Publications related to Asymptotics for geometric combinatorial optimization problems

Geometric Learning: Leveraging differential geometry for learning and control

In this thesis, we concentrate on advancing high-level behavioral control policies for robotic systems within the framework of Dynamical Systems (DS). Throughout the course of this research, a unifying thread weaving through diverse fields emerges, and tha ...

EPFL2024

Anomalous and Chern topological waves in hyperbolic networks

Romain Christophe Rémy Fleury, Haoye Qin, Aleksi Antoine Bossart, Zhechen Zhang

Hyperbolic lattices are a new type of synthetic materials based on regular tessellations in non-Euclidean spaces with constant negative curvature. While so far, there has been several theoretical investigations of hyperbolic topological media, experimental ...

2024

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Silvestro Micera, Simone Romeni

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

2024

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

The task of discovering equivalent entities in knowledge graphs (KGs), so-called KG entity alignment, has drawn much attention to overcome the incompleteness problem of KGs. The majority of existing techniques learns the pointwise representations of entiti ...

London2023

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Aude Billard, Bernardo Fichera

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

SPRINGER INTERNATIONAL PUBLISHING AG2023

Approximation Algorithms for Allocation and Network Design

Etienne Michel François Bamas

In this thesis, we give new approximation algorithms for some NP-hard problems arising in resource allocation and network design. As a resource allocation problem, we study the Santa Claus problem (also known as the MaxMin Fair Allocation problem) in which ...

EPFL2023

Distributed Optimization with Byzantine Robustness Guarantees

Lie He

As modern machine learning continues to achieve unprecedented benchmarks, the resource demands to train these advanced models grow drastically. This has led to a paradigm shift towards distributed training. However, the presence of adversariesâwhether ma ...

EPFL2023

A structured prediction approach for robot imitation learning

Aude Billard, Iason Batzianoulis, Anqing Duan

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

London2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Maryam Kamgarpour, Orcun Karaca

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

2023

Asymptotics for geometric combinatorial optimization problems

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Geometric Learning: Leveraging differential geometry for learning and control

Anomalous and Chern topological waves in hyperbolic networks

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Approximation Algorithms for Allocation and Network Design

Distributed Optimization with Byzantine Robustness Guarantees

A structured prediction approach for robot imitation learning

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Geometric Learning: Leveraging differential geometry for learning and control

Anomalous and Chern topological waves in hyperbolic networks

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Approximation Algorithms for Allocation and Network Design

A structured prediction approach for robot imitation learning

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Distributed Optimization with Byzantine Robustness Guarantees