Zobrazeno 1 - 10
of 3 893
pro vyhledávání: '"Thiessen, P."'
Autor:
Bressan, Marco, Brukhim, Nataly, Cesa-Bianchi, Nicolò, Esposito, Emmanuel, Mansour, Yishay, Moran, Shay, Thiessen, Maximilian
Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a false negative prediction can lead to worse consequences than a false po
Externí odkaz:
http://arxiv.org/abs/2412.08012
Go-or-grow approaches represent a specific class of mathematical models used to describe populations where individuals either migrate or reproduce, but not both simultaneously. These models have a wide range of applications in biology and medicine, c
Externí odkaz:
http://arxiv.org/abs/2412.05191
In this paper, we first propose a natural generalization of the well-known compare-and-swap object, one that replaces the equality comparison with an arbitrary comparator. We then present a simple wait-free universal construction using this object an
Externí odkaz:
http://arxiv.org/abs/2410.19102
Autor:
Thießen, Thore, Vahrenhold, Jan
Publikováno v:
Thore Thie{\ss}en and Jan Vahrenhold. Optimal offline ORAM with perfect security via simple oblivious priority queues. In 35th International Symposium on Algorithms and Computation (ISAAC 2024), 18 pages. 2024
Oblivious RAM (ORAM) is a well-researched primitive to hide the memory access pattern of a RAM computation; it has a variety of applications in trusted computing, outsourced storage, and multiparty computation. In this paper, we study the so-called o
Externí odkaz:
http://arxiv.org/abs/2409.12021
Autor:
Sokolov, Georgy, Thiessen, Maximilian, Akhmejanova, Margarita, Vitale, Fabio, Orabona, Francesco
We study the problem of learning the clusters of a given graph in the self-directed learning setup. This learning setting is a variant of online learning, where rather than an adversary determining the sequence in which nodes are presented, the learn
Externí odkaz:
http://arxiv.org/abs/2409.01428
Autor:
Bressan, Marco, Cesa-Bianchi, Nicolò, Esposito, Emmanuel, Mansour, Yishay, Moran, Shay, Thiessen, Maximilian
Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning models that are *interpretable* by humans. In this work we study such quest
Externí odkaz:
http://arxiv.org/abs/2406.10529
Autor:
Stocker, Markus, Snyder, Lauren, Anfuso, Matthew, Ludwig, Oliver, Thießen, Freya, Farfar, Kheir Eddine, Haris, Muhammad, Oelen, Allard, Jaradeh, Mohamad Yaser
Literature is the primary expression of scientific knowledge and an important source of research data. However, scientific knowledge expressed in narrative text documents is not inherently machine reusable. To facilitate knowledge reuse, e.g. for syn
Externí odkaz:
http://arxiv.org/abs/2405.13129
We study the problem of learning a binary classifier on the vertices of a graph. In particular, we consider classifiers given by monophonic halfspaces, partitions of the vertices that are convex in a certain abstract sense. Monophonic halfspaces, and
Externí odkaz:
http://arxiv.org/abs/2405.00853
Autor:
Nykänen, Anton, Thiessen, Leander, Borrelli, Elsi-Mari, Krishna, Vijay, Knecht, Stefan, Pavošević, Fabijan
Quantum chemistry simulations offer a cost-effective way for computational design of BODIPY photosensitizers with potential use in photodynamic therapy (PDT). However, accurate predictions of photophysical properties, such as excitation energies, pos
Externí odkaz:
http://arxiv.org/abs/2404.16149
Linearizable datastores are desirable because they provide users with the illusion that the datastore is run on a single machine that performs client operations one at a time. To reduce the performance cost of providing this illusion, many specialize
Externí odkaz:
http://arxiv.org/abs/2404.05470