Publications related to Cumulants of Hawkes Processes are Robust to Observation Noise

Related publications (11)

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recen ...

ICLR2025

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Zeki Doruk Erden

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

EPFL2025

Improved bounds and inference on optimal regimes

Mats Julius Stensrud, Aaron Leor Sarvet, Julien David Laurendeau

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, t ...

Informa UK Limited2025

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

OASIS: An integrated optimisation framework for activity scheduling

Janody Pougala

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

EPFL2024

Distribution Testing with a Confused Collector

Nathaniel Idein Harms

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learni ...

Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing2024

Infinite-width limit of deep linear neural networks

Maria Colombo, Xavier Fernandez-Real Girona, Lénaïc Chizat

This paper studies the infinite-width limit of deep linear neural networks (NNs) initialized with random parameters. We obtain that, when the number of parameters diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a ...

Hoboken2024

Multi-Scale and Multimodal Species Distribution Modeling

Devis Tuia, Benjamin Alexander Kellenberger, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

Springer Nature Link2024

Exploring crystallographic texture manipulation in stainless steels via laser powder bed fusion: insights from neutron diffraction and machine learning

Roland Logé

Laser powder bed fusion of metals (PBF-LB/M) is a pivotal additive manufacturing technique that enables the production of intricate components. In addition to enabling the production of complex shapes, it allows for a high degree of freedom in manipulating ...

Taylor & Francis2024

No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO

Andréa Serge Alfredo Miele, Daniil Pyatko, Caglar Gulcehre, Skander Moalla

Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fitting n ...

2024

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

Jean-Philippe Thiran, Roser Vinals Terres

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Zenodo2024