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In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such as processing power and execution time limits, or…

Machine Learning · Computer Science 2021-06-22 Nathan Dahlin , Krishna Chaitanya Kalagarla , Nikhil Naik , Rahul Jain , Pierluigi Nuzzo

Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, from modelling routes on maps to approving loan predictions.…

Machine Learning · Computer Science 2020-01-31 Ioannis Papantonis , Vaishak Belle

Concurrency control (CC) algorithms are important in modern transactional databases, as they enable high performance by executing transactions concurrently while ensuring correctness. However, state-of-the-art CC algorithms struggle to…

Databases · Computer Science 2026-03-11 Hexiang Pan , Shaofeng Cai , Tien Tuan Anh Dinh , Yuncheng Wu , Yeow Meng Chee , Gang Chen , Beng Chin Ooi

When dealing with real-world optimization problems, decision-makers usually face high levels of uncertainty associated with partial information, unknown parameters, or complex relationships between these and the problem decision variables.…

Optimization and Control · Mathematics 2023-05-01 Antonio Alcántara , Carlos Ruiz

Autonomous robots need to be able to adapt to unforeseen situations and to acquire new skills through trial and error. Reinforcement learning in principle offers a suitable methodological framework for this kind of autonomous learning.…

Robotics · Computer Science 2016-08-02 Nikolas J. Hemion

Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Mattia Carletti , Matteo Terzi , Gian Antonio Susto

Representation learning is a widely adopted framework for learning in data-scarce environments, aiming to extract common features from related tasks. While centralized approaches have been extensively studied, decentralized methods remain…

Machine Learning · Computer Science 2025-12-30 Donghwa Kang , Shana Moothedath

It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to…

Machine Learning · Computer Science 2018-02-14 Angus Galloway , Graham W. Taylor , Medhat Moussa

An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of…

Machine Learning · Computer Science 2024-10-01 Daniel N. Nissani

We propose Trusted Neural Network (TNN) models, which are deep neural network models that satisfy safety constraints critical to the application domain. We investigate different mechanisms for incorporating rule-based knowledge in the form…

Machine Learning · Computer Science 2018-05-21 Shalini Ghosh , Amaury Mercier , Dheeraj Pichapati , Susmit Jha , Vinod Yegneswaran , Patrick Lincoln

We introduce the framework of "social learning" in the context of large language models (LLMs), whereby models share knowledge with each other in a privacy-aware manner using natural language. We present and evaluate two approaches for…

Machine Learning · Computer Science 2024-02-09 Amirkeivan Mohtashami , Florian Hartmann , Sian Gooding , Lukas Zilka , Matt Sharifi , Blaise Aguera y Arcas

Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. Despite its numerous empirical successes, theoretical analysis for transfer learning…

Machine Learning · Computer Science 2023-01-30 Haoyang Cao , Haotian Gu , Xin Guo , Mathieu Rosenbaum

We investigate sample-based learning of conditional distributions on multi-dimensional unit boxes, allowing for different dimensions of the feature and target spaces. Our approach involves clustering data near varying query points in the…

Machine Learning · Statistics 2024-06-14 Cyril Bénézet , Ziteng Cheng , Sebastian Jaimungal

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they maintain their effectiveness even against other models. With great efforts delved into the…

Machine Learning · Computer Science 2019-05-10 Yunhan Jia , Yantao Lu , Senem Velipasalar , Zhenyu Zhong , Tao Wei

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to…

Machine Learning · Computer Science 2018-01-16 Bo Luo , Yannan Liu , Lingxiao Wei , Qiang Xu

As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world,…

Machine Learning · Statistics 2023-10-19 Shide Du , Zihan Fang , Shiyang Lan , Yanchao Tan , Manuel Günther , Shiping Wang , Wenzhong Guo

Cross-validation techniques for risk estimation and model selection are widely used in statistics and machine learning. However, the understanding of the theoretical properties of learning via model selection with cross-validation risk…

Machine Learning · Statistics 2024-05-27 Diego Marcondes , Cláudia Peixoto

Learning curves model a classifier's test error as a function of the number of training samples. Prior works show that learning curves can be used to select model parameters and extrapolate performance. We investigate how to use learning…

Machine Learning · Computer Science 2021-04-06 Derek Hoiem , Tanmay Gupta , Zhizhong Li , Michal M. Shlapentokh-Rothman

Sequential decision making techniques hold great promise to improve the performance of many real-world systems, but computational complexity hampers their principled application. Influence-based abstraction aims to gain leverage by modeling…

Artificial Intelligence · Computer Science 2021-02-24 Elena Congeduti , Alexander Mey , Frans A. Oliehoek

Deep learning networks excel at classification, yet identifying minimal architectures that reliably solve a task remains challenging. We present a computational methodology for systematically exploring and analyzing the relationships among…

Machine Learning · Computer Science 2026-01-27 Ziwei Zheng , Huizhi Liang , Vaclav Snasel , Vito Latora , Panos Pardalos , Giuseppe Nicosia , Varun Ojha
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