Publications by Emmanuel Abbé

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.

Accurate segmentation of coronary arteries remains a significant challenge in clinical practice, hindering the ability to effectively diagnose and manage coronary artery disease. The lack of large, annotated datasets for model training exacerbates this issue, limiting the development of automated tools that could assist radiologists. To address this, we introduce CM-UNet, which leverages self-supervised pre-training on unannotated datasets and transfer learning on limited annotated data, enabling accurate d

We introduce a code-based challenge for automated, open-ended mathematical discovery based on the

k

-server conjecture, a central open problem in competitive analysis. The task is to discover a potential function satisfying a large graph-structured system of simple linear inequalities. The resulting evaluation procedure is sound but incomplete: any violated inequality definitively refutes a candidate, whereas satisfying all inequalities does not by itself constitute a proof of the corresponding conjecture'

Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction

The study investigated gender bias in GPT-4's assessment of coronary artery disease risk by presenting identical clinical vignettes of men and women with and without psychiatric comorbidities. Results suggest that psychiatric conditions may influence GPT-4 ...

The problem of learning graphons has attracted considerable attention across several scientific communities, with significant progress over the re-cent years in sparser regimes. Yet, the current techniques still require diverg-ing degrees in order to succe ...

This paper investigates the construction of determin- istic measurement matrices preserving the entropy of a random vector with a given probability distribution. In particular, it is shown that for a random vector with i.i.d. discrete components, this is a ...

This paper explores a connection between randomness extraction and channel (source) coding problems. It is explained how efficient extractors can be used to define efficient coding schemes and reciprocally, a new deterministic extractor based on a polar coding scheme is proposed. Since the source model used in extractors for computer science (cryptography) do not assume i.i.d. or known distributions, a generalized polarization phenomenon for sources with block memory and unknown distributions is developed.

We derive generalization and excess risk bounds for neural networks using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural networks and b ...

Discovering efficient algorithms for solving complex problems has been an outstanding challenge in mathematics and computer science, requiring substantial human expertise over the years. Recent advancements in evolutionary search with large language models (LLMs) have shown promise in accelerating the discovery of algorithms across various domains, particularly in mathematics and optimization. However, existing approaches treat the LLM as a static generator, missing the opportunity to update the model with

In patients with stable Coronary Artery Disease (CAD), the identification of lesions which will be responsible of a myocardial infarction (MI) during follow-up remains a daily challenge. In this work, we propose to predict culprit stenosis by applying a de ...

We introduce Boolformer, a Transformer-based model trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for complex functions not seen during training, given their full truth table. Then, we demonstrate that even with incomplete or noisy observations, Boolformer is still able to find good approximate expressions. We evaluate Boolformer on a broad set of real-world binary classification datasets, demonstrating its potential as an interpre

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in finding solutions at low density. This phenomenon has been justified numerically by the existence of subdominant and dense connected regions of solutions, which are accessible by simple learning algorithms. In this paper, we establish formally such a phenomen

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

In this paper, a polar code for the m-user multiple access channel (MAC) with binary inputs is constructed. In particular, Arıkan’s polarization technique applied individually to each user will polarize any m-user binary input MAC into a finite collection of extremal MACs. The extremal MACs have a number of desirable properties: (i) the ‘uniform sum rate’1 of the original channel is not lost, (ii) the extremal MACs have rate regions that are not only polymatroids but matroids and thus (iii) their uniform su

In 1948, Shannon used a probabilistic argument to show the existence of codes achieving a maximal rate defined by the channel capacity. In 1954, Muller and Reed introduced a simple deterministic code construction based on polynomial evaluations, which was conjectured and eventually proven to achieve capacity. Meanwhile, polarization theory emerged as an analytic framework to prove capacity results for a variation of RM codes - the polar codes. Polarization theory further gave a powerful framework for variou

Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic worldwide triggering extensive debate about its predictive power, its potential uses, and its wider implications. Recent publications have demonstrated that ChatGPT can correct ...

Polar codes allow to perform lossless compression of i.i.d. sources at the lowest rate with low encoding and decoding complexity. In this paper, it is shown that for binary sources, there exist “universal polar codes” which can compress any source of low enough entropy, without requiring knowledge of the source 1 i−1 n→∞ distribution. While this result does not extend to q-ary sources, n|{i:H(Ui|U )>1−δ}| −→ H(p), (2) it is shown how it extends to q-ary sources which belong to a restricted family. An analog

Recent studies have shown that large language models (LLMs), especially smaller ones, often lack robustness in grade school math (GSM) reasoning. In particular, they tend to experience performance drops when faced with distribution shifts, such as changes to numerical or nominal variables, or insertions of distracting clauses. A possible strategy to address this involves generating synthetic data to further "instantiate" reasoning problems on potential variations. In this work, we instead focus on the strat

Background/Introduction The use of coronary computed tomography angiography (CCTA) in managing high-risk acute coronary syndrome (ACS) patients is increasingly common. Yet, its role remains unvalidated in this context, posing challenges to traditional clinical decision-making algorithms. Physicians, accustomed to a hierarchical and structured approach involving symptom assessment, electrocardiograms, and biomarkers for deciding on the necessity of invasive coronary angiography (ICA), now face dilemmas when

Emmanuel Abbé

Chat with Graph Search

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography

k-server-bench: Automating Potential Discovery for the $k$-Server Conjecture

Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective

Gender Bias in AI's Perception of Cardiovascular Risk

Learning Sparse Graphons And The Generalized Kesten-Stigum Threshold

Adaptive sensing using deterministic partial Hadamard matrices

Polarization and randomness extraction

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks

Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning

Predicting future myocardial infarction from angiographies with deep learning

Boolformer: Symbolic Regression of Logic Functions with Transformers

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Binary perceptron: efficient algorithms can find solutions in a rare well-connected cluster

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Polar codes for the m-user multiple access channel and matroids

Polynomial Freiman-Ruzsa, Reed-Muller codes and Shannon capacity

ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story?

Universal Source Polarization and Spare Recovery

AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking

Assessing the need for coronary angiography in high-risk acute coronary syndrome patients using artificial intelligence and computed tomography