Publications related to Importance Sampling Tree for Large-scale Empirical Expectation

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

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We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Deep Learning Generalization with Limited and Noisy Labels

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Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

ReLU Neural Network Galerkin BEM

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We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

SPRINGER/PLENUM PUBLISHERS2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

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Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we intro ...

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Monte Carlo Estimators for Differential Light Transport

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Physically based differentiable rendering algorithms propagate derivatives through realistic light transport simulations and have applications in diverse areas including inverse reconstruction and machine learning. Recent progress has led to unbiased metho ...

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Tree in Tree: from Decision trees to Decision Graphs

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Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional dec ...

Curran Associates, Inc, (NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems)2021

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

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We consider the numerical approximation of an optimal control problem for an elliptic Partial Differential Equation (PDE) with random coefficients. Specifically, the control function is a deterministic, distributed forcing term that minimizes the expected ...

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Importance Sampling Tree for Large-scale Empirical Expectation

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Deep Learning Generalization with Limited and Noisy Labels

ReLU Neural Network Galerkin BEM

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Monte Carlo Estimators for Differential Light Transport

Tree in Tree: from Decision trees to Decision Graphs

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Deep Learning Generalization with Limited and Noisy Labels

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Monte Carlo Estimators for Differential Light Transport

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Tree in Tree: from Decision trees to Decision Graphs

ReLU Neural Network Galerkin BEM