Publications associées à Règle d'inférence

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Robert West, Maxime Jean Julien Peyrard, Valentin Hartmann, Léo Nicolas René Meynent

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

IEEE COMPUTER SOC2023

A note on universal inference

Anthony Christopher Davison, Timmy Rong Tian Tse

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

WILEY2022

Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps ...

ASSOC COMPUTING MACHINERY2022

Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes

Elisa Oricchio, Stephanie Jocelyne Sungalee, Daniele Tavernari, Giovanni Ciriello, Marco Mina

Chromatin compartmentalization reflects biological activity. However, inference of chromatin sub-compartments and compartment domains from chromosome conformation capture (Hi-C) experiments is limited by data resolution. As a result, these have been charac ...

Nature Research2021

Protecting Mobile Food Diaries from Getting too Personal

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Smartphone applications that use passive sensing to support human health and well-being primarily rely on: (a) generating low-dimensional representations from high-dimensional data streams; (b) making inferences regarding user behavior; and (c) using those ...

Association for Computing Machinery2020

Structural Rewriting in XOR-Majority Graphs

Giovanni De Micheli, Mathias Soeken, Zhufei Chu

In this paper, we present a structural rewriting method for a recently proposed XOR-Majority graph (XMG), which has exclusive-OR (XOR), majority-of-three (MAJ), and inverters as primitives. XMGs are an extension of Majority-Inverter Graphs (MIGs). Previous ...

ASSOC COMPUTING MACHINERY2019

Verified Functional Programming

Nicolas Charles Yves Voirol

In this thesis, we present Stainless, a verification system for an expressive subset of the Scala language. Our system is based on a dependently-typed language and an algorithmic type checking procedure which ensures total correctness. We rely on SMT solve ...

EPFL2019

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

Semiparametric Bayesian Risk Estimation for Complex Extremes

Thomas Lugrin

Extreme events are responsible for huge material damage and are costly in terms of their human and economic impacts. They strike all facets of modern society, such as physical infrastructure and insurance companies through environmental hazards, banking an ...

EPFL2018

Quantifying Interdependent Privacy Risks with Location Data

Jean-Pierre Hubaux, Reza Shokri, Kévin Clément Huguenin, Mathias Jacques Jean-Marc Humbert, Alexandra-Mihaela Olteanu

Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the ...

Ieee Computer Soc2017

Principled Parallel Mean-Field Inference for Discrete Random Fields

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Timur Bagautdinov

Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and in many situations can be impractical. Thus, in practice, various parallel te ...

2016

A Theory Agenda for Component-Based Design

Joseph Sifakis, Simon Bliudze

The aim of the paper is to present a theory agenda for component-based design based on results that motivated the development of the BIP component framework, to identify open problems and discuss further research directions. The focus is on proposing a sem ...

Springer International Publishing2015

Disabling conditional inferences: An EEG study

Mariia Kaliuzhna

Although the Modus Ponens inference is one of the most basic logical rules, decades of conditional reasoning research show that it is often rejected when people consider stored background knowledge about potential disabling conditions. In the present study ...

Pergamon-Elsevier Science Ltd2014

Accurate Directional Inference for Vector Parameters in Linear Exponential Families

Anthony Christopher Davison

We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value i ...

American Statistical Association2014

What are the Odds? Probabilistic programming in Scala

Tiark Rompf, Nada Amin, Sandro Stucki, Manohar Jonnalagedda

Probabilistic programming is a powerful high-level paradigm for probabilistic modeling and inference. We present Odds, a small domain-specific language (DSL) for probabilistic programming, embedded in Scala. Odds provides first-class support for random var ...

ACM2013

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Pascal Fua, Aurélien Lucchi, Yunpeng Li

We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphic ...

2013

Dispersion operators and resistant second-order functional data analysis

Victor Panaretos, David Kraus

Inferences related to the second-order properties of functional data, as expressed by covariance structure, can become unreliable when the data are non-Gaussian or contain unusual observations. In the functional setting, it is often difficult to identify a ...

Oxford University Press2012

Asymptotic Inference For Partially Observed Branching Processes

Victor Panaretos, Andrea Kraus

We consider the problem of estimation in a partially observed discrete-time Galton-Watson branching process, focusing on the first two moments of the offspring distribution. Our study is motivated by modelling the counts of new cases at the onset of a stoc ...

Applied Probability Trust2011

Interpolation and Symbol Elimination

Laura Ildiko Kovacs

We prove several results related to local proofs, interpolation and superposition calculus and discuss their use in predicate abstraction and invariant generation. Our proofs and results suggest that symbol-eliminating inferences may be an interesting alte ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2009

Fuzzy Trust Aggregation And Personalized Trust Inference In Virtual Social Networks

Mohsen Lesani

Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of tim ...

2009