Lalit Jain

NEXT: a system for real-world development, evaluation, and application of active learning

2015

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather …

The Landscape of Non-Convex Quadratic Feasibility

2018

Motivated by applications such as ordinal embedding and collaborative ranking, we formulate homogeneous quadratic feasibility as an unconstrained, non-convex minimization problem. Our work aims to understand the landscape (local minimizers and global minimizers) of the …

Improved Algorithms for Agnostic Pool-based Active Classification

2021 · arXiv (Cornell University)

We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active learning in the agnostic setting are inspired by the CAL algorithm where each query is uniformly …