In-context learning is the ability of a model to condition on a prompt sequence consisting of
in-context examples (input-output pairs corresponding to some task) along with a new query …

We prove that it is NP-hard to properly PAC learn decision trees with queries, resolving a
longstanding open problem in learning theory (Bshouty 1993; Guijarro–Lavín–Raghavan …

We propose a simple generalization of standard and empirically successful decision tree
learning algorithms such as ID3, C4. 5, and CART. These algorithms, which have been …

Random forests are classical ensemble algorithms that construct multiple randomized
decision trees and aggregate their predictions using naive averaging.\citet {zhou2019deep} …

We propose a general framework for end-to-end learning of data structures. Our framework
adapts to the underlying data distribution and provides fine-grained control over query and …

This thesis explores questions surrounding the foundations of intelligence, both artificial and
natural. The first part focuses on the algorithmic and statistical underpinnings of modern …

The problem of predicting the future has a long history, and it has become ever more
important in recent years, as uncertainties arise with evolving geopolitical crises and quickly …

Otitis is a disease that occurs in the middle ear in the form of inflammation. This research
aims to develop an analysis model for the classification of Otitis disease based on …

Otitis is a disease that occurs in the middle ear in the form of inflammation. This research
aims to develop an analysis model for the classification of Otitis disease based on …