Search Results for author: Isabelle Guyon

Found 40 papers, 17 papers with code

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

no code implementations15 Jun 2022 Adrian El Baz, André Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Shell Hu, Frank Hutter, Zhengying Liu, Felix Mohr, Jan van Rijn, Xin Wang, Isabelle Guyon

Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.

Few-Shot Learning Image Classification +1

Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020

1 code implementation6 Apr 2022 Zhen Xu, Lanning Wei, Huan Zhao, Rex Ying, Quanming Yao, Wei-Wei Tu, Isabelle Guyon

Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.

Graph Learning Neural Architecture Search +1

LTU Attacker for Membership Inference

1 code implementation4 Feb 2022 Joseph Pedersen, Rafael Muñoz-Gómez, Jiangnan Huang, Haozhe Sun, Wei-Wei Tu, Isabelle Guyon

In both cases classification accuracy or error rate are used as the metric: Utility is evaluated with the classification accuracy of the Defender model; Privacy is evaluated with the membership prediction error of a so-called "Leave-Two-Unlabeled" LTU Attacker, having access to all of the Defender and Reserved data, except for the membership label of one sample from each.

Inference Attack Membership Inference Attack

Advances in MetaDL: AAAI 2021 challenge and workshop

no code implementations1 Feb 2022 Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan van Rijn, Sebastien Treguer, Joaquin Vanschoren

Winning methods featured various classifiers trained on top of the second last layer of popular CNN backbones, fined-tuned on the meta-training data (not necessarily in an episodic manner), then trained on the labeled support and tested on the unlabeled query sets of the meta-test data.

Few-Shot Learning

OmniPrint: A Configurable Printed Character Synthesizer

2 code implementations17 Jan 2022 Haozhe Sun, Wei-Wei Tu, Isabelle Guyon

We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research.

Meta-Learning

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

no code implementations26 Oct 2021 Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model.

Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform

2 code implementations12 Oct 2021 Zhen Xu, Sergio Escalera, Isabelle Guyon, Adrien Pavão, Magali Richard, Wei-Wei Tu, Quanming Yao, Huan Zhao

A public instance of Codabench (https://www. codabench. org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats.

AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge

1 code implementation28 Jul 2021 Zhen Xu, Wei-Wei Tu, Isabelle Guyon

Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020.

AutoML Feature Engineering +2

ChaLearn Looking at People: Inpainting and Denoising challenges

no code implementations24 Jun 2021 Sergio Escalera, Marti Soler, Stephane Ayache, Umut Guclu, Jun Wan, Meysam Madadi, Xavier Baro, Hugo Jair Escalante, Isabelle Guyon

Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence.

Denoising Pose Estimation

Learning to run a Power Network Challenge: a Retrospective Analysis

no code implementations2 Mar 2021 Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold, Adrian Kelly, Aïdan O'Sullivan, Jan Viebahn, Mariette Awad, Isabelle Guyon, Patrick Panciatici, Camilo Romero

Motivated to investigate the potential of AI methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks.

Deep Statistical Solvers

1 code implementation NeurIPS 2020 Balthazar Donon, Zhengying Liu, Wenzhuo LIU, Isabelle Guyon, Antoine Marot, Marc Schoenauer

This paper introduces Deep Statistical Solvers (DSS), a new class of trainable solvers for optimization problems, arising e. g., from system simulations.

AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data

no code implementations30 Oct 2020 Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu

Neural architecture search (NAS) is an AutoML approach that generates and evaluates multiple neural network architectures concurrently and improves the accuracy of the generated models iteratively.

Neural Architecture Search

Learning to run a power network challenge for training topology controllers

no code implementations5 Dec 2019 Antoine Marot, Benjamin Donnot, Camilo Romero, Luca Veyrin-Forrer, Marvin Lerousseau, Balthazar Donon, Isabelle Guyon

For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities.

Synthetic Event Time Series Health Data Generation

no code implementations14 Nov 2019 Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, Andrew Yale, Kristin P. Bennett

Due to the complexity of the real data, in which each patient visit is an event, we transform the data by using summary statistics to characterize the events for a fixed set of time intervals, to facilitate analysis and interpretability.

Time Series

LEAP nets for power grid perturbations

1 code implementation22 Aug 2019 Benjamin Donnot, Balthazar Donon, Isabelle Guyon, Zhengying Liu, Antoine Marot, Patrick Panciatici, Marc Schoenauer

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully.

Network Embedding Transfer Learning

ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition

no code implementations29 Jul 2019 Jun Wan, Chi Lin, Longyin Wen, Yunan Li, Qiguang Miao, Sergio Escalera, Gholamreza Anbarjafari, Isabelle Guyon, Guodong Guo, Stan Z. Li

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than $200$ teams round the world.

Gesture Recognition

Graph Neural Solver for Power Systems

no code implementations IJCNN 2019 Balthazar Donon, Benjamin Donnot, Isabelle Guyon, Antoine Marot

Load flow computation is a well studied and understood problem, but current methods (based on Newton-Raphson) are slow.

AutoML @ NeurIPS 2018 challenge: Design and Results

no code implementations12 Mar 2019 Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.

AutoML

Lessons learned from the AutoML challenge

no code implementations Conférence sur l'Apprentissage Automatique 2018 Lisheng Sun-Hosoya, Isabelle Guyon, Michele Sebag

We give a brief account of the main findings of our post-hoc analysis of the first AutoML challenge (2015-2016).

AutoML

Optimization of computational budget for power system risk assessment

no code implementations3 May 2018 Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick Panciatici

We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators.

Anticipating contingengies in power grids using fast neural net screening

no code implementations3 May 2018 Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici

We evaluate that our method scales up to power grids of the size of the French high voltage power grid (over 1000 power lines).

Fast Power system security analysis with Guided Dropout

1 code implementation30 Jan 2018 Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici

We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers.

Causal Generative Neural Networks

1 code implementation ICLR 2018 Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.

Causal Discovery

Introducing machine learning for power system operation support

no code implementations27 Sep 2017 Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Patrick Panciatici, Antoine Marot

One of the primary goals of dispatchers is to protect equipment (e. g. avoid that transmission lines overheat) with few degrees of freedom: we are considering in this paper solely modifications in network topology, i. e. re-configuring the way in which lines, transformers, productions and loads are connected in sub-stations.

Learning Functional Causal Models with Generative Neural Networks

2 code implementations15 Sep 2017 Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN).

Design and Analysis of the NIPS 2016 Review Process

1 code implementation31 Aug 2017 Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike Von Luxburg

Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning.

ChaLearn Looking at People: A Review of Events and Resources

no code implementations10 Jan 2017 Sergio Escalera, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon

This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available.

Gesture Recognition

Principal motion components for gesture recognition using a single-example

no code implementations17 Oct 2013 Hugo Jair Escalante, Isabelle Guyon, Vassilis Athitsos, Pat Jangyodsuk, Jun Wan

In the considered scenario a single training-video is available for each gesture to be recognized, which limits the application of traditional techniques (e. g., HMMs).

Gesture Recognition

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