Search Results for author: Aram Galstyan

Found 102 papers, 47 papers with code

All in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference

no code implementations ICML 2020 Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan

While the Evidence Lower Bound (ELBO) has become a ubiquitous objective for variational inference, the recently proposed Thermodynamic Variational Objective (TVO) leverages thermodynamic integration to provide a tighter and more general family of bounds.

Scheduling Variational Inference

Temporal Generalization for Spoken Language Understanding

no code implementations NAACL (ACL) 2022 Judith Gaspers, Anoop Kumar, Greg Ver Steeg, Aram Galstyan

Spoken Language Understanding (SLU) models in industry applications are usually trained offline on historic data, but have to perform well on incoming user requests after deployment.

Domain Generalization Spoken Language Understanding

FLIRT: Feedback Loop In-context Red Teaming

no code implementations8 Aug 2023 Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta

Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation.

Response Generation

Multi-Task Knowledge Enhancement for Zero-Shot and Multi-Domain Recommendation in an AI Assistant Application

no code implementations9 Jun 2023 Elan Markowitz, Ziyan Jiang, Fan Yang, Xing Fan, Tony Chen, Greg Ver Steeg, Aram Galstyan

We propose in this work to unify these approaches: Using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would be impossible with either information source alone.

Knowledge Graphs Recommendation Systems

Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning

1 code implementation30 May 2023 Umang Gupta, Aram Galstyan, Greg Ver Steeg

This can be a drawback for low-resource applications and training with differential-privacy constraints, where excessive noise may be introduced during finetuning.

Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models

no code implementations26 May 2023 Neal Lawton, Anoop Kumar, Govind Thattai, Aram Galstyan, Greg Ver Steeg

Parameter-efficient tuning (PET) methods fit pre-trained language models (PLMs) to downstream tasks by either computing a small compressed update for a subset of model parameters, or appending and fine-tuning a small number of new model parameters to the pre-trained network.

Neural Architecture Search

Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding

no code implementations23 May 2023 Zheng Chen, Ziyan Jiang, Fan Yang, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Aram Galstyan

This paper presents our "Collaborative Query Rewriting" approach, which specifically addresses the task of rewriting new user interactions that have not been previously observed in the user's history.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +9

Measuring and Mitigating Local Instability in Deep Neural Networks

no code implementations18 May 2023 Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan

We formulate the model stability problem by studying how the predictions of a model change, even when it is retrained on the same data, as a consequence of stochasticity in the training process.

Natural Language Understanding

ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems

1 code implementation12 May 2023 Sarik Ghazarian, Yijia Shao, Rujun Han, Aram Galstyan, Nanyun Peng

We take the first step by focusing on event commonsense that considers events and their relations, and is crucial in both dialogues and general commonsense reasoning.

Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations

no code implementations2 Nov 2022 Kuan-Hao Huang, Varun Iyer, Anoop Kumar, Sriram Venkatapathy, Kai-Wei Chang, Aram Galstyan

In this paper, we demonstrate that leveraging Abstract Meaning Representations (AMR) can greatly improve the performance of unsupervised syntactically controlled paraphrase generation.

Data Augmentation Paraphrase Generation

An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation

no code implementations7 Oct 2022 Jwala Dhamala, Varun Kumar, Rahul Gupta, Kai-Wei Chang, Aram Galstyan

We present a systematic analysis of the impact of decoding algorithms on LM fairness, and analyze the trade-off between fairness, diversity and quality.

Fairness Text Generation

Formal limitations of sample-wise information-theoretic generalization bounds

no code implementations13 May 2022 Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

Remarkably, PAC-Bayes, single-draw and expected squared generalization gap bounds that depend on information in pairs of examples exist.

Generalization Bounds

Robust Conversational Agents against Imperceptible Toxicity Triggers

1 code implementation NAACL 2022 Ninareh Mehrabi, Ahmad Beirami, Fred Morstatter, Aram Galstyan

Existing work to generate such attacks is either based on human-generated attacks which is costly and not scalable or, in case of automatic attacks, the attack vector does not conform to human-like language, which can be detected using a language model loss.

