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Related papers: Monotonicity in practice of adaptive testing

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Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches toward decentralized learning disregard the interaction…

Machine Learning · Computer Science 2022-04-14 Yatin Dandi , Anastasia Koloskova , Martin Jaggi , Sebastian U. Stich

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty…

Systems and Control · Electrical Eng. & Systems 2024-03-29 Tansel Yucelen , Selahattin Burak Sarsilmaz , Emre Yildirim

We consider a nonparametric Bayesian approach to estimation and testing for a multivariate monotone density. Instead of following the conventional Bayesian route of putting a prior distribution complying with the monotonicity restriction,…

Statistics Theory · Mathematics 2023-06-09 Kang Wang , Subhashis Ghosal

In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchronous algorithms use an upper bound on the information…

Machine Learning · Computer Science 2022-04-12 Xuyang Wu , Sindri Magnusson , Hamid Reza Feyzmahdavian , Mikael Johansson

Bayesian methods have shown success in deep learning applications. For example, in predictive tasks, Bayesian neural networks leverage Bayesian reasoning of model uncertainty to improve the reliability and uncertainty awareness of deep…

Machine Learning · Computer Science 2025-10-21 Wenlong Chen , Bolian Li , Ruqi Zhang , Yingzhen Li

Randomized A/B comparisons of alternative pedagogical strategies or other course improvements could provide useful empirical evidence for instructor decision-making. However, traditional experiments do not provide a straightforward pathway…

Human-Computer Interaction · Computer Science 2024-06-10 Ilya Musabirov , Angela Zavaleta-Bernuy , Pan Chen , Michael Liut , Joseph Jay Williams

A number of machine learning algorithms are using a metric, or a distance, in order to compare individuals. The Euclidean distance is usually employed, but it may be more efficient to learn a parametric distance such as Mahalanobis metric.…

Machine Learning · Computer Science 2016-12-16 Hoel Le Capitaine

We study active structure learning of Bayesian networks in an observational setting, in which there are external limitations on the number of variable values that can be observed from the same sample. Random samples are drawn from the joint…

Machine Learning · Computer Science 2022-08-23 Noa Ben-David , Sivan Sabato

Improving adversarial robustness of neural networks remains a major challenge. Fundamentally, training a neural network via gradient descent is a parameter estimation problem. In adaptive control, maintaining persistency of excitation (PoE)…

Machine Learning · Statistics 2021-10-18 Kaustubh Sridhar , Oleg Sokolsky , Insup Lee , James Weimer

Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used meta learning methods include model agnostic meta learning (MAML), implicit MAML, Bayesian MAML. Thanks to its ability of modeling uncertainty,…

Machine Learning · Computer Science 2022-03-08 Lisha Chen , Tianyi Chen

We give a new consistent scoring function for structure learning of Bayesian networks. In contrast to traditional approaches to scorebased structure learning, such as BDeu or MDL, the complexity penalty that we propose is data-dependent and…

Machine Learning · Computer Science 2013-09-27 Eliot Brenner , David Sontag

Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal density. The mixture model can be…

Methodology · Statistics 2013-03-07 Cinzia Viroli

Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD)…

Machine Learning · Computer Science 2020-06-24 Jinghui Chen , Dongruo Zhou , Yiqi Tang , Ziyan Yang , Yuan Cao , Quanquan Gu

Training multimodal networks requires a vast amount of data due to their larger parameter space compared to unimodal networks. Active learning is a widely used technique for reducing data annotation costs by selecting only those samples…

Multimedia · Computer Science 2023-08-22 Meng Shen , Yizheng Huang , Jianxiong Yin , Heqing Zou , Deepu Rajan , Simon See

Deep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models offer a mathematical framework to reason about model…

Machine Learning · Computer Science 2019-05-28 Manikanta Srikar Yellapragada , Chandra Prakash Konkimalla

Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue…

Computation · Statistics 2014-12-30 Xun Huan , Youssef M. Marzouk

The analysis in Part I revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization when gradient noise is present. These algorithms are used when the risk functions are non-smooth and…

Optimization and Control · Mathematics 2017-04-21 Bicheng Ying , Ali H. Sayed

Exact algorithms for learning Bayesian networks guarantee to find provably optimal networks. However, they may fail in difficult learning tasks due to limited time or memory. In this research we adapt several anytime heuristic search-based…

Artificial Intelligence · Computer Science 2013-09-27 Brandon Malone , Changhe Yuan

Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in knowledge discovery. We study the problem of…

Optimization and Control · Mathematics 2022-05-10 Simge Kucukyavuz , Ali Shojaie , Hasan Manzour , Linchuan Wei , Hao-Hsiang Wu

This paper presents a new Bayesian framework for quantifying discretization errors in numerical solutions of ordinary differential equations. By modelling the errors as random variables, we impose a monotonicity constraint on the variances,…

Numerical Analysis · Mathematics 2024-11-14 Yuto Miyatake , Kaoru Irie , Takeru Matsuda