Publications associées à A Constant-factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem

Publications associées (24)

Privatized graph federated learning

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023

Exact Algorithms for Continuous Pricing with Advanced Discrete Choice Demand Models

Michel Bierlaire, Fabian Alejandro Torres Duran, Tom Haering

We present the Breakpoint Exact Algorithm (BEA) together with a Spatial Branch and Bound (B&B), and Spatial Branch and Benders Decomposition (B&BD) approach to tackle the uncapacitated choice-based pricing problem (CPP) where demand is captured by a disc ...

2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Nicolas Würsch, Laure-Emmanuelle Perret Aebi, Marine Dominique Cauz

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023

Optimizing Lattice-based Post-Quantum Cryptography Codes for High-Level Synthesis

Francesco Regazzoni, Andrea Guerrieri, Andres Upegui

High-level synthesis is a mature Electronics Design Automation (EDA) technology for building hardware design in a short time. It produces automatically HDL code for FPGAs out of C/C++, bridging the gap from algorithm to hardware. Nevertheless, sometimes th ...

IEEE2022

Sparse random hypergraphs: Non-backtracking spectra and community detection

Ludovic Théo Stephan

We consider the community detection problem in a sparse q-uniform hypergraph G, assuming that G is generated according to the Hypergraph Stochastic Block Model (HSBM). We prove that a spectral method based on the non-backtracking operator for hypergraphs w ...

IEEE COMPUTER SOC2022

Space-Efficient Representations of Graphs

Jakab Tardos

With the increasing prevalence of massive datasets, it becomes important to design algorithmic techniques for dealing with scenarios where the input to be processed does not fit in the memory of a single machine. Many highly successful approaches have emer ...

EPFL2022

Adversarial-Free Speaker Identity-Invariant Representation Learning for Automatic Dysarthric Speech Classification

Ina Kodrasi

Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique to learn speaker identity-invariant re ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Engineering of Existing Structures: The Need and Place for Non-destructive Evaluation

Guillaume Justin Henriques, Eugen Brühwiler, Numa Joy Bertola, Thomas Schumacher

Structural engineers today are still educated mainly to design new structures. This ignores the fact that structures already exist and need to be maintained. Students are thus often ill-equipped to deal with maintenance and preservation of existing structu ...

2022

Low-Level Physiological Implications of End-to-End Learning of Speech Recognition

Philip Neil Garner

Current speech recognition architectures perform very well from the point of view of machine learning, hence user interaction. This suggests that they are emulating the human biological system well. We investigate whether the inference can be inverted to p ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Admissibility of Uncertain Injections in Quadratic Algebraic Systems

Jean-Yves Le Boudec, Cong Wang, Eleni Stai

We study the admissibility problem in multivariate algebraic systems, such as ac electrical networks, where the power injection is quadratic in the state. The goal of such systems is to ensure that the state stays in some security set (e.g., magnitudes of ...

2021

Encrypted DNS double right arrow Privacy? A Traffic Analysis Perspective

Carmela González Troncoso, Sandra Deepthy Siby

Virtually every connection to an Internet service is preceded by a DNS lookup. Lookups are performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations s ...

INTERNET SOC2020

Edge colorings of graphs without monochromatic stars

Abhishek Methuku

In this note, we improve on results of Hoppen, Kohayakawa and Lefmann about the maximum number of edge colorings without monochromatic copies of a star of a fixed size that a graph on n vertices may admit. Our results rely on an improved application of an ...

Elsevier2020

Permutation-based Decoding of Reed-Muller Codes in Binary Erasure Channel

Rüdiger Urbanke, Kirill Ivanov

In this paper, we consider the problem of decoding Reed-Muller (RM) codes in binary erasure channel. We propose a novel algorithm, which exploits several techniques, such as list recursive (successive cancellation) decoding based on Plotkin decomposition, ...

IEEE2019

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Yusuf Leblebici, Severin Sidler, Alessandro Fumarola

Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, ...

IEEE2019

Learning Hawkes Processes from a Handful of Events

Patrick Thiran, Matthias Grossglauser, Farnood Salehi, William Trouleau

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only ...

2019

SPar-K: a method to partition NGS signal data

Philipp Bucher, Romain Fernand Pietro Groux

We present SPar-K (Signal Partitioning with K-means), a method to search for archetypical chromatin architectures by partitioning a set of genomic regions characterized by chromatin signal profiles around ChIP-seq peaks and other kinds of functional sites. ...

Oxford University Press2019

Backpropagation-Friendly Eigendecomposition

Pascal Fua, Mathieu Salzmann, Wei Wang, Yinlin Hu, Zheng Dang

Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it with the Power Iteration method, particularly when dealing with large matri ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Collecting Experience Data from Remotely Hosted Learning Applications

Denis Gillet, Christophe Salzmann

The ability to integrate multiple learning applications from different organizations allows sharing resources and reducing costs in the deployment of learning systems. In this sense, Learning Tools Interoperability (LTI) is the main current leading technol ...

SPRINGER2018

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Ali H. Sayed

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

IEEE2018

Learning Monocular 3D Human Pose Estimation from Multi-view Images

Pascal Fua, Mathieu Salzmann, Frédéric Meyer, Helge Jochen Rhodin, Isinsu Katircioglu, Victor Constantin

Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Ma ...

IEEE2018