Language Modelling Text Generation

Inferring topological transitions in pattern-forming processes with self-supervised learning

1 code implementation19 Mar 2022 Marcin Abram, Keith Burghardt, Greg Ver Steeg, Aram Galstyan, Remi Dingreville

The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domains.

Self-Supervised Learning

DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations

1 code implementation ACL 2022 Sarik Ghazarian, Nuan Wen, Aram Galstyan, Nanyun Peng

We also show that DEAM can distinguish between coherent and incoherent dialogues generated by baseline manipulations, whereas those baseline models cannot detect incoherent examples generated by DEAM.

Coherence Evaluation Dialogue Evaluation

Implicit SVD for Graph Representation Learning

1 code implementation NeurIPS 2021 Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e. g., for calculating gradients via backprop over many data epochs.

Graph Representation Learning

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

1 code implementation NeurIPS 2021 Greg Ver Steeg, Aram Galstyan

Auxiliary neural models can learn to speed up MCMC, but the overhead for training the extra model can be prohibitive.

Cognitively Inspired Learning of Incremental Drifting Concepts

no code implementations9 Oct 2021 Mohammad Rostami, Aram Galstyan

Humans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences.

Continual Learning

Information-theoretic generalization bounds for black-box learning algorithms

1 code implementation NeurIPS 2021 Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan

We derive information-theoretic generalization bounds for supervised learning algorithms based on the information contained in predictions rather than in the output of the training algorithm.

Generalization Bounds

Partner-Assisted Learning for Few-Shot Image Classification

no code implementations ICCV 2021 Jiawei Ma, Hanchen Xie, Guangxing Han, Shih-Fu Chang, Aram Galstyan, Wael Abd-Almageed

In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples.

Classification Few-Shot Image Classification +1

Attributing Fair Decisions with Attention Interventions

1 code implementation NAACL (TrustNLP) 2022 Ninareh Mehrabi, Umang Gupta, Fred Morstatter, Greg Ver Steeg, Aram Galstyan

The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods.

Decision Making Fairness

Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation

no code implementations4 Jul 2021 Mohammad Rostami, Aram Galstyan

We introduce a new domain adaptation method which induces large margins between different classes in an embedding space.

Domain Adaptation Marketing +1

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

1 code implementation1 Jul 2021 Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood

Many common machine learning methods involve the geometric annealing path, a sequence of intermediate densities between two distributions of interest constructed using the geometric average.

Bayesian Inference

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

1 code implementation NAACL 2021 Sarik Ghazarian, Zixi Liu, Akash SM, Ralph Weischedel, Aram Galstyan, Nanyun Peng

We propose to tackle these issues by generating a more comprehensive set of implausible stories using {\em plots}, which are structured representations of controllable factors used to generate stories.

Story Generation

Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources

no code implementations EMNLP 2021 Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, Aram Galstyan

In addition, we analyze two downstream models that use ConceptNet as a source for commonsense knowledge and find the existence of biases in those models as well.

Fast Graph Learning with Unique Optimal Solutions

1 code implementation ICLR Workshop GTRL 2021 Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction.

Graph Learning Graph Representation Learning +3

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

1 code implementation ICLR 2021 Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan

We propose Graph Traversal via Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs.

Graph Representation Learning

Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis

no code implementations1 Jan 2021 Mohammad Rostami, Aram Galstyan

Large margins in the source domain help to reduce the effect of ``domain shift'' on the performance of a trained classifier in the target domain.

Domain Adaptation Marketing +1

Likelihood Ratio Exponential Families

no code implementations NeurIPS Workshop DL-IG 2020 Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Ver Steeg

The exponential family is well known in machine learning and statistical physics as the maximum entropy distribution subject to a set of observed constraints, while the geometric mixture path is common in MCMC methods such as annealed importance sampling.


