Aram Galstyan

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USC Information Sciences Institute

Director of the Machine Intelligence and Data Science (MINDS) group at Information Sciences Institute, University of Southern California, Research Associate Professor at the USC Computer Science department, Project Leader of Co-PI for IARPA’s Mercury program and Information Sciences Institute USC, Leader of PI DARPA SMISC project,

Featured Co-authors

Yan Liu
78 publications
Frank Wood
64 publications
Frank Nielsen
61 publications
Nanyun Peng
55 publications
Emilio Ferrara
48 publications
Kristina Lerman
46 publications
Greg Ver Steeg
44 publications
Irina Rish
29 publications
Qiaozhu Mei
24 publications
Sahil Garg
23 publications
Filippo Menczer
23 publications

Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation

With the recent advances of open-domain story generation, the lack of re...
04/12/2021 ∙ by Sarik Ghazarian, et al. ∙ 3 ∙ share

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Fast Graph Learning with Unique Optimal Solutions

Graph Representation Learning (GRL) has been advancing at an unprecedent...
02/17/2021 ∙ by Sami Abu-El-Haija, et al. ∙ 23 ∙ share

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

Graph Representation Learning (GRL) methods have impacted fields from ch...
02/08/2021 ∙ by Elan Markowitz, et al. ∙ 11 ∙ share

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DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

Having engaging and informative conversations with users is the utmost g...
02/03/2021 ∙ by Sarik Ghazarian, et al. ∙ 10 ∙ share

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Likelihood Ratio Exponential Families

The exponential family is well known in machine learning and statistical...
12/31/2020 ∙ by Rob Brekelmans, et al. ∙ 12 ∙ share

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Annealed Importance Sampling with q-Paths

Annealed importance sampling (AIS) is the gold standard for estimating p...
12/14/2020 ∙ by Rob Brekelmans, et al. ∙ 8 ∙ share

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MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization

Deep neural networks are powerful, massively parameterized machine learn...
11/30/2020 ∙ by Hanchen Xie, et al. ∙ 5 ∙ share

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Compressing Deep Neural Networks via Layer Fusion

This paper proposes layer fusion - a model compression technique that di...
07/29/2020 ∙ by James O'Neill, et al. ∙ 18 ∙ share

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Robust Classification under Class-Dependent Domain Shift

Investigation of machine learning algorithms robust to changes between t...
07/10/2020 ∙ by Tigran Galstyan, et al. ∙ 10 ∙ share

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

The recently proposed Thermodynamic Variational Objective (TVO) leverage...
07/01/2020 ∙ by Rob Brekelmans, et al. ∙ 9 ∙ share

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Sequential Unsupervised Domain Adaptation through Prototypical Distributions

We develop an algorithm for unsupervised domain adaptation (UDA) of a cl...
07/01/2020 ∙ by Mohammad Rostami, et al. ∙ 19 ∙ share

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Event Cartography: Latent Point Process Embeddings

Many important phenomena arise naturally as temporal point processes wit...
05/05/2020 ∙ by Myrl G. Marmarelis, et al. ∙ 10 ∙ share

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Anchor Attention for Hybrid Crowd Forecasts Aggregation

Forecasting the future is a notoriously difficult task. To overcome this...
03/03/2020 ∙ by Yuzhong Huang, et al. ∙ 0 ∙ share

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Improving Generalization by Controlling Label-Noise Information in Neural Network Weights

In the presence of noisy or incorrect labels, neural networks have the u...
02/19/2020 ∙ by Hrayr Harutyunyan, et al. ∙ 23 ∙ share

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Towards Learning Representations of Binary Executable Files for Security Tasks

Tackling binary analysis problems has traditionally implied manually def...
02/09/2020 ∙ by Shushan Arakelyan, et al. ∙ 10 ∙ share

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Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems

User engagement is a critical metric for evaluating the quality of open-...
11/04/2019 ∙ by Sarik Ghazarian, et al. ∙ 0 ∙ share

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Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition

We study the bias in several state-of-the-art named entity recognition (...
10/24/2019 ∙ by Ninareh Mehrabi, et al. ∙ 1 ∙ share

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Deep Structured Neural Network for Event Temporal Relation Extraction

We propose a novel deep structured learning framework for event temporal...
09/22/2019 ∙ by Rujun Han, et al. ∙ 0 ∙ share

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Stacking Models for Nearly Optimal Link Prediction in Complex Networks

Most real-world networks are incompletely observed. Algorithms that can ...
09/17/2019 ∙ by Amir Ghasemian, et al. ∙ 16 ∙ share

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Nearly-Unsupervised Hashcode Representations for Relation Extraction

Recently, kernelized locality sensitive hashcodes have been successfully...
09/09/2019 ∙ by Sahil Garg, et al. ∙ 4 ∙ share

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A Survey on Bias and Fairness in Machine Learning

With the widespread use of AI systems and applications in our everyday l...
08/23/2019 ∙ by Ninareh Mehrabi, et al. ∙ 1 ∙ share

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Efficient Covariance Estimation from Temporal Data

Estimating the covariance structure of multivariate time series is a fun...
05/30/2019 ∙ by Hrayr Harutyunyan, et al. ∙ 3 ∙ share

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

Existing popular methods for semi-supervised learning with Graph Neural ...
04/30/2019 ∙ by Sami Abu-El-Haija, et al. ∙ 10 ∙ share

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Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings

