Search Results for author: Balázs Kégl

Found 15 papers, 3 papers with code

Guided Safe Shooting: model based reinforcement learning with safety constraints

no code implementations20 Jun 2022 Giuseppe Paolo, Jonas Gonzalez-Billandon, Albert Thomas, Balázs Kégl

In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game.

Decision Making Model-based Reinforcement Learning +2

Knothe-Rosenblatt transport for Unsupervised Domain Adaptation

no code implementations6 Oct 2021 Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang, Balázs Kégl

Knothe-Rosenblatt Domain Adaptation (KRDA) is based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians.

Density Estimation Unsupervised Domain Adaptation

The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning

no code implementations29 Sep 2021 Albert Thomas, Balázs Kégl, Othman Gaizi, Gabriel Hurtado

Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints.

Acrobot Reinforcement Learning (RL)

Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

1 code implementation ICLR 2021 Balázs Kégl, Gabriel Hurtado, Albert Thomas

We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent.

Acrobot Model-based Reinforcement Learning

InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification

no code implementations30 May 2019 Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl

Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels.

General Classification

Spurious samples in deep generative models: bug or feature?

no code implementations3 Oct 2018 Balázs Kégl, Mehdi Cherti, Akın Kazakçı

Traditional wisdom in generative modeling literature is that spurious samples that a model can generate are errors and they should be avoided.

Similarity encoding for learning with dirty categorical variables

2 code implementations4 Jun 2018 Patricio Cerda, Gaël Varoquaux, Balázs Kégl

We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains.

Dimensionality Reduction

Digits that are not: Generating new types through deep neural nets

no code implementations14 Jun 2016 Akın Kazakçıand Mehdi Cherti, Balázs Kégl

For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users.

Correlation-based construction of neighborhood and edge features

no code implementations20 Dec 2013 Balázs Kégl

Next, we connect neighborhood features that correlate, and construct edge features by subtracting the correlated neighborhood features of each other.

Multi-class Classification

The return of AdaBoost.MH: multi-class Hamming trees

no code implementations20 Dec 2013 Balázs Kégl

Within the framework of AdaBoost. MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to $K$ binary one-against-all classifications.

Algorithms for Hyper-Parameter Optimization

no code implementations NeurIPS 2011 James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl

Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs.

Image Classification

Learning the 2-D Topology of Images

no code implementations NeurIPS 2007 Nicolas L. Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl

We study the following question: is the two-dimensional structure of images a very strong prior or is it something that can be learned with a few examples of natural images?

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