Exacerbating Algorithmic Bias through Fairness Attacks

1 code implementation16 Dec 2020 Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, Aram Galstyan

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms.

Adversarial Attack BIG-bench Machine Learning +2

Annealed Importance Sampling with q-Paths

2 code implementations NeurIPS Workshop DL-IG 2020 Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen

Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distributions between a tractable base and an unnormalized target.

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

no code implementations30 Nov 2020 Hanchen Xie, Mohamed E. Hussein, Aram Galstyan, Wael Abd-Almageed

We also show that MUSCLE has the potential to boost the classification performance when used in the fine-tuning phase for a model pre-trained only on unlabeled data.

One-shot Learning for Temporal Knowledge Graphs

no code implementations AKBC 2021 Mehrnoosh Mirtaheri, Mohammad Rostami, Xiang Ren, Fred Morstatter, Aram Galstyan

Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times.

Knowledge Graphs Link Prediction +1

Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms

no code implementations4 Sep 2020 Akira Matsui, Emilio Ferrara, Fred Morstatter, Andres Abeliuk, Aram Galstyan

In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories.

Autonomous Driving Clustering

Compressing Deep Neural Networks via Layer Fusion

no code implementations29 Jul 2020 James O' Neill, Greg Ver Steeg, Aram Galstyan

This paper proposes \textit{layer fusion} - a model compression technique that discovers which weights to combine and then fuses weights of similar fully-connected, convolutional and attention layers.

Exponential degradation Language Modelling +1

Robust Classification under Class-Dependent Domain Shift

no code implementations10 Jul 2020 Tigran Galstyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan

Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research.

Classification General Classification +1

Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions

1 code implementation1 Jul 2020 Mohammad Rostami, Aram Galstyan

We develop an algorithm to improve the performance of a pre-trained model under concept shift without retraining the model from scratch when only unannotated samples of initial concepts are accessible.

Transfer Learning Unsupervised Domain Adaptation

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

1 code implementation1 Jul 2020 Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan

We propose to choose intermediate distributions using equal spacing in the moment parameters of our exponential family, which matches grid search performance and allows the schedule to adaptively update over the course of training.

Variational Inference

A Metric Space for Point Process Excitations

no code implementations5 May 2020 Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan

A multivariate Hawkes process enables self- and cross-excitations through a triggering matrix that behaves like an asymmetrical covariance structure, characterizing pairwise interactions between the event types.

Point Processes

ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data

no code implementations ACL 2021 Woojeong Jin, Rahul Khanna, Suji Kim, Dong-Ho Lee, Fred Morstatter, Aram Galstyan, Xiang Ren

In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.

Knowledge Graphs Language Modelling +5

Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks

no code implementations9 Feb 2020 Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan

We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks - functional algorithm classification and vulnerability discovery.

Vulnerability Detection

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

2 code implementations4 Nov 2019 Sarik Ghazarian, Ralph Weischedel, Aram Galstyan, Nanyun Peng

In this paper, we investigate the possibility and efficacy of estimating utterance-level engagement and define a novel metric, {\em predictive engagement}, for automatic evaluation of open-domain dialogue systems.

Dialogue Evaluation

Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction

no code implementations IJCNLP 2019 Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo Cecchi

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.

Relation Extraction

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

1 code implementation24 Oct 2019 Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, Aram Galstyan

We study the bias in several state-of-the-art named entity recognition (NER) models---specifically, a difference in the ability to recognize male and female names as PERSON entity types.

named-entity-recognition Named Entity Recognition +1

Stacking Models for Nearly Optimal Link Prediction in Complex Networks

2 code implementations17 Sep 2019 Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan, Edoardo M. Airoldi, Aaron Clauset

These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions.

Link Prediction Meta-Learning

Nearly-Unsupervised Hashcode Representations for Relation Extraction

no code implementations9 Sep 2019 Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo Cecchi

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.

Relation Extraction

A Survey on Bias and Fairness in Machine Learning

2 code implementations23 Aug 2019 Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan

With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them.

