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Related papers: Online Learning of k-CNF Boolean Functions

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PAC-learning usually aims to compute a small subset ($\varepsilon$-sample/net) from $n$ items, that provably approximates a given loss function for every query (model, classifier, hypothesis) from a given set of queries, up to an additive…

Machine Learning · Computer Science 2020-06-11 Alaa Maalouf , Ibrahim Jubran , Murad Tukan , Dan Feldman

In this paper, we consider unregularized online learning algorithms in a Reproducing Kernel Hilbert Spaces (RKHS). Firstly, we derive explicit convergence rates of the unregularized online learning algorithms for classification associated…

Machine Learning · Computer Science 2015-04-28 Yiming Ying , Ding-Xuan Zhou

Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes…

Machine Learning · Statistics 2020-02-06 Benjamin Guedj

In this paper, we demonstrate how to learn the objective function of a decision-maker while only observing the problem input data and the decision-maker's corresponding decisions over multiple rounds. We present exact algorithms for this…

Optimization and Control · Mathematics 2020-03-31 Andreas Bärmann , Alexander Martin , Sebastian Pokutta , Oskar Schneider

Networked dynamical systems are widely used as formal models of real-world cascading phenomena, such as the spread of diseases and information. Prior research has addressed the problem of learning the behavior of an unknown dynamical system…

We study the problem of computationally efficient proper agnostic learning of multidimensional concept classes under the Gaussian distribution. In this setting, given i.i.d. labeled samples from an unknown distribution over $\mathbb{R}^d…

Data Structures and Algorithms · Computer Science 2026-05-28 Sergei Tikhonov , Arsen Vasilyan

Polynomial approximations to boolean functions have led to many positive results in computer science. In particular, polynomial approximations to the sign function underly algorithms for agnostically learning halfspaces, as well as…

Computational Complexity · Computer Science 2014-12-09 Mark Bun , Thomas Steinke

The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the critical learning capacity ($\alpha_c=\frac{16}{\pi} \sqrt{\ln K}$) of large committee machines, to verify the…

Condensed Matter · Physics 2007-05-23 Remi Monasson , Riccardo Zecchina

We present a simple randomized algorithm that approximates the number of satisfying assignments of Boolean formulas in conjunctive normal form. To the best of our knowledge this is the first algorithm which approximates #k-SAT for any k >=…

Data Structures and Algorithms · Computer Science 2011-07-12 Marc Thurley

A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for…

Artificial Intelligence · Computer Science 2013-02-08 Russell Greiner , Adam J. Grove , Dale Schuurmans

Convolutional Neural Networks (CNNs) are well established models capable of achieving state-of-the-art classification accuracy for various computer vision tasks. However, they are becoming increasingly larger, using millions of parameters,…

Computer Vision and Pattern Recognition · Computer Science 2017-07-27 Nikolaos Passalis , Anastasios Tefas

We study the generalization performance of online learning algorithms trained on samples coming from a dependent source of data. We show that the generalization error of any stable online algorithm concentrates around its regret--an easily…

Machine Learning · Statistics 2012-06-08 Alekh Agarwal , John C. Duchi

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm…

Quantum Physics · Physics 2012-10-10 Christopher E. Granade , Christopher Ferrie , Nathan Wiebe , D. G. Cory

Supervised learning requires the specification of a loss function to minimise. While the theory of admissible losses from both a computational and statistical perspective is well-developed, these offer a panoply of different choices. In…

Machine Learning · Computer Science 2020-02-11 Richard Nock , Aditya Krishna Menon

Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model. Existing techniques for loss function learning have shown promising results, often…

Machine Learning · Computer Science 2025-10-14 Christian Raymond , Qi Chen , Bing Xue , Mengjie Zhang

This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current…

Machine Learning · Computer Science 2012-05-14 Thomas J. Walsh , Istvan Szita , Carlos Diuk , Michael L. Littman

Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-trivial task. In this paper we propose an unsupervised online…

Computer Vision and Pattern Recognition · Computer Science 2018-06-05 Daniel Maurer , Andrés Bruhn

We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and…

Machine Learning · Computer Science 2021-12-22 Ehsan Mokhtarian , Sina Akbari , Fateme Jamshidi , Jalal Etesami , Negar Kiyavash

A Boolean function of n bits is balanced if it takes the value 1 with probability 1/2. We exhibit a balanced Boolean function with a randomized evaluation procedure (with probability 0 of making a mistake) so that on uniformly random…

Probability · Mathematics 2012-06-21 Itai Benjamini , Oded Schramm , David B. Wilson

We present a unifying picture of PAC-Bayesian and mutual information-based upper bounds on the generalization error of randomized learning algorithms. As we show, Tong Zhang's information exponential inequality (IEI) gives a general recipe…

Machine Learning · Computer Science 2021-10-26 Pradeep Kr. Banerjee , Guido Montúfar