Greg Ver Steeg

research

∙ 11/03/2021

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

Sampling from an unnormalized probability distribution is a fundamental ...

135 Greg Ver Steeg, et al. ∙

research

∙ 10/04/2021

Information-theoretic generalization bounds for black-box learning algorithms

We derive information-theoretic generalization bounds for supervised lea...

2 Hrayr Harutyunyan, et al. ∙

research

∙ 08/07/2021

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

Federated learning (FL) enables distributed computation of machine learn...

20 Dimitris Stripelis, et al. ∙

research

∙ 07/01/2021

q-Paths: Generalizing the Geometric Annealing Path using Power Means

Many common machine learning methods involve the geometric annealing pat...

5 Vaden Masrani, et al. ∙

research

∙ 05/06/2021

Membership Inference Attacks on Deep Regression Models for Neuroimaging

Ensuring the privacy of research participants is vital, even more so in ...

8 Umang Gupta, et al. ∙

research

∙ 02/17/2021

Fast Graph Learning with Unique Optimal Solutions

Graph Representation Learning (GRL) has been advancing at an unprecedent...

23 Sami Abu-El-Haija, et al. ∙

research

∙ 02/08/2021

Improved Brain Age Estimation with Slice-based Set Networks

Deep Learning for neuroimaging data is a promising but challenging direc...

5 Umang Gupta, et al. ∙

research

∙ 02/08/2021

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

Graph Representation Learning (GRL) methods have impacted fields from ch...

11 Elan Markowitz, et al. ∙

research

∙ 01/11/2021

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Controlling bias in training datasets is vital for ensuring equal treatm...

7 Umang Gupta, et al. ∙

research

∙ 12/31/2020

Likelihood Ratio Exponential Families

The exponential family is well known in machine learning and statistical...

12 Rob Brekelmans, et al. ∙

research

∙ 12/14/2020

Annealed Importance Sampling with q-Paths

Annealed importance sampling (AIS) is the gold standard for estimating p...

8 Rob Brekelmans, et al. ∙

research

∙ 07/29/2020

Compressing Deep Neural Networks via Layer Fusion

This paper proposes layer fusion - a model compression technique that di...

18 James O'Neill, et al. ∙

research

∙ 07/10/2020

Robust Classification under Class-Dependent Domain Shift

Investigation of machine learning algorithms robust to changes between t...

10 Tigran Galstyan, et al. ∙

research

∙ 07/01/2020

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference

The recently proposed Thermodynamic Variational Objective (TVO) leverage...

9 Rob Brekelmans, et al. ∙

research

∙ 05/29/2020

Overview of Scanner Invariant Representations

Pooled imaging data from multiple sources is subject to bias from each s...

8 Daniel Moyer, et al. ∙

research

∙ 05/05/2020

Event Cartography: Latent Point Process Embeddings

Many important phenomena arise naturally as temporal point processes wit...

10 Myrl G. Marmarelis, et al. ∙

research

∙ 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...

23 Hrayr Harutyunyan, et al. ∙

research

∙ 12/02/2019

Discovery and Separation of Features for Invariant Representation Learning

Supervised machine learning models often associate irrelevant nuisance f...

47 Ayush Jaiswal, et al. ∙

research

∙ 11/11/2019

Invariant Representations through Adversarial Forgetting

We propose a novel approach to achieving invariance for deep neural netw...

29 Ayush Jaiswal, et al. ∙

research

∙ 09/09/2019

Nearly-Unsupervised Hashcode Representations for Relation Extraction

Recently, kernelized locality sensitive hashcodes have been successfully...

4 Sahil Garg, et al. ∙

research

∙ 05/30/2019

Efficient Covariance Estimation from Temporal Data

Estimating the covariance structure of multivariate time series is a fun...

3 Hrayr Harutyunyan, et al. ∙

research

∙ 04/30/2019

MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing

Existing popular methods for semi-supervised learning with Graph Neural ...

10 Sami Abu-El-Haija, et al. ∙

research

∙ 04/15/2019

Exact Rate-Distortion in Autoencoders via Echo Noise

Compression is at the heart of effective representation learning. Howeve...

36 Rob Brekelmans, et al. ∙

research

∙ 04/10/2019

Scanner Invariant Representations for Diffusion MRI Harmonization

Pooled imaging data from multiple sources is subject to variation betwee...

