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关于因果机器学习、强化学习与生活的笔记

less than 1 minute read

Published:

不再沉默。从今天开始我向伟大的Tony学习，写博客（当然他主要是写日记）。写文字最好的一点是，毫无压力地做自己，所以我会写中文，这样更自在。

Notes on Causal ML, Reinforcement Learning, and Life

5 minute read

Published:

No more silence. Starting today, I am going to learn from the great Tony and write a blog (though he mostly writes a diary). The best thing about writing is being able to be myself without pressure. I will write in Chinese because it feels more natural—but here is an English version too.

portfolio

publications

talks

Responsible Recommendation Systems through the Lens of Conformal Prediction and Causal Inference

Published:

Recommendation systems play a crucial role in today’s massive information systems. Unfortunately, existing methods developed for maximizing utility of recommendation systems will lead to issues such as unfair item exposure allocation and popularity bias. In this talk, I will discuss two of our recent works. In the first work, we propose an efficient method for fairness utility trade-off in ranking, which transforms deterministic rankers to stochastic ones with finite-sample utility guarantees and improved fairness. In the second work, we formulate debiasing recommendation system as a multiple causal inference problem, and handle confounding bias by learning identifiable latent confounders.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.