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Yaonan Jin

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8 papers
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8

FOCS Conference 2025 Conference Paper

Beyond Regularity: Simple versus Optimal Mechanisms, Revisited

  • Yiding Feng 0001
  • Yaonan Jin

A large proportion of the Bayesian mechanism design literature is restricted to the family of regular distributions $\mathbb{F}_{\text {reg }}$ [Mye81] or the family of monotone hazard rate (MHR) distributions $\mathbb{F}_{M H R}$ [BMP63], which has overshadowed this rich and well-developed theory. We (re-)introduce two generalized families: quasi-regular distributions $\mathbb{F}_{Q-r e g}$ and quasi-MHR distributions $\mathbb{F}_{Q-M H R}$. Altogether, these four families form the following hierarchy: $\mathbb{F}_{\mathrm{MHR}} \subsetneq\left(\mathbb{F}_{\mathrm{reg}} \cap \mathbb{F}_{Q-\mathrm{MHR}}\right) \subsetneq \mathbb{F}_{\mathrm{reg}}, \mathbb{F}_{Q-\mathrm{MHR}} \subsetneq\left(\mathbb{F}_{\mathrm{reg}} \cup \mathbb{F}_{Q-\mathrm{MHR}}\right) \subsetneq \mathbb{F}_{Q-\mathrm{reg}}$ Likewise, the parameterized families of $\lambda$-regular (a. k. a. $\alpha$ strongly regular) distributions [CR14], [SS19], which smoothly interpolate $\mathbb{F}_{\text {reg }}$ and $\mathbb{F}_{\text {MHR }}$, generalize to $\lambda$-quasi-regular distributions. The significance of our new families is manifold. Firstly, their defining conditions are immediate “economic” relaxations of the original defining conditions (e. g. , regularity as monotonicity of the virtual value functions), capturing key economic intuitions. Secondly, they satisfy natural mathematical properties (about order statistics) failed for the original families, thus technically more tractable. Thirdly, numerous results (by [BK96], [HR09a], [CD15], [DRY15], [HR14], [AHN ${ }^{+}$19], [JLTX20], [JLQ ${ }^{+}$19b], [FLR19], [GHZ19b], [JLX23], [LM24] etc) known merely for the original families now can extend to our new families. Many of these extensions incur no quantitative loss, or even improve the state of the art for the original families. Finally, beyond the third point, our new families guide us to entirely new perspectives and thus entirely unknown results. For example, regarding revenue maximization for symmetric versus asymmetric regular buyers, we acquire $\frac{1}{2}$ - versus 0. 1908 -approximations for the (less-than-)one-sample prophet inequalities, respectively. To the best of our knowledge, such results are blank in the literature, despite their widely-studied welfare maximization counterparts [CDFS22], [RWW20], [CCES20], [CDF ${ }^{+}$21], [CCES24].

FOCS Conference 2024 Conference Paper

Benchmark-Tight Approximation Ratio of Simple Mechanism for a Unit-Demand Buyer

  • Yaonan Jin
  • Pinyan Lu

We study revenue maximization in the unit-demand single-buyer setting. Our main result is that Uniform-Ironed-Virtual-Value Item Pricing guarantees a tight 3-approximation to the Duality Relaxation Benchmark [Chawla-Malec-Sivan, EC’10/GEB’15; Cai-Devanur-Weinberg, STOC’16/ SICOMP’21], breaking the barrier of 4 since [Chawla-Hartline-Malec-Sivan, STOC’10; Chawla-Malec-Sivan, EC’10/GEB’15]. To our knowledge, this is the first benchmark-tight revenue guarantee of any simple multi-item mechanism. Technically, all previous works employ Myerson Auction as an intermediary. The barrier of 4 follows as Uniform-Ironed-Virtual-Value Item Pricing achieves a tight 2-approximation to Myerson Auction, which then achieves a tight 2-approximation to Duality Relaxation Benchmark. Instead, our new approach avoids Myerson Auction, thus enabling the improvement. Central to our work are a benchmark-based 3-competitive prophet inequality and its fully constructive proof. Such variant prophet inequalities shall find future applications, e. g. , to Multi-Item Mechanism Design where optimal revenues are relaxed to various more accessible benchmarks. We complement our benchmark-tight ratio with an impossibility result. All previous works and ours follow the single-dimensional representative approach introduced by [Chawla-Hartline-Kleinberg, EC'07]. Against Duality Relaxation Benchmark, it turns out that this approach cannot beat our bound of 3 for a large class of Item Pricing's.

