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Found 5 papers, 1 papers with code
When Redundancy Matters: Machine Teaching of Representations
In traditional machine teaching, a teacher wants to teach a concept to a learner, by means of a finite set of examples, the witness set.
Predictable Artificial Intelligence
We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key indicators of present and future AI ecosystems.
Fairness and Missing Values
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).
Forgetting and consolidation for incremental and cumulative knowledge acquisition systems
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.
On the definition of a general learning system with user-defined operators
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.
