Publications related to Bayesian network

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Michael Christoph Gastpar, Adrien Vandenbroucque, Amedeo Roberto Esposito

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

2022

Masked Training of Neural Networks with Partial Gradients

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD). Recently, many variations have been explored: perturbing parameters for better accuracy (such as in Extra-gradient), limiting SGD updates to a sub ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Volkan Cevher, Kimon Antonakopoulos, Ya-Ping Hsieh

We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully ...

2022

A Variational Inference Approach to Learning Multivariate Wold Processes

Patrick Thiran, Matthias Grossglauser, Negar Kiyavash, Seyed Jalal Etesami, William Trouleau

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

PMLR2021

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Nicolas Macris, Jean François Emmanuel Barbier, Chun Lam Chan

Recently, a new adaptive path interpolation method has been developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gen ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Learning in Volatile Environments With the Bayes Factor Surprise

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

MIT PRESS2021

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

Predictive brain: Addressing the level of representation by reviewing perceptual hysteresis

David Pascucci

In recent years, the idea that the prediction of sensory input is one of the major computational goals of the nervous system led to the development of several large-scale theories of brain functioning, such as different versions of the Bayesian approach to ...

ELSEVIER MASSON, CORP OFF2021

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Bayesian network

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Masked Training of Neural Networks with Partial Gradients

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Bayesian Decision Making in Groups is Hard

A Variational Inference Approach to Learning Multivariate Wold Processes

Learning Self-Exciting Temporal Point Processes Under Noisy Observations

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Learning in Volatile Environments With the Bayes Factor Surprise

Social Learning Under Inferential Attacks

Predictive brain: Addressing the level of representation by reviewing perceptual hysteresis

Graph Chatbot

Chat with Graph Search

Masked Training of Neural Networks with Partial Gradients

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Learning Self-Exciting Temporal Point Processes Under Noisy Observations

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Bayesian Decision Making in Groups is Hard

A Variational Inference Approach to Learning Multivariate Wold Processes

Learning in Volatile Environments With the Bayes Factor Surprise

Predictive brain: Addressing the level of representation by reviewing perceptual hysteresis

Social Learning Under Inferential Attacks