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11/03/2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
Sampling from an unnormalized probability distribution is a fundamental ...
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10/04/2021
Information-theoretic generalization bounds for black-box learning algorithms
We derive information-theoretic generalization bounds for supervised lea...
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08/07/2021
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Federated learning (FL) enables distributed computation of machine learn...
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07/01/2021
q-Paths: Generalizing the Geometric Annealing Path using Power Means
Many common machine learning methods involve the geometric annealing pat...
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05/06/2021
Membership Inference Attacks on Deep Regression Models for Neuroimaging
Ensuring the privacy of research participants is vital, even more so in ...
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02/17/2021
Fast Graph Learning with Unique Optimal Solutions
Graph Representation Learning (GRL) has been advancing at an unprecedent...
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02/08/2021
Improved Brain Age Estimation with Slice-based Set Networks
Deep Learning for neuroimaging data is a promising but challenging direc...
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02/08/2021
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Graph Representation Learning (GRL) methods have impacted fields from ch...
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01/11/2021
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Controlling bias in training datasets is vital for ensuring equal treatm...
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12/31/2020
Likelihood Ratio Exponential Families
The exponential family is well known in machine learning and statistical...
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12/14/2020
Annealed Importance Sampling with q-Paths
Annealed importance sampling (AIS) is the gold standard for estimating p...
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07/29/2020
Compressing Deep Neural Networks via Layer Fusion
This paper proposes layer fusion - a model compression technique that di...
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07/10/2020
Robust Classification under Class-Dependent Domain Shift
Investigation of machine learning algorithms robust to changes between t...
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07/01/2020
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
The recently proposed Thermodynamic Variational Objective (TVO) leverage...
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05/29/2020
Overview of Scanner Invariant Representations
Pooled imaging data from multiple sources is subject to bias from each s...
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05/05/2020
Event Cartography: Latent Point Process Embeddings
Many important phenomena arise naturally as temporal point processes wit...
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02/19/2020
Improving Generalization by Controlling Label-Noise Information in Neural Network Weights
In the presence of noisy or incorrect labels, neural networks have the u...
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12/02/2019
Discovery and Separation of Features for Invariant Representation Learning
Supervised machine learning models often associate irrelevant nuisance f...
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11/11/2019
Invariant Representations through Adversarial Forgetting
We propose a novel approach to achieving invariance for deep neural netw...
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09/09/2019
Nearly-Unsupervised Hashcode Representations for Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully...
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05/30/2019
Efficient Covariance Estimation from Temporal Data
Estimating the covariance structure of multivariate time series is a fun...
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04/30/2019
MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural ...
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04/15/2019
Exact Rate-Distortion in Autoencoders via Echo Noise
Compression is at the heart of effective representation learning. Howeve...
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04/10/2019
Scanner Invariant Representations for Diffusion MRI Harmonization
Pooled imaging data from multiple sources is subject to variation betwee...
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02/04/2019
Identifying and Analyzing Cryptocurrency Manipulations in Social Media
Interest surrounding cryptocurrencies, digital or virtual currencies tha...
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11/27/2018
Maximizing Multivariate Information with Error-Correcting Codes
Multivariate mutual information provides a conceptual framework for char...
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06/12/2018
Measures of Tractography Convergence
In the present work, we use information theory to understand the empiric...
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05/24/2018
Evading the Adversary in Invariant Representation
Representations of data that are invariant to changes in specified nuisa...
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05/17/2018
A Forest Mixture Bound for Block-Free Parallel Inference
Coordinate ascent variational inference is an important algorithm for in...
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04/26/2018
Dialogue Modeling Via Hash Functions: Applications to Psychotherapy
We propose a novel machine-learning framework for dialogue modeling whic...
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02/16/2018
Auto-Encoding Total Correlation Explanation
Advances in unsupervised learning enable reconstruction and generation o...
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01/11/2018
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Natural language processing often involves computations with semantic or...
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10/09/2017
Unifying Local and Global Change Detection in Dynamic Networks
Many real-world networks are complex dynamical systems, where both local...
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06/27/2017
Unsupervised Learning via Total Correlation Explanation
Learning by children and animals occurs effortlessly and largely without...
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06/11/2017
Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality
Learning the structure of graphical models from data is a fundamental pr...
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11/30/2016
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
While generative models such as Latent Dirichlet Allocation (LDA) have p...
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06/22/2016
Toward Interpretable Topic Discovery via Anchored Correlation Explanation
Many predictive tasks, such as diagnosing a patient based on their medic...
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06/09/2016
Variational Information Maximization for Feature Selection
Feature selection is one of the most fundamental problems in machine lea...
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06/07/2016
Sifting Common Information from Many Variables
Measuring the relationship between any pair of variables is a rich and a...
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07/08/2015
The Information Sieve
We introduce a new framework for unsupervised learning of representation...
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12/01/2014
Understanding confounding effects in linguistic coordination: an information-theoretic approach
We suggest an information-theoretic approach for measuring stylistic coo...
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11/13/2014
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
We propose a scalable temporal latent space model for link prediction in...
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11/07/2014
Efficient Estimation of Mutual Information for Strongly Dependent Variables
We demonstrate that a popular class of nonparametric mutual information ...
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10/27/2014
Maximally Informative Hierarchical Representations of High-Dimensional Data
We consider a set of probabilistic functions of some input variables as ...
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06/04/2014
Discovering Structure in High-Dimensional Data Through Correlation Explanation
We introduce a method to learn a hierarchy of successively more abstract...
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12/02/2013
Phase Transitions in Community Detection: A Solvable Toy Model
Recently, it was shown that there is a phase transition in the community...
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06/14/2011
Co-evolution of Selection and Influence in Social Networks
Many networks are complex dynamical systems, where both attributes of no...
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06/08/2011
