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Scalable greedy algorithms for transfer learning

Publications associées (39)

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Theory and Algorithms for Hypothesis Transfer Learning

Ilja Kuzborskij

The design and analysis of machine learning algorithms typically considers the problem of learning on a single task, and the nature of learning in such scenario is well explored. On the other hand, very often tasks faced by machine learning systems arrive ...

EPFL2018

Toward Multimodal Analytics in Ubiquitous Learning Environments

Maria Jesus Rodriguez Triana

While Ubiquitous Learning Environments (ULEs) have shown several benefits for learning, they pose challenges for orchestration. Teachers need to be aware of the learning process, which is difficult to achieve when it occurs across a heterogeneous set of sp ...

CEUR2017

The technological environment that supports the learning process tends to be the main data source for Learning Analytics. However, this trend leaves out those parts of the learning process that are not computer-mediated. To overcome this problem, involving ...

CEUR2017

LPN in Cryptography

Sonia Mihaela Bogos

The security of public-key cryptography relies on well-studied hard problems, problems for which we do not have efficient algorithms. Factorization and discrete logarithm are the two most known and used hard problems. Unfortunately, they can be easily solv ...

EPFL2017

Efficient Algorithms for the Data Exchange Problem

Michael Christoph Gastpar, Sameer Pawar

In this paper, we study the data exchange problem, where a set of users is interested in gaining access to a common file, but where each has only partial knowledge about it as side-information. Assuming that the file is broken into packets, the side-inform ...

Institute of Electrical and Electronics Engineers2016

Transfer Learning through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

In this work we study the binary transfer learning problem involving 10^2 -10^3 sources. We focus on how to select sources from the large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer lea ...

Idiap2015

Transfer Learning Through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

We study the binary transfer learning problem, focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer learning setting where one does not have direct ...

Springer Int Publishing Ag2015

Reduced Basis Techniques For Nonlinear Conservation Laws

Alfio Quarteroni

In this paper we present a new reduced basis technique for parametrized nonlinear scalar conservation laws in presence of shocks. The essential ingredients are an efficient algorithm to approximate the shock curve, a procedure to detect the smooth componen ...

EDP Sciences2015

Asynchronized Concurrency: The Secret to Scaling Concurrent Search Data Structures

Rachid Guerraoui, Vasileios Trigonakis, Tudor Alexandru David

We introduce "asynchronized concurrency (ASCY),'' a paradigm consisting of four complementary programming patterns. ASCY calls for the design of concurrent search data structures (CSDSs) to resemble that of their sequential counterparts. We argue that ASCY ...

2015

Near-Optimally Teaching the Crowd to Classify

Andreas Krause, Amin Karbasi, Ilija Bogunovic

How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also moti- vated by challenges in data-driven online educa- tion. We propose a ...

2014