Kati8/ICLR_2023_2025 huggingface dataset:
authors: Amy Greenwald
authors: Sadie Zhao
authors: Alec Koppel
authors: Sumitra Ganesh
reviews: 
Comments: Game theory provides a structured approach to predicting outcomes of interactions between rational agents. Inverse game theory deals with situations where the players' behavioral models are unknown and aims to deduce the payoff functions that explain observed actions as equilibria. This paper presents a new approach for solving inverse equilibrium problems in a range of games, using generative adversarial optimization to match game-theoretic models to observed data and make predictions, as exemplified by modeling the Spanish electricity market.
Strengths: The authors study an interesting and challenging problem of inverse MARL. The theoretical results are nice; they are simple yet relevant and impactful. 
The experiments on an electricity market are well thought and designed.
Weaknesses: The readability can be improved. I think Section 3 never mentions that it is for the one-shot game setting. 
There is no methodological contributions. All presented algorithms are simple extension of known algorithms. (On the other hand we should not invent/propose algorithms just for the sake of proposing them).
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 4 excellent
Clarity: 3 good
reviews: 
Comments: This paper formalizes the problem of inverse game-theory (determining
equilibrium strategies and associated game structure from historical play) as a
novel min-max optimization problem and solves it using a primal-dual gradient
method. This technique is deployed on a practical and interesting application
domain of market pricing dynamics.
Strengths: The simple formulation of the set of inverse Nash equilibria (NE) as a min-max
game is elegant and appears to be original. If it is indeed original, for this
alone, the paper merits publication and should be highlighted.

The paper overall is well written and showcases immediate applications of the
proposed solution to an important and practical domain as a proof-of-concept. I
believe these results are significant and will be impactful.
Weaknesses: As an easily rectified issue, Figure 1 could have been better represented by
plotting residuals over time or, by subsampling the data, plotting mean
residuals with error bars.

As a minor complaint, I do not prefer the language of "generative-adversarial"
(especially not in terms of a "discriminator"), even if this is the closest
analogy familiar to machine learning practitioners: This is a standard min-max
optimization problem that need not be wed to the ML setting.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 4 excellent
Clarity: 3 good
reviews: 
Comments: This paper studied inverse game theory to find parameters of the payoff functions of the game. Polynomial time and sample efficient algorithms are provided and claimed.
Strengths: 1. This paper formulate the inverse game as an generative-adversarial optimization problem and provide polynomial time algorithms.
Weaknesses: 1. The proofs are not completed, e.g., I cannot find the proofs for Theorem 4.1 and Theorem 5.2.

2. The presentation can be further improved, e.g., more intuitions about the assumptions and theorems.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 1 poor