Publications associées à Inductive programming

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...

2022

ADAGRAD Avoids Saddle Points

Kimon Antonakopoulos, Xiao Wang

Adaptive first-order methods in optimization are prominent in machine learning and data science owing to their ability to automatically adapt to the landscape of the function being optimized. However, their convergence guarantees are typically stated in te ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order t ...

EPFL2021

A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples

Pierre Dillenbourg, Jennifer Kaitlyn Olsen, Louis Pierre Faucon

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. I ...

ASSOC COMPUTING MACHINERY2020

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Ali H. Sayed, Stefan Vlaski, Jie Chen, Roula Nassif

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Deductive Synthesis and Repair

Etienne Kneuss

In this thesis, we explore techniques for the development of recursive functional programs over unbounded domains that are proved correct according to their high-level specifications. We present algorithms for automatically synthesizing executable code, st ...

EPFL2016

Optimizing Majority-Inverter Graphs with Functional Hashing

Giovanni De Micheli, Mathias Soeken, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù

A Majority-Inverter Graph (MIG) is a recently introduced logic representation form whose algebraic and Boolean properties allow for efficient logic optimization. In particular, when considering logic depth reduction, MIG algorithms obtained signific ...

IEEE2016

Induction for SMT Solvers

Viktor Kuncak, Andrew Joseph Reynolds

Satisfiability modulo theory solvers are increasingly being used to solve quantified formulas over structures such as integers and term algebras. Quantifier instantiation combined with ground decision procedure alone is insufficient to prove many formulas ...

Springer-Verlag2015

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014

A Programming Workshop using the Robot "Thymio II": The Effect on the Understanding by Children

Francesco Mondada, Michael Bonani, Stéphane Magnenat, Fanny Riedo

Robots are both fascinating objects for the general public and devices whose conception, understanding and pro- gramming involve many fields. This unique combination makes them an ideal tool for introducing science and technology to children. This paper pr ...

IEEE2012