363 citations · 485 across the 18 of their papers we have counts for
20 papers
Improving Neural Ranking Models with Traditional IR Methods
Anik Saha, Oktie Hassanzadeh, Alex Gittens +3
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercia…
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Anik Saha, Oktie Hassanzadeh, Alex Gittens +3
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understan…
Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning
Deniz Koyuncu, Alex Gittens, Bülent Yener +1
Inference of causal structures from observational data is a key component of causal machine learning; in practice, this data may be incompletely observed. Prior work has demonstrat…
Word Sense Induction with Knowledge Distillation from BERT
Anik Saha, Alex Gittens, Bulent Yener
Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embedding…
Anti-Malware Sandbox Games
Sujoy Sikdar, Sikai Ruan, Qishen Han +4
We develop a game theoretic model of malware protection using the state-of-the-art sandbox method, to characterize and compute optimal defense strategies for anti-malware. We model…
Extending Model-x Framework to Missing Data
Deniz Koyuncu, Alex Gittens, Bülent Yener
One limitation of the most statistical/machine learning-based variable selection approaches is their inability to control the false selections. A recently introduced framework, mod…
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models
Daniel Park, Haidar Khan, Azer Khan +2
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model in a "white box" s…
The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective
Arun Baskaran, Elizabeth J. Kautz, Aritra Chowdhary +3
The recent surge in the adoption of machine learning techniques for materials design, discovery, and characterization has resulted in an increased interest and application of Image…
Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording
Zaid Bin Tariq, Arun Iyengar, Lara Marcuse +2
Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research…
A survey on practical adversarial examples for malware classifiers
Daniel Park, Bülent Yener
Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neur…
Towards Obfuscated Malware Detection for Low Powered IoT Devices
Daniel Park, Hannah Powers, Benji Prashker +2
With the increased deployment of IoT and edge devices into commercial and user networks, these devices have become a new threat vector for malware authors. It is imperative to prot…
Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships
Wufei Ma, Elizabeth Kautz, Arun Baskaran +4
We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium-molybdenum) that…
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Elizabeth Kautz, Wufei Ma, Saumyadeep Jana +4
Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously dem…
Deep density ratio estimation for change point detection
Haidar Khan, Lara Marcuse, Bülent Yener
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use…
Thwarting finite difference adversarial attacks with output randomization
Haidar Khan, Daniel Park, Azer Khan +1
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model and to the opposit…
Generation & Evaluation of Adversarial Examples for Malware Obfuscation
Daniel Park, Haidar Khan, Bülent Yener
There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to advers…
Quantifying error contributions of computational steps, algorithms and hyperparameter choices in image classification pipelines
Aritra Chowdhury, Malik Magdin-Ismail, Bulent Yener
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for perfor…
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines
Aritra Chowdhury, Malik Magdon-Ismail, Bulent Yener
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for perfor…
Focal onset seizure prediction using convolutional networks
Haidar Khan, Lara Marcuse, Madeline Fields +2
Objective: This work investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distingui…
On the Security of Key Extraction from Measuring Physical Quantities
Matt Edman, Aggelos Kiayias, Qiang Tang +1
Key extraction via measuring a physical quantity is a class of information theoretic key exchange protocols that rely on the physical characteristics of the communication channel t…