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Found 5 papers, 4 papers with code
Deep Discriminative to Kernel Density Graph for In- and Out-of-distribution Calibrated Inference
However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples.
Simplest Streaming Trees
In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations.
Towards a theory of out-of-distribution learning
Establishing proper and universally agreed-upon definitions for these learning setups is essential for thoroughly exploring the evolution of ideas across different learning scenarios and deriving generalized mathematical bounds for these learners.
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
