Publications
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2025
S Shekkizhar, R Cosentino, A Earle, S Savarese · arXiv Preprints, 2025
As large language model (LLM) based agents interact autonomously with one another, a new class of failures emerges that ...
R Cosentino, S Shekkizhar, A Earle · arXiv Preprints, 2025
We develop and analyze a theoretical framework for agent-to-agent interactions in a simplified in-context linear regress...
S Shekkizhar, R Cosentino · arXiv Preprints, 2025
This paper investigates multimodal agents, in particular, OpenAI's Computer-User Agent (CUA), trained to control an...
2024
A. Gulati, X. Dong, C. Hurtado, S. Shekkizhar, S. Swayamdipta, A. Ortega · Findings of the Association for Computational Linguistics: EMNLP, 2024
As language models become more general purpose, increased attention needs to be paid to detecting out-of-distribution (O...
R Cosentino, S Shekkizhar · arXiv Preprints, 2024
The advancement of large language models (LLMs) for real-world applications hinges critically on enhancing their reasoni...
R Balestriero, R Cosentino, S Shekkizhar · International Conference on Machine Learning (ICML), 2024
Large Language Models~(LLMs) drive current AI breakthroughs despite very little being known about their internal represe...
P. Das, S. Shekkizhar, A. Ortega · IEEE Open Journal of Signal Processing, 2024
Spatio-temporal graph convolutional networks (STGCNs) have emerged as a desirable model for many applications including ...
2023
S. Shekkizhar, N. Bulut, M. Farghal, S. Tavakkol, M. Bateni, A. Nandi · Mining and Learning with Graphs, Knowledge Discovery and Data Mining (KDD), 2023
Recent works, such as GRALE, have focused on the semi-supervised setting to learn an optimal similarity function for con...
S. Shekkizhar, A. Ortega · Graph Signal Processing Workshop 2023, 2023
Deep learning approaches have achieved unprecedented performance success in many application domains. In this work, we f...
2022
C. Hurtado, S. Shekkizhar, J. Ruiz-Hidalgo, A. Ortega · IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. O...
R. Cosentino, S. Shekkizhar, M. Soltanolkotabi, S. Avestimehr, A. Ortega · arXiv Preprints, 2022
The recent popularity of SSL has led to the development of several models that make use of diverse training strategies, ...
S. Shekkizhar, A. Ortega · IEEE 30th European Signal Processing Conference (EUSIPCO), 2022
An increasing number of systems are being designed by first gathering significant amounts of data, and then optimizing t...
D. Bonnet, A. Ortega, J.Ruiz-Hidalgo, S.Shekkizhar · IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult ...
2021
D. Bonnet, A. Ortega, J.Ruiz-Hidalgo, S.Shekkizhar · Asia Pacific Signal and Information Processing Association (APSIPA), 2021
Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple ...
S. Shekkizhar, A. Ortega · Asilomar Conference on Signals, Systems, and Computers, 2021
Modern machine learning systems based on neural networks have shown great success in learning complex data patterns whil...
S. Shekkizhar, A. Ortega · IEEE Data Science and Learning Workshop (DSLW), 2021
Several machine learning methods leverage the idea of locality by using $k$-nearest neighbor (KNN) techniques to design ...
2020
S. Shekkizhar, A. Ortega · IEEE International Conference on Image Processing (ICIP), 2020
Graphs are useful to interpret widely used image processing methods, e.g., bilateral filtering, or to develop new ones, ...
K. Nonaka, S. Shekkizhar, A. Ortega · IEEE International Workshop on Multimedia Signal Processing (MMSP), 2020
While deep learning is a powerful tool for manyapplications, there has been only limited research about selectionof data...
S. Shekkizhar, A. Ortega · arXiv Preprints, 2020
Modern machine learning systems based on neural networks have shown great success in learning complex data patterns whil...
S. Shekkizhar, A. Ortega · IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Data driven graph constructions are often used in machine learning applications. However, learning an optimal graph from...
2019
S. Shekkizhar, A. Ortega · arXiv, 2019
Data driven graph constructions are often used in various applications, including several machine learning tasks, where ...
2011
S. Deivalakshmi, S. Shekkizhar, P. Palanisamy · IEEE Recent Advances in Intelligent Computational Systems, 2011
A methodology based on median filters for the removal of Salt and Pepper noise by its detection followed by filtering in...