Concept

Gradient boosting

Related publications (157)

UCLID-Net: Single View Reconstruction in Object Space

Pascal Fua, Benoît Alain René Guillard, Edoardo Remelli

Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean st ...

2020

MATHICSE Technical Report : A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters

Fabio Nobile, Matthieu Claude Martin, Panagiotis Tsilifis

In this paper, we present a multilevel Monte Carlo (MLMC) version of the Stochastic Gradient (SG) method for optimization under uncertainty, in order to tackle Optimal Control Problems (OCP) where the constraints are described in the form of PDEs with rand ...

MATHICSE2020

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

AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Sabine Süsstrunk, Majed El Helou, Frederike Dümbgen

The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting i ...

IEEE2020

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Negar Kiyavash

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

2020

Adaptation and Learning Over Networks Under Subspace Constraints & x2014;Part II: Performance Analysis

Ali H. Sayed, Stefan Vlaski, Roula Nassif

Part & x00A0;I of this paper considered optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

SHiP is a proposed high-intensity beam dump experiment set to operate at the CERN SPS. It is expected to have an unprecedented sensitivity to a variety of models containing feebly interacting particles, such as Heavy Neutral Leptons (HNLs). Two HNLs or mor ...

2020

Generative adversarial training of product of policies for robust and adaptive movement primitives

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...

2020

Extrapolation for Large-batch Training in Deep Learning

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Lingjing Kong

Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data. A major roadblock faced when increasing the batc ...

2020

Byzantine machine learning

Rachid Guerraoui, Mahsa Taziki, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

The present invention concerns computer-implemented methods for training a machine learning model using Stochastic Gradient Descent, SGD. In one embodiment, the method is performed by a first computer in a distributed computing environment and comprises pe ...

2020

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