Publications associées à Deterministic error bounds for kernel-based learning techniques under bounded noise

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Rudolf Haraksim

This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingerma ...

Inst Engineering Technology-IET2017

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

Design of a Distributed Quantized Luenberger Filter for Bounded Noise

Colin Neil Jones, Ye Pu, Andrea Alessandretti, Francisco Fernandes Castro Rego

This paper addresses the problem of distributed state estimation for linear systems with process and measurement noise, in the case of limited communication data rate, where the data exchanged between agents is quantized. We propose a linear distributed Lu ...

Ieee2016

A consensus algorithm for networks with process noise and quantization error

Colin Neil Jones, Ye Pu, Andrea Alessandretti, Francisco Fernandes Castro Rego

In this paper we address the problem of quantized consensus where process noise or external inputs corrupt the state of each agent at each iteration. We propose a quantized consensus algorithm with progressive quantization, where the quantization interval ...

IEEE2015

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Daniel Gatica-Perez

Human mobility prediction is an important problem which has a large num- ber of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address mod- eling and application asp ...

2015

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

A rigorous formula representing the counterpart of the Cooray-Rubinstein expression in the time domain has recently been derived by the authors. In this paper, the formula is particularized for the cases of a unit step and a linearly rising channel-base cu ...

Institute of Electrical and Electronics Engineers2015

The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Barbara Caputo

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which cont ...

2014

Nonparametric Construction of Multivariate Kernels

Victor Panaretos, Kjell Konis

We propose a nonparametric method for constructing multivariate kernels tuned to the configuration of the sample, for density estimation in R-d, d moderate. The motivation behind the approach is to break down the construction of the kernel into two parts: ...

American Statistical Association2012

Deterministic error bounds for kernel-based learning techniques under bounded noise

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Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Design of a Distributed Quantized Luenberger Filter for Bounded Noise

A consensus algorithm for networks with process noise and quantization error

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Nonparametric Construction of Multivariate Kernels

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

A consensus algorithm for networks with process noise and quantization error

Nonparametric Construction of Multivariate Kernels

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

Design of a Distributed Quantized Luenberger Filter for Bounded Noise