Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance

no code implementations • 2 Nov 2022 • Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, Bing-Rong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork

As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design.

Learning-To-Rank regression

Reinforced Pipeline Optimization: Behaving Optimally with Non-Differentiabilities

no code implementations • 27 Sep 2018 • Aijun Bai, Dongdong Chen, Gang Hua, Lu Yuan

Many machine learning systems are implemented as pipelines.

object-detection Object Detection

Multi-Object Tracking and Identification over Sets

no code implementations • 25 May 2016 • Aijun Bai

The ability for an autonomous agent or robot to track and identify potentially multiple objects in a dynamic environment is essential for many applications, such as automated surveillance, traffic monitoring, human-robot interaction, etc.

Multi-Object Tracking Object

Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search

no code implementations • NeurIPS 2013 • Aijun Bai, Feng Wu, Xiaoping Chen

Monte-Carlo tree search is drawing great interest in the domain of planning under uncertainty, particularly when little or no domain knowledge is available.

Thompson Sampling