Summary and Contributions: This paper revisits the problem of designing revenue-optimal mechanisms via deep learning. The mechanism specification is trained with the goal of maximizing revenue, for a given allocation problem and valuations drawn from fixed distributions, subject to individual rationality and incentive compatibility conditions (encoded as penalties). This paper builds on prior work by certifying the incentive compatibility using adversarial nets, which essentially strengthens the incentive compatibility guarantees and certifies those guarantees. Much of the technical work involves adapting prior network architectures for mechanism design (RegretNet) to be compatible with known verification/certification techniques. The authors evaluate the resulting trained networks empirically, and argue that their method does improve incentive properties (relative to prior methods) at the expense of revenue.
Strengths: This paper makes a solid contribution to the growing literature on mechanism design via deep learning. A downside of existing solutions is that they tend to find mechanisms that outperform known revenue bounds, presumably by relaxing incentive compatibility constraints. Since it is difficult to understand how agents will behave when faced with "almost incentive compatibility," it's important to provide tools to make this more robust. The authors make a significant step forward in that regard. The paper is written well and I enjoyed reading it. The conceptual contribution is to connect two established methods (RegretNet and adversarial verification), but the authors do a good job of describing the challenges and modifications needed to make this work and the technical contribution is non-trivial.
Weaknesses: It's left ambiguous how much of the revenue impact is due to the improved IC enforcement, and how much is due to the modified network archetecture. I would have liked to see slightly more experimentation here; e.g., testing the impact of different modifications made separately. But this is not a deal-breaker, and overall I found the empirical evaluation satisfactory.
Correctness: As far as I understand the claims and method are correct.
Clarity: The paper is well-written.
Relation to Prior Work: The relationship with prior work is clear.
Additional Feedback: Post-rebuttal: I sympathize with the authors' point about it being difficult to do a fair comparison with RegretNet due to tuning issues. My overall opinion of the paper is still positive.
Summary and Contributions: This work builds on prior work in deep learning for auctions by modifying the neural network formulation in order to admit IP based techniques for NN verification to the literature on deep learning for auctions.
Strengths: Providing guarantees for learned auctions is a key element of being able to actually use the auctions generated through machine learning (or continue to keep looking). While some of the prior models build in full strategy-proofness from the start, performance and scalability considerations may constrain methods used to those that are approximately strategyproof. This is a very promising direction and the work will likely prove to be very valuable.
Weaknesses: The work does not adequately prove that its certificates are correct. [edit - addressed in response] The work leaves open a number of questions on the scalability and cost of certification that would make it a much stronger work. Can we use this approach to adjust training times or architecture to admit stronger guarantees on empirical regret? Being able to show this would be a significant improvement. What is the performance cost of a certificate of regret? This is mentioned as an open question (as in, an open question why performance is not as good as original), but should be described within this work. Without this or the above, it is hard to extrapolate from the results included to understand broader ramifications.
Correctness: I have not verified the correctness of the approach. Additionally, the lack of a proof that the certificates are correct (even if it is a simple consequence from the literature described in section 2.2) makes it hard to verify without referring to the code.
Clarity: Much of the paper is presented as a circuit of the challenges associated with the work rather than a presentation of the results and approach. That circuit is discussed nicely and connects clearly to the literature but will be much stronger with a more direct framing of the results, and theorems surrounding correctness. Figure 1 is a very helpful representation of the scale and amounts of strategic deviation. There are informal elements that occasionally make the work hard to follow - see Line 183.
Relation to Prior Work: The paper very clearly discusses what techniques are used and from where they are derived.
Summary and Contributions: This paper describes the training of a neural network to compute an approximately strategyproof and revenue maximizing auction with one or two buyers with additive valuations uniformly drawn from [0,1], and the case of three items and three buyers for which the IR constraint is not enforced. I have two main concerns: 1. The difference from  who initiated this line of inquiry is not clearly explained and in particular the experimental results obtained here are not compared with the results of . It is briefly mentioned that the approach in the current paper provides certifiability but this is only very briefly discussed in section 2.2 and my impression (I am not an expert) is that this is not a novel approach but an adaptation taken from known literature. In addition section 5 mentions explicitly that the results here are “not as strong as those in ”. 2. There is a series of papers by Cai, Daskalakis, and Weinberg that describe how to construct linear programs that exactly solve the problems study here, especially for additive valuations. The paper does not discuss how it compares to that literature. In particular, it seems to me that it is quite straight-forward to solve the simple cases considered in this paper using the linear program approach of Cai et al. Note that that approach gives an exact optimal solution on all fronts.
