Publications related to The Complexity of Distinguishing Distributions

Learning new physics from an imperfect machine

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties a ...

SPRINGER2022

FANOK: Knockoffs in Linear Time

Quentin Rebjock

We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large-scale feature selection problems. Identifying the knockoff distribution requires solving a large-scale semidefinite progra ...

SIAM PUBLICATIONS2021

Variational Monte Carlo Calculations of A

Giuseppe Carleo

The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum m ...

AMER PHYSICAL SOC2021

Third correction of the Rhône River in Switzerland: physical and numerical modelling of the “Martigny bend” priority measure

Giovanni De Cesare, Azin Amini, Stéphanie Joëlle André, Irene Almeida Samora, Pierre Bourqui, Jean-Noël Saugy

The Rhône River, second largest in Switzerland, has already been corrected twice (1863-1893; 1930-1960) to ensure safety and economic development in the Rhône valley. However, in October 2000, the highest flood of the century occurred showing the limitatio ...

International Research Society INTERPRAEVENT2021

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

On the Error Rate of the LoRa Modulation With Interference

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Orion Afisiadis, Matthieu Cotting

LoRa is a chirp spread-spectrum modulation developed for the Internet of Things (IoT). In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

A Complexity Analysis Approach for Model-based System Engineering

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng, Jinwei Chen

With the increasing complexity of the highly engineered products, Model-based Systems Engineering (MBSE) is proposed to support the complexity management of the product development. As the basic of complexity management, complexity analysis is used to meas ...

IEEE2020

Kernel Density Estimation through Density Constrained Near Neighbor Search

Mikhail Kapralov, Navid Nouri

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020

LaMBERT: Light and Multigranular BERT

Ljupche Milosheski

Pre-training complex language models is essential for the success of the recent methods such as BERT or OpenAI GPT. Their size makes not only the pre-training phase, but also consecutive applications to be computationally expensive. BERT-like models excel ...

2020

String Matching: Communication, Circuits, and Learning

Mika Tapani Göös

String matching is the problem of deciding whether a given n-bit string contains a given k-bit pattern. We study the complexity of this problem in three settings. - Communication complexity. For small k, we provide near-optimal upper and lower bounds on th ...

Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany2019

The Complexity of Distinguishing Distributions

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Learning new physics from an imperfect machine

FANOK: Knockoffs in Linear Time

Variational Monte Carlo Calculations of A

Third correction of the Rhône River in Switzerland: physical and numerical modelling of the “Martigny bend” priority measure

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

On the Error Rate of the LoRa Modulation With Interference

A Complexity Analysis Approach for Model-based System Engineering

Kernel Density Estimation through Density Constrained Near Neighbor Search

LaMBERT: Light and Multigranular BERT

String Matching: Communication, Circuits, and Learning

Learning new physics from an imperfect machine

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

LaMBERT: Light and Multigranular BERT

Kernel Density Estimation through Density Constrained Near Neighbor Search

A Complexity Analysis Approach for Model-based System Engineering

Variational Monte Carlo Calculations of A

Third correction of the Rhône River in Switzerland: physical and numerical modelling of the “Martigny bend” priority measure

FANOK: Knockoffs in Linear Time

On the Error Rate of the LoRa Modulation With Interference

String Matching: Communication, Circuits, and Learning