Search Results for author: José Hernández-Orallo

Found 15 papers, 4 papers with code

Compute and Energy Consumption Trends in Deep Learning Inference

no code implementations12 Sep 2021 Radosvet Desislavov, Fernando Martínez-Plumed, José Hernández-Orallo

The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters.

Conditional Teaching Size

no code implementations29 Jun 2021 Manuel Garcia-Piqueras, José Hernández-Orallo

Recent research in machine teaching has explored the instruction of any concept expressed in a universal language.

Automating Data Science: Prospects and Challenges

no code implementations12 May 2021 Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

AutoML BIG-bench Machine Learning

The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition

4 code implementations12 Sep 2019 Benjamin Beyret, José Hernández-Orallo, Lucy Cheke, Marta Halina, Murray Shanahan, Matthew Crosby

Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents.

Fairness and Missing Values

1 code implementation29 May 2019 Fernando Martínez-Plumed, Cèsar Ferri, David Nieves, José Hernández-Orallo

To support this claim, (1) we analyse the sources of missing data and bias, and we map the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should not be treated as the uncomfortable ugly data that different techniques and libraries get rid of at the first occasion, and (3) we study the trade-off between performance and fairness when the rows with missing values are used (either because the technique deals with them directly or by imputation methods).

Decision Making Fairness +1

Analysing Results from AI Benchmarks: Key Indicators and How to Obtain Them

no code implementations20 Nov 2018 Fernando Martínez-Plumed, José Hernández-Orallo

The two-parameter IRT model provides two indicators (difficulty and discrimination) on the side of the item (or AI problem) while only one indicator (ability) on the side of the respondent (or AI agent).

Atari Games

General-purpose Declarative Inductive Programming with Domain-Specific Background Knowledge for Data Wrangling Automation

1 code implementation26 Sep 2018 Lidia Contreras-Ochando, César Ferri, José Hernández-Orallo, Fernando Martínez-Plumed, María José Ramírez-Quintana, Susumu Katayama

In this paper we propose to use IP as a means for automating repetitive data manipulation tasks, frequently presented during the process of {\em data wrangling} in many data manipulation problems.

Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour

no code implementations7 Jun 2018 Emilia Gómez, Carlos Castillo, Vicky Charisi, Verónica Dahl, Gustavo Deco, Blagoj Delipetrev, Nicole Dewandre, Miguel Ángel González-Ballester, Fabien Gouyon, José Hernández-Orallo, Perfecto Herrera, Anders Jonsson, Ansgar Koene, Martha Larson, Ramón López de Mántaras, Bertin Martens, Marius Miron, Rubén Moreno-Bote, Nuria Oliver, Antonio Puertas Gallardo, Heike Schweitzer, Nuria Sebastian, Xavier Serra, Joan Serrà, Songül Tolan, Karina Vold

The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.

Decision Making

Between Progress and Potential Impact of AI: the Neglected Dimensions

no code implementations2 Jun 2018 Fernando Martínez-Plumed, Shahar Avin, Miles Brundage, Allan Dafoe, Sean Ó hÉigeartaigh, José Hernández-Orallo

We reframe the analysis of progress in AI by incorporating into an overall framework both the task performance of a system, and the time and resource costs incurred in the development and deployment of the system.

Atari Games Board Games +1

Forgetting and consolidation for incremental and cumulative knowledge acquisition systems

no code implementations19 Feb 2015 Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, María José Ramírez-Quintana

The application of cognitive mechanisms to support knowledge acquisition is, from our point of view, crucial for making the resulting models coherent, efficient, credible, easy to use and understandable.

Definition and properties to assess multi-agent environments as social intelligence tests

no code implementations27 Aug 2014 Javier Insa-Cabrera, José Hernández-Orallo

Instead, in this paper we start from a parametrised definition of social intelligence as the expected performance in a set of environments with several agents, and we assess and derive tests from it.

On the definition of a general learning system with user-defined operators

no code implementations18 Nov 2013 Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, María-José Ramírez-Quintana

As a result, the architecture can be seen as a 'system for writing machine learning systems' or to explore new operators where the policy reuse (as a kind of transfer learning) is allowed.

BIG-bench Machine Learning Structured Prediction +1

Test cost and misclassification cost trade-off using reframing

no code implementations30 May 2013 Celestine Periale Maguedong-Djoumessi, José Hernández-Orallo

Many solutions to cost-sensitive classification (and regression) rely on some or all of the following assumptions: we have complete knowledge about the cost context at training time, we can easily re-train whenever the cost context changes, and we have technique-specific methods (such as cost-sensitive decision trees) that can take advantage of that information.

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