Finding a high-quality feasible solution to a combinatorial optimization...
While pre-trained language models (PLMs) have become a de-facto standard...
Reinforcement learning has recently shown promise in learning quality
so...
In this paper, we consider stochastic multi-armed bandits (MABs) with
he...
Bootstrapping has been a primary tool for uncertainty quantification, an...
NeurIPS 2019 AutoDL challenge is a series of six automated machine learn...
We design and implement a ready-to-use library in PyTorch for performing...
In this paper, a neural architecture search (NAS) framework is proposed ...
Data augmentation is an indispensable technique to improve generalizatio...
In this paper, we present a new class of Markov decision processes (MDPs...
In this paper, we focus on the supervised learning problem with corrupte...
Performance of data-driven network for tumor classification varies with
...
In this paper, we propose an uncertainty-aware learning from demonstrati...