Despite advances in open-domain dialogue systems, automatic evaluation o...
04/24/2019 ∙ by Sarik Ghazarian, et al. ∙ 0 ∙ share

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Exact Rate-Distortion in Autoencoders via Echo Noise

Compression is at the heart of effective representation learning. Howeve...
04/15/2019 ∙ by Rob Brekelmans, et al. ∙ 36 ∙ share

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Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Interest surrounding cryptocurrencies, digital or virtual currencies tha...
02/04/2019 ∙ by Mehrnoosh Mirtaheri, et al. ∙ 12 ∙ share

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Maximizing Multivariate Information with Error-Correcting Codes

Multivariate mutual information provides a conceptual framework for char...
11/27/2018 ∙ by Kyle Reing, et al. ∙ 0 ∙ share

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Evading the Adversary in Invariant Representation

Representations of data that are invariant to changes in specified nuisa...
05/24/2018 ∙ by Daniel Moyer, et al. ∙ 0 ∙ share

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A Forest Mixture Bound for Block-Free Parallel Inference

Coordinate ascent variational inference is an important algorithm for in...
05/17/2018 ∙ by Neal Lawton, et al. ∙ 0 ∙ share

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Dialogue Modeling Via Hash Functions: Applications to Psychotherapy

We propose a novel machine-learning framework for dialogue modeling whic...
04/26/2018 ∙ by Sahil Garg, et al. ∙ 0 ∙ share

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Adaptive prior probabilities via optimization of risk and entropy

An agent choosing between various actions tends to take the one with the...
03/18/2018 ∙ by Armen E. Allahverdyan, et al. ∙ 0 ∙ share

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Auto-Encoding Total Correlation Explanation

Advances in unsupervised learning enable reconstruction and generation o...
02/16/2018 ∙ by Shuyang Gao, et al. ∙ 0 ∙ share

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Stochastic Learning of Nonstationary Kernels for Natural Language Modeling

Natural language processing often involves computations with semantic or...
01/11/2018 ∙ by Sahil Garg, et al. ∙ 0 ∙ share

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Efficient Representation for Natural Language Processing via Kernelized Hashcodes

Kernel similarity functions have been successfully applied in classifica...
11/10/2017 ∙ by Sahil Garg, et al. ∙ 0 ∙ share

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Unifying Local and Global Change Detection in Dynamic Networks

Many real-world networks are complex dynamical systems, where both local...
10/09/2017 ∙ by Wenzhe Li, et al. ∙ 0 ∙ share

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Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality

Learning the structure of graphical models from data is a fundamental pr...
06/11/2017 ∙ by Greg Ver Steeg, et al. ∙ 0 ∙ share

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Multitask Learning and Benchmarking with Clinical Time Series Data

Health care is one of the most exciting frontiers in data mining and mac...
03/22/2017 ∙ by Hrayr Harutyunyan, et al. ∙ 0 ∙ share

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Toward Interpretable Topic Discovery via Anchored Correlation Explanation

Many predictive tasks, such as diagnosing a patient based on their medic...
06/22/2016 ∙ by Kyle Reing, et al. ∙ 0 ∙ share

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Variational Information Maximization for Feature Selection

Feature selection is one of the most fundamental problems in machine lea...
06/09/2016 ∙ by Shuyang Gao, et al. ∙ 0 ∙ share

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Sifting Common Information from Many Variables

Measuring the relationship between any pair of variables is a rich and a...
06/07/2016 ∙ by Greg Ver Steeg, et al. ∙ 0 ∙ share

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The DARPA Twitter Bot Challenge

A number of organizations ranging from terrorist groups such as ISIS to ...
01/20/2016 ∙ by V. S. Subrahmanian, et al. ∙ 0 ∙ share

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Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text

We advance the state of the art in biomolecular interaction extraction w...
12/04/2015 ∙ by Sahil Garg, et al. ∙ 0 ∙ share

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The Information Sieve

We introduce a new framework for unsupervised learning of representation...
07/08/2015 ∙ by Greg Ver Steeg, et al. ∙ 0 ∙ share

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

We suggest an information-theoretic approach for measuring stylistic coo...
12/01/2014 ∙ by Shuyang Gao, et al. ∙ 0 ∙ share

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Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization

We propose a scalable temporal latent space model for link prediction in...
11/13/2014 ∙ by Linhong Zhu, et al. ∙ 0 ∙ share

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Efficient Estimation of Mutual Information for Strongly Dependent Variables

We demonstrate that a popular class of nonparametric mutual information ...
11/07/2014 ∙ by Shuyang Gao, et al. ∙ 0 ∙ share

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Active Inference for Binary Symmetric Hidden Markov Models

We consider active maximum a posteriori (MAP) inference problem for Hidd...
11/03/2014 ∙ by Armen E. Allahverdyan, et al. ∙ 0 ∙ share

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Maximally Informative Hierarchical Representations of High-Dimensional Data

We consider a set of probabilistic functions of some input variables as ...
10/27/2014 ∙ by Greg Ver Steeg, et al. ∙ 0 ∙ share

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Discovering Structure in High-Dimensional Data Through Correlation Explanation

We introduce a method to learn a hierarchy of successively more abstract...
06/04/2014 ∙ by Greg Ver Steeg, et al. ∙ 0 ∙ share

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Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs

We present an asymptotic analysis of Viterbi Training (VT) and contrast ...
12/16/2013 ∙ by Armen E. Allahverdyan, et al. ∙ 0 ∙ share

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