Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: This blogpost covers "Auction Learning as a Two-Player Game" by Rahme et al that appeared in ICLR 2021.  The blogpost is very well written and assumes very little knowledge about Auction Theory making it an accessible and interesting read to a larger ICLR audience. The author introduces auctions and illustrates the main concepts with very simple and easy to understand examples.  Using deep learning to accelerate mechanism design and auction theory in particular is a relatively new area of research. The author does a very good job at explaining why this research direction is interesting, where the contributions of the paper fit in this research landscape, how it connects to other fields and why researchers in Deep Learning should care about Mechanism Design (and vice versa).

Beyond mechanism design, the authors explores the similarities and difference between the algorithm introduced in the paper (ALGnet) and Generative Adversarial Networks (GANs), a popular approach used in computer visions to generate images that is also based on a two player game.  The blogposts contains very relevant references on GANs and convergence to Nash equilibriums in zero sum games and others.

I think that this blogpost provides great content and added value to the reader and therefore I recommend it's acceptance. 

I do have some remarks and comments that I think could improve the blogpost: 

1- Individual Rationality is not defined in the text.  

2- Maybe explicitly mentioning that IR refers to Individual Rationality. 

3- It's maybe clearer to refer to the amount that the auctioneer makes as the **revenue instead of the payment** since we already have a payment function which could confuse the reader.

4- In the last sentence before the section *Mechanism Design As Learning*,  "... maximizing payment [revenue] also requires carefully choosing a **and p**". (Optimizing p is also important).

5- In the section *Why is the GAN approach more efficient?*. You mention types,  " ... the mapping from **types** to optimal misreports ...". Readers not familiar with auction theory might not correctly infer what a type is. 

6- Maybe it's worth mentioning that ALGnet adapts better to non-stationary auctions? Related to the stationarity of its loss function.

7-  (Minor) The *Recap on GANs* section has only one sentence, is it worth making a section out of it? 
review_title: Great content, very enjoyable read.

Decision: Accept