we use information theory to understand the empiric...
read it

Evading the Adversary in Invariant Representation
Representations of data that are invariant to changes in specified nuisa...
read it

A Forest Mixture Bound for BlockFree Parallel Inference
Coordinate ascent variational inference is an important algorithm for in...
read it

Dialogue Modeling Via Hash Functions: Applications to Psychotherapy
We propose a novel machinelearning framework for dialogue modeling whic...
read it

AutoEncoding Total Correlation Explanation
Advances in unsupervised learning enable reconstruction and generation o...
read it

Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Natural language processing often involves computations with semantic or...
read it

Unifying Local and Global Change Detection in Dynamic Networks
Many realworld networks are complex dynamical systems, where both local...
read it

Unsupervised Learning via Total Correlation Explanation
Learning by children and animals occurs effortlessly and largely without...
read it

Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality
Learning the structure of graphical models from data is a fundamental pr...
read it

Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
While generative models such as Latent Dirichlet Allocation (LDA) have p...
read it

Toward Interpretable Topic Discovery via Anchored Correlation Explanation
Many predictive tasks, such as diagnosing a patient based on their medic...
read it

Variational Information Maximization for Feature Selection
Feature selection is one of the most fundamental problems in machine lea...
read it

Sifting Common Information from Many Variables
Measuring the relationship between any pair of variables is a rich and a...
read it

The Information Sieve
We introduce a new framework for unsupervised learning of representation...
read it

Understanding confounding effects in linguistic coordination: an informationtheoretic approach
We suggest an informationtheoretic approach for measuring stylistic coo...
read it

Scalable Link Prediction in Dynamic Networks via NonNegative Matrix Factorization
We propose a scalable temporal latent space model for link prediction in...
read it

Efficient Estimation of Mutual Information for Strongly Dependent Variables
We demonstrate that a popular class of nonparametric mutual information ...
read it

Maximally Informative Hierarchical Representations of HighDimensional Data
We consider a set of probabilistic functions of some input variables as ...
read it

Discovering Structure in HighDimensional Data Through Correlation Explanation
We introduce a method to learn a hierarchy of successively more abstract...
read it

Phase Transitions in Community Detection: A Solvable Toy Model
Recently, it was shown that there is a phase transition in the community...
read it

Coevolution of Selection and Influence in Social Networks
Many networks are complex dynamical systems, where both attributes of no...
read it

A Sequence of Relaxations Constraining Hidden Variable Models
Many widely studied graphical models with latent variables lead to nontr...
read it