We have briefly touched some concepts of causal inference in [1, 2]. This post introduces some more specific works which apply causal inference in recommendation systems. Some works need to know the background of backdoor and frontdoor adjustments. So we will introduce them first. Backdoor and frontdoor adjustment Suppose we have a causal graph like …
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This post runs a simple simulation on using the Dollar-Cost-Average strategy to invest in QQQ vs. TQQQ, its 3x-leveraged ETF. In the simulation, QQQ will plunge 34% after 20 rounds. One round is a small up-and-down cycle – the index first moves up 1% then 3% down, until 34% down from the top. After reaching …
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Policy Generalist Deepmind has recently published a work named Gato. I find it interesting as Gato learns a multi-modal multi-task policy to many tasks such as robot arm manipulation, playing atari, and image captioning. I don’t think the original paper [2] has every detail of implementation but I’ll try to best summarize what I understand. …
7 years ago I posted one tutorial about recommendation systems. Now it is 2022 and there are many more advancements. This post will overview several latest ideas. CTR models Google’s recsys 2022 paper [1] introduces many practical details on their CTR models. First, to reduce training cost, there are 3 effective ways: applying bottleneck layers …
There are many advancements in new model architectures in AI domain. Let me overview these advancements in this post. Linear Compression Embedding LCE [1] is simply using a matrix to project one embedding matrix to another: , where . Pyramid networks, inception network, dhen, lce Perceiver and Perceiver IO Perceiver-based architectures [5,6] solve …
I start to feel the importance of simulating any practical problem before deploying an RL policy. If you cannot implement a reasonable simulator on your own, you are not clear about your environment and your model. It is then a pure gamble to me if we just train an RL policy offline without testing in …
I tried to search the answer for long time and came across this solution which works well: https://superuser.com/a/1464698/1279375
GFlowNet is the latest technique developed for solving combinatorial optimization problems [1]. I’ve prepared a series of deep dive slides for it (See GFlowNet deep dive). In this post, I just list a few more references. References [1] https://yoshuabengio.org/2022/03/05/generative-flow-networks/ [2] https://towardsdatascience.com/the-what-why-and-how-of-generative-flow-networks-4fb3cd309af0 [3] https://neurips.cc/media/neurips-2021/Slides/26729.pdf [4] https://www.youtube.com/watch?v=7W69-ffTs48 [5] https://milayb.notion.site/GFlowNet-Tutorial-919dcf0a0f0c4e978916a2f509938b00#afe03e54d6db43468f8dee3a3350f98a [6] http://folinoid.com/w/gflownet/
I learned about Markov Chain Monte Carlo (MCMC) algorithm a little bit during my phd but I did not record my thoughts back then. In this post, I revisit the core concept of MCMC, particularly focusing on illustrating the Metropolis-Hastings (MH) algorithm. What is the motivation of MCMC? Suppose you have observed some data . …
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There are several terms often used in the Ads ML domain. Let’s dive into them. total bid = ecpm bid + quality bid ecpm bid = paced bid * ecvr ecvr is “expected conversion rate”. Different ads can have different definitions of conversion [8] – e.g., link click, app install, or offsite conversion. ecvr = …
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