Publications related to Collaborative Sampling in Generative Adversarial Networks

Center-aware Adversarial Augmentation for Single Domain Generalization

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Adversarial Parametric Pose Prior

Pascal Fua, Mathieu Salzmann, Anastasia Remizova, Andrey Davydov, Victor Constantin, Sina Honari

The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is und ...

IEEE2022

On the Double Descent of Random Features Models Trained with SGD

Volkan Cevher, Fanghui Liu

We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...

2022

Dalton: Learned Partitioning for Distributed Data Streams

Anastasia Ailamaki, Eleni Zapridou, Ioannis Mytilinis

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...

2022

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Nicolas Henri Bernard Flammarion, Xiang Cheng

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an under-damped form of the Langevin algorithm perfor ...

INT STATISTICAL INST2021

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, . . . , Xn from an unknown finite-support distribution p are passed through n copies of a discrete privatization chan- nel (a.k.a. mechanism) producing outputs Y1, . . . , Yn. The channel law gua ...

2021

System and methods for differential imaging using a lock-in camera

Christophe Moser, Timothé Laforest, Damien Claude-Marie Loterie, Chiara Bonati

The present invention describes an imaging system that allows visualization of a wide range of samples both in terms of morphology and in terms of material (e.g. density distribution, varying chemical composition, or anything that induces a change of optic ...

2021

A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Alessandro Pau

This paper reports an algorithm to automatically identify the chain of events leading to a disruption, evaluating the so-called reference warning time. This time separates the plasma current flat-top of each disrupted discharge into two parts: a non-disrup ...

2021

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Collaborative Sampling in Generative Adversarial Networks

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Center-aware Adversarial Augmentation for Single Domain Generalization

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Adversarial Parametric Pose Prior

On the Double Descent of Random Features Models Trained with SGD

Dalton: Learned Partitioning for Distributed Data Streams

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

System and methods for differential imaging using a lock-in camera

A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Adversarial Parametric Pose Prior

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Dalton: Learned Partitioning for Distributed Data Streams

Center-aware Adversarial Augmentation for Single Domain Generalization

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Is there an analog of Nesterov acceleration for gradient-based MCMC?

On the Double Descent of Random Features Models Trained with SGD

System and methods for differential imaging using a lock-in camera