ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynam ...

In a matter of years, single-cell omics has matured from a pioneering technique employed by just a handful of specialized laboratories to become a ubiquitous feature of biological research and a key driver of scientific discovery. The widespread adoption a ...

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in ...

Ash dieback, caused by the fungal pathogen Hymenoscyphus fraxineus, is devastating ash tree populations across U.K. and Europe, with projections indicating that up to 80% of ash trees may die as a result of the disease. The extensive loss of this keystone ...

Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic fun ...

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

In 2019, the JPEG Standardization Committee initiated JPEG AI to define the first image coding specifications, taking advantage of an end-to-end learning-based coding approach. The JPEG AI specifications will be soon published as an international standard ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which a ...

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepa ...

This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method. ALO-VC enables any-to-any voice conversion using only one utterance from the target speaker, with only 47.5 ms future look-ahead. ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...

Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel alg ...

This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow ...

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Chat with Graph Search

Boosting Protein Graph Representations through Static-Dynamic Fusion

A clinical road map for single-cell omics

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

Leaf Level Ash Dieback Disease Detection and Online Severity Estimation With UAVs

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation

Efficient Continual Finite-Sum Minimization

JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

ALO-VC: Any-to-any Low-latency One-shot Voice Conversion

On double-descent in uncertainty quantification in overparametrized models

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Hough Forest With Optimized Leaves for Global Hand Pose Estimation With Arbitrary Postures

A Conditional Gradient-Based Augmented Lagrangian Framework

Non-intrusive reduced order modeling of nonlinear problems using neural networks