BIG-bench Machine Learning Fairness

BioRelEx 1.0: Biological Relation Extraction Benchmark

1 code implementation WS 2019 Hrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, Aram Galstyan

Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general.

Relation Extraction

Efficient Covariance Estimation from Temporal Data

2 code implementations30 May 2019 Hrayr Harutyunyan, Daniel Moyer, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan

Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis.

Time Series Time Series Analysis

Exact Rate-Distortion in Autoencoders via Echo Noise

1 code implementation NeurIPS 2019 Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg

The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions.

Representation Learning

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

1 code implementation4 Feb 2019 Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg, Aram Galstyan

Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump.

Invariant Representations without Adversarial Training

1 code implementation NeurIPS 2018 Daniel Moyer, Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan

Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation.

Representation Learning

A Forest Mixture Bound for Block-Free Parallel Inference

no code implementations17 May 2018 Neal Lawton, Aram Galstyan, Greg Ver Steeg

Coordinate ascent variational inference is an important algorithm for inference in probabilistic models, but it is slow because it updates only a single variable at a time.

Variational Inference

Capturing Edge Attributes via Network Embedding

1 code implementation8 May 2018 Palash Goyal, Homa Hosseinmardi, Emilio Ferrara, Aram Galstyan

Here we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations.

Social and Information Networks

Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach

no code implementations26 Apr 2018 Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan

We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.

Dialogue Generation Model Selection

Auto-Encoding Total Correlation Explanation

no code implementations16 Feb 2018 Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan

Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned.


Stochastic Learning of Nonstationary Kernels for Natural Language Modeling

no code implementations11 Jan 2018 Sahil Garg, Greg Ver Steeg, Aram Galstyan

Natural language processing often involves computations with semantic or syntactic graphs to facilitate sophisticated reasoning based on structural relationships.

Language Modelling

Kernelized Hashcode Representations for Relation Extraction

1 code implementation10 Nov 2017 Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo Cecchi, Shuyang Gao

Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.

General Classification Relation Extraction

Disentangled Representations via Synergy Minimization

1 code implementation10 Oct 2017 Greg Ver Steeg, Rob Brekelmans, Hrayr Harutyunyan, Aram Galstyan

Scientists often seek simplified representations of complex systems to facilitate prediction and understanding.

Unifying Local and Global Change Detection in Dynamic Networks

no code implementations9 Oct 2017 Wenzhe Li, Dong Guo, Greg Ver Steeg, Aram Galstyan

Many real-world networks are complex dynamical systems, where both local (e. g., changing node attributes) and global (e. g., changing network topology) processes unfold over time.

Change Detection

Fast structure learning with modular regularization

3 code implementations NeurIPS 2019 Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan

We also use our approach for estimating covariance structure for a number of real-world datasets and show that it consistently outperforms state-of-the-art estimators at a fraction of the computational cost.

Toward Interpretable Topic Discovery via Anchored Correlation Explanation

no code implementations22 Jun 2016 Kyle Reing, David C. Kale, Greg Ver Steeg, Aram Galstyan

Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts.

Variational Information Maximization for Feature Selection

1 code implementation NeurIPS 2016 Shuyang Gao, Greg Ver Steeg, Aram Galstyan

We demonstrate that approximations made by existing methods are based on unrealistic assumptions.

feature selection

Sifting Common Information from Many Variables

1 code implementation7 Jun 2016 Greg Ver Steeg, Shuyang Gao, Kyle Reing, Aram Galstyan

Measuring the relationship between any pair of variables is a rich and active area of research that is central to scientific practice.

Dimensionality Reduction

Predicting online extremism, content adopters, and interaction reciprocity

no code implementations2 May 2016 Emilio Ferrara, Wen-Qiang Wang, Onur Varol, Alessandro Flammini, Aram Galstyan

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media.