26 Daniel Moyer, et al. ∙

research

∙ 02/04/2019

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Interest surrounding cryptocurrencies, digital or virtual currencies tha...

12 Mehrnoosh Mirtaheri, et al. ∙

research

∙ 11/27/2018

Maximizing Multivariate Information with Error-Correcting Codes

Multivariate mutual information provides a conceptual framework for char...

0 Kyle Reing, et al. ∙

research

∙ 06/12/2018

Measures of Tractography Convergence

In the present work, we use information theory to understand the empiric...

0 Daniel Moyer, et al. ∙

research

∙ 05/24/2018

Evading the Adversary in Invariant Representation

Representations of data that are invariant to changes in specified nuisa...

0 Daniel Moyer, et al. ∙

research

∙ 05/17/2018

A Forest Mixture Bound for Block-Free Parallel Inference

Coordinate ascent variational inference is an important algorithm for in...

0 Neal Lawton, et al. ∙

research

∙ 04/26/2018

Dialogue Modeling Via Hash Functions: Applications to Psychotherapy

We propose a novel machine-learning framework for dialogue modeling whic...

0 Sahil Garg, et al. ∙

research

∙ 02/16/2018

Auto-Encoding Total Correlation Explanation

Advances in unsupervised learning enable reconstruction and generation o...

0 Shuyang Gao, et al. ∙

research

∙ 01/11/2018

Stochastic Learning of Nonstationary Kernels for Natural Language Modeling

Natural language processing often involves computations with semantic or...

0 Sahil Garg, et al. ∙

research

∙ 10/09/2017

Unifying Local and Global Change Detection in Dynamic Networks

Many real-world networks are complex dynamical systems, where both local...

0 Wenzhe Li, et al. ∙

research

∙ 06/27/2017

Unsupervised Learning via Total Correlation Explanation

Learning by children and animals occurs effortlessly and largely without...

0 Greg Ver Steeg, et al. ∙

research

∙ 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...

0 Greg Ver Steeg, et al. ∙

research

∙ 11/30/2016

Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

While generative models such as Latent Dirichlet Allocation (LDA) have p...

0 Ryan J. Gallagher, et al. ∙

research

∙ 06/22/2016

Toward Interpretable Topic Discovery via Anchored Correlation Explanation

Many predictive tasks, such as diagnosing a patient based on their medic...

0 Kyle Reing, et al. ∙

research

∙ 06/09/2016

Variational Information Maximization for Feature Selection

Feature selection is one of the most fundamental problems in machine lea...

0 Shuyang Gao, et al. ∙

research

∙ 06/07/2016

Sifting Common Information from Many Variables

Measuring the relationship between any pair of variables is a rich and a...

0 Greg Ver Steeg, et al. ∙

research

∙ 07/08/2015

The Information Sieve

We introduce a new framework for unsupervised learning of representation...

0 Greg Ver Steeg, et al. ∙

research

∙ 12/01/2014

Understanding confounding effects in linguistic coordination: an information-theoretic approach

We suggest an information-theoretic approach for measuring stylistic coo...

0 Shuyang Gao, et al. ∙

research

∙ 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...

0 Linhong Zhu, et al. ∙

research

∙ 11/07/2014

Efficient Estimation of Mutual Information for Strongly Dependent Variables

We demonstrate that a popular class of nonparametric mutual information ...

0 Shuyang Gao, et al. ∙

research

∙ 10/27/2014

Maximally Informative Hierarchical Representations of High-Dimensional Data

We consider a set of probabilistic functions of some input variables as ...

0 Greg Ver Steeg, et al. ∙

research

∙ 06/04/2014

Discovering Structure in High-Dimensional Data Through Correlation Explanation

We introduce a method to learn a hierarchy of successively more abstract...

0 Greg Ver Steeg, et al. ∙

research

∙ 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...

0 Greg Ver Steeg, et al. ∙

research

∙ 06/14/2011

Co-evolution of Selection and Influence in Social Networks

Many networks are complex dynamical systems, where both attributes of no...

0 Yoon-Sik Cho, et al. ∙

research

∙ 06/08/2011

A Sequence of Relaxations Constraining Hidden Variable Models

Many widely studied graphical models with latent variables lead to nontr...

0 Greg Ver Steeg, et al. ∙

Greg Ver Steeg

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