Publications related to Relative likelihood

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer2024

Fitting summary statistics of neural data with a differentiable spiking network simulator

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is w ...

2021

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

SPRINGER/PLENUM PUBLISHERS2021

Social Learning Under Inferential Attacks

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...

IEEE2021

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Wolfram Wiesemann

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood ...

2019

Database Alignment with Gaussian Features

Negar Kiyavash, Osman Emre Dai

We consider the problem of aligning a pair of databases with jointly Gaussian features. We consider two algorithms, complete database alignment via MAP estimation among all possible database alignments, and partial alignment via a thresholding approach of ...

MLR Press2019

Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network

Jean-Marc Odobez, Petr Motlicek, Weipeng He

We propose a novel multi-task neural network-based approach for joint sound source localization and speech/non-speech classification in noisy environments. The network takes raw short time Fourier transform as input and outputs the likelihood values for th ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Ramya Rasipuram, Marzieh Razavi

Developing a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be available, particularly for under-resourced languages. An alternative to development of a phonetic lexicon is to automatically derive subwo ...

Idiap2017

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Relative likelihood

Boosting likelihood learning with event reweighting

Fitting summary statistics of neural data with a differentiable spiking network simulator

Population pharmacokinetic model selection assisted by machine learning

Social Learning Under Inferential Attacks

Transpochimeric gene trancripts (tcgts) as cancer biomarkers

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Gaussian Process Regression for Maximum Entropy Distribution

Database Alignment with Gaussian Features

Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Graph Chatbot

Chat with Graph Search

Database Alignment with Gaussian Features

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Fitting summary statistics of neural data with a differentiable spiking network simulator

Social Learning Under Inferential Attacks

Population pharmacokinetic model selection assisted by machine learning

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Gaussian Process Regression for Maximum Entropy Distribution

Boosting likelihood learning with event reweighting

Transpochimeric gene trancripts (tcgts) as cancer biomarkers

Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network