Publications related to Improved inference for a boundary parameter

Related publications (21)

Coarse extrinsic curvature of Riemannian submanifolds

We introduce a novel concept of coarse extrinsic curvature for Riemannian submanifolds, inspired by Ollivier’s notion of coarse Ricci curvature. This curvature is derived from the Wasserstein 1-distance between probability measures supported in the tubular ...

2025

Source-Free Domain-Invariant Performance Prediction

Mathieu Salzmann

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

Springer Nature2025

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Identifying Aerodynamics of Delta-Wing Drones for Model-Based Navigation: A Comparative Study

Jan Skaloud, Aman Sharma, Pasquale Longobardi

This paper presents a comparative analysis of two methodologies for estimating unknown parameters in a Vehicle Dynamic Model (VDM)-based sensor fusion framework for small drones. Focusing on a delta-wing drone, we conduct open-air wind tunnel experiments t ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Statistical mechanics of the maximum-average submatrix problem

Rodrigo Emilio Pérez Ortiz, Lenka Zdeborová, Florent Gérard Krzakala, Vittorio Erba

We study the maximum-average submatrix problem, wherein given an N x N matrix J, one needs to find the k x k submatrix with the largest average number of entries. We investigate the problem for random matrices J, whose entries are i.i.d. random variables, ...

Iop Publishing Ltd2024

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

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

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 ...

2023

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Yanina Yurina Shkel, Anuj Kumar Yadav

Given two jointly distributed random variables (X,Y), a functional representation of X is a random variable Z independent of Y, and a deterministic function g(⋅,⋅) such that X=g(Y,Z). The problem of finding a minimum entropy functional representation is kn ...

2023

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Ali H. Sayed, Emre Telatar, Mert Kayaalp, Yunus Inan

We consider a distributed binary hypothesis testing setup where multiple nodes send quantized information to a central processor, which is oblivious to the nodes' statistics. We study the regime where the missed detection (type-II error) probability decays ...

IEEE2022

Bayesian approach for validation of runaway electron simulations

Carl Mathias Paul Hoppe

Plasma-terminating disruptions in future fusion reactors may result in conversion of the initial current to a relativistic runaway electron beam. Validated predictive tools are required to optimise the scenarios and mitigation actuators to avoid the excess ...

Cambridge University Press2022

Machine learning with kernels for portfolio valuation and risk management

Damir Filipovic, Lotfi Boudabsa

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is ...

SPRINGER HEIDELBERG2021

Contrastive Learning Inverts the Data Generating Process

Steffen Schneider

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feed-forward ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Equinox: Training (for Free) on a Custom Inference Accelerator

Babak Falsafi, Martin Jaggi, Mario Paulo Drumond Lages De Oliveira, Ahmet Caner Yüzügüler, Arash Pourhabibi Zarandi, Louis Coulon

DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...

ACM2021

Testing for Equivalence of Network Distribution Using Subgraph Counts

Sofia Charlotta Olhede

We consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a network sample are drawn from a specified model. We achieve this by deriving the j ...

AMER STATISTICAL ASSOC2020

Markov cubature rules for polynomial processes

Damir Filipovic, Martin Larsson, Sergio Andres Pulido Nino

We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rul ...

ELSEVIER2020

Fractional extreme distributions

Lotfi Boudabsa

We consider three classes of linear differential equations on distribution functions, with a fractional order alpha is an element of [0; 1]. The integer case alpha = 1 corresponds to the three classical extreme families. In general, we show that there is a ...

2020

On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients

Andreas Pautz, Mathieu Hursin, Daniel Jerôme Siefman

Sensitivity coefficients from Monte Carlo neutron transport codes have uncertainties that can affect nuclear data adjustments with integral experiments. This paper presents an extended version of Generalized Linear Least Squares (GLLS), called xGLLS, that ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration

Jan Skaloud, Stéphane Guerrier, Mehran Khaghani

The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of incre ...

2020

Large values of Hecke-Maass L-functions with prescribed argument

Alexandre François Peyrot

We investigate the existence of large values of L-functions attached to Maass forms on the critical line with prescribed argument. The results obtained rely on the resonance method developed by Soundararajan and furthered by Hough. ...

WORLD SCIENTIFIC PUBL CO PTE LTD2020

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Zhenzhu Meng

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monit ...

2019

A pathwise approach to the extinction of branching processes with countably many types

Sophie Myriam Hautphenne

We consider the extinction events of Galton-Watson processes with countably infinitely many types. In particular, we construct truncated and augmented Galton-Watson processes with finite but increasing sets of types. A pathwise approach is then used to sho ...

ELSEVIER SCIENCE BV2019