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Related papers: Uniform Convergence Bounds for Codec Selection

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Optimal design of experiments for correlated processes is an increasingly relevant and active research topic. Present methods have restricted possibilities to judge their quality. To fill this gap, we complement the virtual noise approach…

Statistics Theory · Mathematics 2021-10-25 Andrej Pázman , Markus Hainy , Werner G. Müller

Gradient clipping is a popular modification to standard (stochastic) gradient descent, at every iteration limiting the gradient norm to a certain value $c >0$. It is widely used for example for stabilizing the training of deep learning…

Machine Learning · Computer Science 2023-11-10 Anastasia Koloskova , Hadrien Hendrikx , Sebastian U. Stich

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit…

Multimedia · Computer Science 2015-06-15 Laura Toni , Ramon Aparicio-Pardo , Karine Pires , Gwendal Simon , Alberto Blanc , Pascal Frossard

When training an estimator such as a neural network for tasks like image denoising, it is often preferred to train one estimator and apply it to all noise levels. The de facto training protocol to achieve this goal is to train the estimator…

Machine Learning · Computer Science 2020-07-20 Abhiram Gnansambandam , Stanley H. Chan

We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios. This challenge has been referred to as the holy grail of ensemble learning, an open research issue for over 30…

Machine Learning · Computer Science 2024-02-08 Danny Wood , Tingting Mu , Andrew Webb , Henry Reeve , Mikel Luján , Gavin Brown

Universal audio codecs learn entangled representations across audio types, whereas some specific codecs offer decoupled representations but are limited to speech. Real-world audio, however, often contains mixed speech and background sounds,…

Sound · Computer Science 2025-09-12 Xiaoxue Luo , Jinwei Huang , Runyan Yang , Yingying Gao , Junlan Feng , Chao Deng , Shilei Zhang

Recently ensemble selection for consensus clustering has emerged as a research problem in Machine Intelligence. Normally consensus clustering algorithms take into account the entire ensemble of clustering, where there is a tendency of…

Machine Learning · Computer Science 2015-08-19 Shouvick Mondal , Arko Banerjee

The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions. In fact, this problem emerges whenever an acoustic scene classification…

Audio and Speech Processing · Electrical Eng. & Systems 2020-05-04 Alessandro Ilic Mezza , Emanuël A. P. Habets , Meinard Müller , Augusto Sarti

We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a…

Optimization and Control · Mathematics 2017-04-12 Angelia Nedić , Alex Olshevsky , César A. Uribe

We formulate selecting the best optimizing system (SBOS) problems and provide solutions for those problems. In an SBOS problem, a finite number of systems are contenders. Inside each system, a continuous decision variable affects the…

Methodology · Statistics 2025-11-04 Nian Si , Yifu Tang , Zeyu Zheng

This paper revisits the principle of uniform convergence in statistical learning, discusses how it acts as the foundation behind machine learning, and attempts to gain a better understanding of the essential problem that current deep…

Computer Vision and Pattern Recognition · Computer Science 2022-09-07 Lei Zhang , Heung-Yeung Shum

In the uniformity testing task, an algorithm is provided with samples from an unknown probability distribution over a (known) finite domain, and must decide whether it is the uniform distribution, or, alternatively, if its total variation…

Data Structures and Algorithms · Computer Science 2025-08-05 Guy Blanc , Clément L. Canonne , Erik Waingarten

Clipping or saturation in audio signals is a very common problem in signal processing, for which, in the severe case, there is still no satisfactory solution. In such case, there is a tremendous loss of information, and traditional methods…

The coherent control of wave absorption has important applications in areas such as energy harvesting, imaging, and sensing. However, most practical scenarios involve the absorption of partially coherent rather than fully coherent waves.…

Optics · Physics 2024-08-13 Cheng Guo , Shanhui Fan

We work out a theory of approximate quantum error correction that allows us to derive a general lower bound for the entanglement fidelity of a quantum code. The lower bound is given in terms of Kraus operators of the quantum noise. This…

Quantum Physics · Physics 2009-11-13 Rochus Klesse

We determine the optimal rates of universal quantum codes for entanglement transmission and generation under channel uncertainty. In the simplest scenario the sender and receiver are provided merely with the information that the channel…

Quantum Physics · Physics 2015-05-13 I. Bjelakovic , H. Boche , J. Noetzel

Feature selection is an important part of building a machine learning model. By eliminating redundant or misleading features from data, the machine learning model can achieve better performance while reducing the demand on com-puting…

Machine Learning · Computer Science 2021-06-11 Song Tan , Xia He

Data classification, the process of analyzing data and organizing it into categories, is a fundamental computing problem of natural and artificial information processing systems. Ideally, the performance of classifier models would be…

Machine Learning · Computer Science 2022-06-07 Claus Metzner , Achim Schilling , Maximilian Traxdorf , Konstantin Tziridis , Holger Schulze , Patrick Krauss

The ranking and selection problem is a popular framework in the simulation literature for studying optimal information collection. We study a version of this problem in which the simulation output for each design is normally distributed…

Optimization and Control · Mathematics 2025-09-03 Jianzhong Du , Ilya O. Ryzhov , Siyang Gao

Consistent sampling is a technique for specifying, in small space, a subset $S$ of a potentially large universe $U$ such that the elements in $S$ satisfy a suitably chosen sampling condition. Given a subset $\mathcal{I}\subseteq U$ it…

Data Structures and Algorithms · Computer Science 2014-04-21 Konstantin Kutzkov , Rasmus Pagh
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