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Hyperparamètre

Publications associées (60)

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Yu-Ting Huang, Kamalaruban Parameswaran, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

2020

Seek and learn: Automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning

Kamiar Aminian, Hooman Dejnabadi, Pritish Chakravarty

Animal‐borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer’s high sensitivity to movement and fast response time ...

2020

Design Patterns for Resource-Constrained Automated Deep-Learning Methods

Prakhar Gupta

We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accura ...

2020

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Volkan Cevher, Junhong Lin

We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...

2020

On The Choice of Graph Neural Network Architectures

Pascal Frossard, Guillermo Ortiz Jimenez, Clément Arthur Yvon Vignac

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...

2020

Privacy-preserving and Personalized Federated Machine Learning for Medical Data

Felix Hans Michel Grimberg

The federated learning setting is prone to suffering from non-identically distributed data across participating agents. This gives rise to the task of model personalization, where agents collaborate to train several different machine learning models instea ...

2020

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Pascal Frossard, Mireille El Gheche, Giovanni Chierchia

In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to larg ...

IEEE2019

Local SGD Converges Fast and Communicates Little

Sebastian Urban Stich

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speedup with respect to the number of workers, but this is rarely seen in practice as the scheme often suffers from large ne ...

2019

Incremental Transfer Learning in Two-pass Information Bottleneck Based Speaker Diarization System for Meetings

The two-pass information bottleneck (TPIB) based speaker diarization system operates independently on different conversational recordings. TPIB system does not consider previously learned speaker discriminative information while di-arizing new conversation ...

2019

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

Volkan Cevher, Alp Yurtsever

We propose a class of novel variance-reduced stochastic conditional gradient methods. By adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of Fang et al. (2018) for the classical Frank-Wolfe (FW) method, we introduce ...

2019

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