Publications related to MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sam ...

2022

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Retroactive Packet Sampling for Traffic Receipts

Pavlos Nikolopoulos, Adrian Perrig, Christos Pappas

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency" system. In this syst ...

ACM2019

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Michaël Unser, Quy Ha Nguyen

In our recent work, the sampling and reconstruction of non-decaying signals, modeled as members of weighted-L-p spaces, were shown to be stable with an appropriate choice of the generating kernel for the shift-invariant reconstruction space. In this paper, ...

2019

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Ting Zhu

The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of ...

EPFL2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Too much success for recent groundbreaking epigenetic experiments

Gregory Francis

An article reporting statistical evidence for epigenetic transfer of learned behavior has important implications, if true. With random sampling, real effects do not always result in rejection of the null hypothesis, but the reported experiments were unifor ...

Genetics Society of America2014

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

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Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Collaborative Sampling in Generative Adversarial Networks

Retroactive Packet Sampling for Traffic Receipts

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Sampling at unknown locations, with an application in surface retrieval

Safe Adaptive Importance Sampling

Shapes from Pixels

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Random sampling of bandlimited signals on graphs

Too much success for recent groundbreaking epigenetic experiments

Sampling at unknown locations, with an application in surface retrieval

Retroactive Packet Sampling for Traffic Receipts

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Safe Adaptive Importance Sampling

Collaborative Sampling in Generative Adversarial Networks

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Shapes from Pixels

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Too much success for recent groundbreaking epigenetic experiments

Random sampling of bandlimited signals on graphs