Strengths: See above
Weaknesses: See above
Correctness: See above
Clarity: See above
Relation to Prior Work: See above
Summary and Contributions: The RegretNet architecture  provides a way learning neural network representations of (approximately) revenue-optimal auctions. However, bounds on this degree of approximation are based on a gradient ascent optimization that does not guarantee optimality. This paper proposed a variant of the RegretNet architecture which is allows this optimization problem to be formulated as MIP and uses it to provide certified bounds on the approximation, which show that the original bounds were moderately loose but within a factor of two.
Strengths: This paper is part of a growing literature on "differentiable economics" which I am very enthusiastic about. It combines this with techniques from another active area, certifying the performance of neural networks, and I think the resulting combination will be of interest to people from both communities. The results strengthen our confidence in the accuracy of techniques like RegretNet, while also serving as a proof of concept for this type of approach that seems likely to lead to further fruitful exploration.
Weaknesses: Conceptually, I’m not entirely clear what being able to certify the exploitability at particular points buys us. The claim we can make is more rigorous, but it is still ultimately empirical. It would be nice to see some further analysis of how to translate the certificates into precise claims about the performance of the resulting mechanism. As discussed under additional feedback, the experimental evaluation, while adequate, is limited in several respects. A richer evaluation would strengthen the paper.
Correctness: All the results appear correct.
Clarity: The exposition, both conceptual and technical, is in general quite clear. There are a few points that could use further explanation or clarification, which are discussed as part of my additional feedback.
Relation to Prior Work: The coverage of prior work is good.
Additional Feedback: 49, 59, 74 – The discussion here talks a bit loosely about certifying the strategyproofness of auctions. However, this is not quite what gets certified, and this is an important caveat that should be made more explicit. In particular, what gets certified is the amount by which agents can manipulate on particular type profiles. This makes no guarantees about the incentives on any other type profile, so it is a bit misleading to describe the approach as certifying the extent of strategyproofness full stop. The abstract (11) is more careful and explicit about this. 193 – I’m confused why the IR penalty approach is discussed here. Based on the description of the 3 trained networks (225-230), none of them seem to use it. However, perhaps there is something I am missing because appendix A discusses the tuning of the IR Lagrange multiplier. 201 – Perhaps related to the previous, I found the explanation of the way distillation is used a bit confusing, and didn’t really understand it until it was used in the experiments on line 229. The key piece here which is currently left implicit is that (if I have understood correctly) the teacher RegretNet architecture enforces IR by construction, while the student network does not attempt to enforce IR at all but instead simply relies on being close to the teacher to approximately enforce it. 222 – It would be nice to see some non-uniform examples, particularly anywhere this approach has the ability to shed light on still-open theory questions. 259 – What does it mean to filter out IR-violating points? 265 – Why cut off your scalability analysis at such low values? Even the largest case seems like it only took a couple of days of compute, and there certainly seems to be more room to scale the number of agents with two items. Post response: Thank you for the response. I encourage you to incorporate the discussion from it into the paper.
Summary and Contributions: In this paper, the authors complement the recent approach of Duetting et al (ICML 2019) to compute revenue maximizing auctions for multi item settings where no theoretical approaches are known. In the initial paper, the authors are computing a regret (that they add in the Lagrangian objective function) corresponding to how the auction is incentive compatible / strategyproof This regret is computed through gradient descent There might exist a risk that the gradient descent is blocked into a local minima, such that the final auction is far from being incentive compatible (in terms of utility that strategic bidder can get by being strategic). The goal of this paper is to propose a way to compute the real value of the regret (by replacing the gradient descent by a Mixed Integer program) to increase the confidence in the correctness of the learned auction mechanism.
Strengths: Being able to certify the mechanisms learned through these new big architectures is an important problem. This paper proposes a solid compelling story to address this problem. The structure is clear and the paper well written. This paper initiates an interesting line of research on top the work of Duetting et al to compute optimal bounds on the lack of incentive compatibility. It’s also interesting to escape from a full gradient descent approach to see how other optimization pipelines can work on such new economics problem.
Weaknesses: To be able to write the optimization loss as a Mixed integer program, you have to replace the softmax (initially used by Duetting et al) by some Sparsemax. This change of architecture looks detrimental in terms of revenue since you do not recover the revenue of the initial paper of Duetting et al (that has been reproduced in some other papers). This is a problem since the ultimate goal would be to be able to obtain the optimal revenue with a certificate on the revenue. Do you have any ideas explaining this gap ?
Correctness: The empirical methodology looks fine and the authors provide their code in the submission.
Clarity: The structure is clear and the paper is well written.
Relation to Prior Work: Prior work is adequately covered.