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1. The menu-complexity of ``one-and-a-half'' dimensional mechanism design

   Saxena, Raghuvansh R.; Schvartzman, Ariel; Weinberg, S. Matthew (January 2018, Symposium on Discrete Algorithms)

   We study the menu complexity of optimal and approximately-optimal auctions in the context of the ``FedEx'' problem, a so-called ``one-and-a-half-dimensional'' setting where a single bidder has both a value and a deadline for receiving an item [FGKK 16]. The menu complexity of an auction is equal to the number of distinct (allocation, price) pairs that a bidder might receive [HN 13]. We show the following when the bidder has $$n$$ possible deadlines: 1) Exponential menu complexity is necessary to be exactly optimal: There exist instances where the optimal mechanism has menu complexity $$\geq 2^n-1$$. This matches exactly the upper bound provided by Fiat et al.'s algorithm, and resolves one of their open questions [FGKK 16]. 2) Fully polynomial menu complexity is necessary and sufficient for approximation: For all instances, there exists a mechanism guaranteeing a multiplicative $$(1-\epsilon)$$-approximation to the optimal revenue with menu complexity $$O(n^{3/2}\sqrt{\frac{\min\{n/\epsilon,\ln(v_{\max})\}}{\epsilon}}) = O(n^2/\epsilon)$$, where $$v_{\max}$$ denotes the largest value in the support of integral distributions. \item There exist instances where any mechanism guaranteeing a multiplicative $(1-O(1/n^2))$-approximation to the optimal revenue requires menu complexity $$\Omega(n^2)$$. Our main technique is the polygon approximation of concave functions [Rote 91], and our results here should be of independent interest. We further show how our techniques can be used to resolve an open question of [DW 17] on the menu complexity of optimal auctions for a budget-constrained buyer. 

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2. Optimal Deterministic Clock Auctions and Beyond

   Christodoulou, Giorgos; Gkatzelis, Vasilis; Schoepflin, Daniel (February 2022, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022))

   We design and analyze deterministic and randomized clock auctions for single-parameter domains with downward-closed feasibility constraints, aiming to maximize the social welfare. Clock auctions have been shown to satisfy a list of compelling incentive properties making them a very practical solution for real-world applications, partly because they require very little reasoning from the participating bidders. However, the first results regarding the worst-case performance of deterministic clock auctions from a welfare maximization perspective indicated that they face obstacles even for a seemingly very simple family of instances, leading to a logarithmic inapproximability result; this inapproximability result is information-theoretic and holds even if the auction has unbounded computational power. In this paper we propose a deterministic clock auction that achieves a logarithmic approximation for any downward-closed set system, using black box access to a solver for the underlying optimization problem. This proves that our clock auction is optimal and that the aforementioned family of instances exactly captures the information limitations of deterministic clock auctions. We then move beyond deterministic auctions and design randomized clock auctions that achieve improved approximation guarantees for a generalization of this family of instances, suggesting that the earlier indications regarding the performance of clock auctions may have been overly pessimistic. 

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3. Simple, Credible, and Approximately-Optimal Auctions

   Daskalakis, C; Fishelson, M; Lucier, B; Syrgkanis, V; Velusamy, S (January 2020, 21st ACM Conference on Economics and Computation, EC 2020)

   null (Ed.)

   We identify the first static credible mechanism for multi-item additive auctions that achieves a constant factor of the optimal revenue. This is one instance of a more general framework for designing two-part tariff auctions, adapting the duality framework of Cai et al [CDW16]. Given a (not necessarily incentive compatible) auction format A satisfying certain technical conditions, our framework augments the auction with a personalized entry fee for each bidder, which must be paid before the auction can be accessed. These entry fees depend only on the prior distribution of bidder types, and in particular are independent of realized bids. Our framework can be used with many common auction formats, such as simultaneous first-price, simultaneous second-price, and simultaneous all-pay auctions. If all-pay auctions are used, we prove that the resulting mechanism is credible in the sense that the auctioneer cannot benefit by deviating from the stated mechanism after observing agent bids. If second-price auctions are used, we obtain a truthful O(1)-approximate mechanism with fixed entry fees that are amenable to tuning via online learning techniques. Our results for first price and all-pay are the first revenue guarantees of non-truthful mechanisms in multi-dimensional environments; an open question in the literature [RST17]. 

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4. DIFFERENTIALLY PRIVATE MECHANISM DESIGN VIA QUANTILE ESTIMATION

   Yang, Yuanyuan; Kumar, Bhuvesh; Morgenstern, Jamie (February 2025, ICLR)

   We investigate the problem of designing differentially private (DP), revenue- maximizing single item auction. Specifically, we consider broadly applicable settings in mechanism design where agents’ valuation distributions are indepen- dent, non-identical, and can be either bounded or unbounded. Our goal is to design such auctions with pure, i.e., (ω, 0) privacy in polynomial time. 

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5. On the Cryptographic Hardness of Learning Single Periodic Neurons

   Min Jae Song; Ilias Zadik; Joan Bruna (December 2021, Advances in neural information processing systems)

   We show a simple reduction which demonstrates the cryptographic hardness of learning a single periodic neuron over isotropic Gaussian distributions in the pres- ence of noise. More precisely, our reduction shows that any polynomial-time algorithm (not necessarily gradient-based) for learning such functions under small noise implies a polynomial-time quantum algorithm for solving worst-case lattice problems, whose hardness form the foundation of lattice-based cryptography. Our core hard family of functions, which are well-approximated by one-layer neural networks, take the general form of a univariate periodic function applied to an affine projection of the data. These functions have appeared in previous seminal works which demonstrate their hardness against gradient-based (Shamir’18), and Statisti- cal Query (SQ) algorithms (Song et al.’17). We show that if (polynomially) small noise is added to the labels, the intractability of learning these functions applies to all polynomial-time algorithms, beyond gradient-based and SQ algorithms, under the aforementioned cryptographic assumptions. Moreover, we demonstrate the necessity of noise in the hardness result by designing a polynomial-time algorithm for learning certain families of such functions under exponentially small adversarial noise. Our proposed algorithm is not a gradient-based or an SQ algorithm, but is rather based on the celebrated Lenstra-Lenstra-Lovász (LLL) lattice basis reduction algorithm. Furthermore, in the absence of noise, this algorithm can be directly applied to solve CLWE detection (Bruna et al.’21) and phase retrieval with an optimal sample complexity of d + 1 samples. In the former case, this improves upon the quadratic-in-d sample complexity required in (Bruna et al.’21). 

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