b'Aram Galstyan'

research

∙ 03/25/2022

On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations

Multiple metrics have been introduced to measure fairness in various nat...

Yang Trista Cao, et al. ∙

research

∙ 03/18/2022

DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations

Automatic evaluation metrics are essential for the rapid development of ...

Sarik Ghazarian, et al. ∙

research

∙ 11/03/2021

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

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

Greg Ver Steeg, et al. ∙

research

∙ 10/09/2021

Cognitively Inspired Learning of Incremental Drifting Concepts

Humans continually expand their learned knowledge to new domains and lea...

Mohammad Rostami, et al. ∙

research

∙ 10/04/2021

Information-theoretic generalization bounds for black-box learning algorithms

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

Hrayr Harutyunyan, et al. ∙

research

∙ 07/04/2021

Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation

Sentiment analysis is a costly yet necessary task for enterprises to stu...

Mohammad Rostami, 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...

Vaden Masrani, et al. ∙

research

∙ 04/12/2021

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

With the recent advances of open-domain story generation, the lack of re...

Sarik Ghazarian, et al. ∙

research

∙ 02/17/2021

Fast Graph Learning with Unique Optimal Solutions

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

Sami Abu-El-Haija, 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...

Elan Markowitz, et al. ∙

research

∙ 02/03/2021

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

Having engaging and informative conversations with users is the utmost g...

Sarik Ghazarian, et al. ∙

research

∙ 12/31/2020

Likelihood Ratio Exponential Families

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

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...

Rob Brekelmans, et al. ∙

research

∙ 11/30/2020

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

Deep neural networks are powerful, massively parameterized machine learn...

Hanchen Xie, et al. ∙

research

∙ 07/29/2020

Compressing Deep Neural Networks via Layer Fusion

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

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...

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...

Rob Brekelmans, et al. ∙

research

∙ 07/01/2020

Sequential Unsupervised Domain Adaptation through Prototypical Distributions

We develop an algorithm for unsupervised domain adaptation (UDA) of a cl...

Mohammad Rostami, et al. ∙

research

∙ 05/05/2020

Event Cartography: Latent Point Process Embeddings

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

Myrl G. Marmarelis, et al. ∙

research

∙ 03/03/2020

Anchor Attention for Hybrid Crowd Forecasts Aggregation

Forecasting the future is a notoriously difficult task. To overcome this...

Yuzhong Huang, 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...

Hrayr Harutyunyan, et al. ∙

research

∙ 02/09/2020

Towards Learning Representations of Binary Executable Files for Security Tasks

Tackling binary analysis problems has traditionally implied manually def...

Shushan Arakelyan, et al. ∙

research

∙ 11/04/2019

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-...

Sarik Ghazarian, et al. ∙

research

∙ 10/24/2019

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 (...

Ninareh Mehrabi, et al. ∙

research

∙ 09/22/2019

Deep Structured Neural Network for Event Temporal Relation Extraction

We propose a novel deep structured learning framework for event temporal...

Rujun Han, et al. ∙

research

∙ 09/17/2019

Stacking Models for Nearly Optimal Link Prediction in Complex Networks

Most real-world networks are incompletely observed. Algorithms that can ...

Amir Ghasemian, et al. ∙

research

∙ 09/09/2019

Nearly-Unsupervised Hashcode Representations for Relation Extraction

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

Sahil Garg, et al. ∙

research

∙ 08/23/2019

A Survey on Bias and Fairness in Machine Learning

With the widespread use of AI systems and applications in our everyday l...

Ninareh Mehrabi, et al. ∙

research

∙ 05/30/2019

Efficient Covariance Estimation from Temporal Data

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

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 ...

Sami Abu-El-Haija, et al. ∙

research

∙ 04/24/2019

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

Despite advances in open-domain dialogue systems, automatic evaluation o...

Sarik Ghazarian, et al. ∙

research

∙ 04/15/2019

Exact Rate-Distortion in Autoencoders via Echo Noise

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

Rob Brekelmans, et al. ∙

research

∙ 02/04/2019

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

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

Mehrnoosh Mirtaheri, et al. ∙

research

∙ 11/27/2018

Maximizing Multivariate Information with Error-Correcting Codes

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

Kyle Reing, et al. ∙

research

∙ 05/24/2018

Evading the Adversary in Invariant Representation

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

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...

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...

Sahil Garg, et al. ∙

research

∙ 03/18/2018

Adaptive prior probabilities via optimization of risk and entropy

An agent choosing between various actions tends to take the one with the...

Armen E. Allahverdyan, et al. ∙

research

∙ 02/16/2018

Auto-Encoding Total Correlation Explanation

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

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...

Sahil Garg, et al. ∙

research

∙ 11/10/2017

Efficient Representation for Natural Language Processing via Kernelized Hashcodes

Kernel similarity functions have been successfully applied in classifica...

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...

Wenzhe Li, 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...

Greg Ver Steeg, et al. ∙

research

∙ 03/22/2017

Multitask Learning and Benchmarking with Clinical Time Series Data

Health care is one of the most exciting frontiers in data mining and mac...

Hrayr Harutyunyan, 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...

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...

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...

Greg Ver Steeg, et al. ∙

research

∙ 01/20/2016

The DARPA Twitter Bot Challenge

A number of organizations ranging from terrorist groups such as ISIS to ...

V. S. Subrahmanian, et al. ∙

research

∙ 12/04/2015

Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text

We advance the state of the art in biomolecular interaction extraction w...

Sahil Garg, et al. ∙

research

∙ 07/08/2015

The Information Sieve

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

Greg Ver Steeg, et al. ∙

Aram Galstyan

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