3 papers
cs.AI2026
Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
(Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning. A compelling approach to improve samp…
cs.LG2026
Approximation-Free Differentiable Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabular data. However, training ac…
cs.LG2024
Vanilla Gradient Descent for Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +1
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, es…