The DARPA Twitter Bot Challenge

no code implementations20 Jan 2016 V. S. Subrahmanian, Amos Azaria, Skylar Durst, Vadim Kagan, Aram Galstyan, Kristina Lerman, Linhong Zhu, Emilio Ferrara, Alessandro Flammini, Filippo Menczer, Andrew Stevens, Alexander Dekhtyar, Shuyang Gao, Tad Hogg, Farshad Kooti, Yan Liu, Onur Varol, Prashant Shiralkar, Vinod Vydiswaran, Qiaozhu Mei, Tim Hwang

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.

Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text

1 code implementation4 Dec 2015 Sahil Garg, Aram Galstyan, Ulf Hermjakob, Daniel Marcu

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations.

Semantic Parsing

Latent Space Model for Multi-Modal Social Data

no code implementations18 Oct 2015 Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan

We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information.

The Information Sieve

2 code implementations8 Jul 2015 Greg Ver Steeg, Aram Galstyan

Each layer of the sieve recovers a single latent factor that is maximally informative about multivariate dependence in the data.

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

no code implementations1 Dec 2014 Shuyang Gao, Greg Ver Steeg, Aram Galstyan

We revisit some of the previous studies that reported strong signatures of stylistic accommodation, and find that a significant part of the observed coordination can be attributed to a simple confounding effect - length coordination.

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

no code implementations13 Nov 2014 Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots.

Link Prediction

Efficient Estimation of Mutual Information for Strongly Dependent Variables

4 code implementations7 Nov 2014 Shuyang Gao, Greg Ver Steeg, Aram Galstyan

We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI.

Active Inference for Binary Symmetric Hidden Markov Models

no code implementations3 Nov 2014 Armen E. Allahverdyan, Aram Galstyan

We consider active maximum a posteriori (MAP) inference problem for Hidden Markov Models (HMM), where, given an initial MAP estimate of the hidden sequence, we select to label certain states in the sequence to improve the estimation accuracy of the remaining states.

Discovering Structure in High-Dimensional Data Through Correlation Explanation

3 code implementations NeurIPS 2014 Greg Ver Steeg, Aram Galstyan

We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective.

Vocal Bursts Intensity Prediction

Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media

no code implementations24 Feb 2014 Linhong Zhu, Aram Galstyan, James Cheng, Kristina Lerman

We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data.

Clustering Graph Clustering +1

Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs

no code implementations NeurIPS 2011 Armen E. Allahverdyan, Aram Galstyan

We present an asymptotic analysis of Viterbi Training (VT) and contrast it with a more conventional Maximum Likelihood (ML) approach to parameter estimation in Hidden Markov Models.

Phase Transitions in Community Detection: A Solvable Toy Model

no code implementations2 Dec 2013 Greg Ver Steeg, Cristopher Moore, Aram Galstyan, Armen E. Allahverdyan

It predicts a first-order detectability transition whenever $q > 2$, while the finite-temperature cavity method shows that this is the case only when $q > 4$.

Community Detection

Demystifying Information-Theoretic Clustering

no code implementations15 Oct 2013 Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo

We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions.


Coevolutionary networks of reinforcement-learning agents

no code implementations5 Aug 2013 Ardeshir Kianercy, Aram Galstyan

This paper presents a model of network formation in repeated games where the players adapt their strategies and network ties simultaneously using a simple reinforcement-learning scheme.

reinforcement-learning Reinforcement Learning (RL)

Latent Self-Exciting Point Process Model for Spatial-Temporal Networks

no code implementations12 Feb 2013 Yoon-Sik Cho, Aram Galstyan, P. Jeffrey Brantingham, George Tita

We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities.

Information-Theoretic Measures of Influence Based on Content Dynamics

3 code implementations22 Aug 2012 Greg Ver Steeg, Aram Galstyan

The fundamental building block of social influence is for one person to elicit a response in another.

Social and Information Networks Physics and Society Applications

Information Transfer in Social Media

1 code implementation12 Oct 2011 Greg Ver Steeg, Aram Galstyan

Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals.

Social and Information Networks Physics and Society Applications

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