SODA Conference 2023 Conference Paper

Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity

  • Yaonan Jin
  • Daogao Liu
  • Zhao Song 0002

The ability to resolve detail in the object that is being imaged, named by resolution, is the core parameter of an imaging system. Super-resolution is a class of techniques that can enhance the resolution of an imaging system and even transcend the diffraction limit of systems. Despite huge success in the application, super-resolution is not well understood on the theoretical side, especially for any dimension d ≥ 2. In particular, in order to recover a k -sparse signal, all previous results suffer from either/both poly(k) samples or running time. We design robust algorithms for any (constant) dimension under a strong noise model based on developing some new techniques in Sparse Fourier transform (Sparse FT), such as inverting a robust linear system, “eggshell” sampling schemes, and partition and voting methods in high dimension. These algorithms are the first to achieve running time and sample complexity (nearly) linear in the number of source points and logarithmic in bandwidth for any constant dimension, and we believe the techniques developed in the work can find their further applications on the Super-resolution and Sparse FT problem. * The full version of the paper can be accessed at https: //arxiv. org/abs/2005. 06156

SODA Conference 2023 Conference Paper

The Price of Stability for First Price Auction

  • Yaonan Jin
  • Pinyan Lu

This paper establishes the Price of Stability (PoS) for First Price Auctions, for all equilibrium concepts that have been studied in the literature: Bayesian Nash Equilibrium ⊊ Bayesian Correlated Equilibrium ⊊ Bayesian Coarse Correlated Equilibrium. • Bayesian Nash Equilibrium: For independent valuations, the tight PoS is 1 − 1/ e 2 ≈ 0. 8647, matching the counterpart Price of Anarchy (PoA) bound [JL22]. For correlated valuations, the tight PoS is 1 − 1/ e ≈ 0. 6321, matching the counterpart PoA bound [ST13, Syr14]. This result indicates that, in the worst cases, efficiency degradation depends not on different selections among Bayesian Nash Equilibria. • Bayesian (Coarse) Correlated Equilibrium: For independent or correlated valuations, the tight PoS is always 1 = 100%, i. e. , no efficiency degradation. This result indicates that First Price Auctions can be fully efficient when we allow the more general equilibrium concepts. * The full version of the paper can be accessed at https: //arxiv. org/abs/2207. 04455

SODA Conference 2022 Conference Paper

Average-Case Subset Balancing Problems

  • Xi Chen 0001
  • Yaonan Jin
  • Tim Randolph 0001
  • Rocco A. Servedio

Given a set of n input integers, the Equal Subset Sum problem asks us to find two distinct subsets with the same sum. In this paper we present an algorithm that runs in time O ∗(3 0. 387 n ) in the average case, significantly improving over the O ∗(3 0. 488 n ) running time of the best known worst-case algorithm [MNPW19] and the Meet-in-the-Middle benchmark of O ∗(3 0. 5 n ). Our algorithm generalizes to a number of related problems, such as the “Generalized Equal Subset Sum” problem, which asks us to assign a coefficient c i from a set C to each input number x i such that Σ i c i x i = 0. Our algorithm for the average-case version of this problem runs in time for some positive constant c 0, whenever C = {0, ± 1, …, ± d} or {±1, …, ± d } for some positive integer d (with runtime O ∗( |C| 0. 45 n ) when |C| < 10). Our results extend to the problem of finding “nearly balanced” solutions in which the target is a not-too-large nonzero offset τ. Our approach relies on new structural results that characterize the probability that Σ i c i x i = τ has a solution c ∊ C n when x i 's are chosen randomly; these results may be of independent interest. Our algorithm is inspired by the “representation technique” introduced by Howgrave-Graham and Joux [HGJ10]. This requires several new ideas to overcome preprocessing hurdles that arise in the representation framework, as well as a novel application of dynamic programming in the solution recovery phase of the algorithm.

STOC Conference 2019 Conference Paper

Tight approximation ratio of anonymous pricing

  • Yaonan Jin
  • Pinyan Lu
  • Qi Qi 0003
  • Zhihao Gavin Tang
  • Tao Xiao

This paper considers two canonical Bayesian mechanism design settings. In the single-item setting, the tight approximation ratio of Anonymous Pricing is obtained: (1) compared to Myerson Auction, Anonymous Pricing always generates at least a 1/2.62-fraction of the revenue; (2) there is a matching lower-bound instance. In the unit-demand single-buyer setting, the tight approximation ratio between the simplest deterministic mechanism and the optimal deterministic mechanism is attained: in terms of revenue, (1) Uniform Pricing admits a 2.62-approximation to Item Pricing; (2) a matching lower-bound instance is presented also. These results answer two open questions asked by Alaei et al. (FOCS’15) and Cai and Daskalakis (GEB’15). As an implication, in the single-item setting: the approximation ratio of Second-Price Auction with Anonymous Reserve (Hartline and Roughgarden EC’09) is improved to 2.62, which breaks the best known upper bound of e ≈ 2.72.

SODA Conference 2019 Conference Paper

Tight Revenue Gaps among Simple Mechanisms

  • Yaonan Jin
  • Pinyan Lu
  • Zhihao Gavin Tang
  • Tao Xiao

We consider a fundamental problem in microeconomics: Selling a single item among a number of buyers whose values are drawn from known independent and regular distributions. There are four widely-used and widely-studied mechanisms in this literature: Anonymous Posted-Pricing (AP), Second-Price Auction with Anonymous Reserve (AR), Sequential Posted-Pricing (SPM), and Myerson Auction (OPT). Myerson Auction is optimal but complicated, which also suffers a few issues in practice such as fairness; AP is the simplest mechanism, but its revenue is also the lowest among these four; AR and SPM are of intermediate complexity and revenue. We study the revenue gaps among these four mechanisms, which is defined as the largest ratio between revenues from two mechanisms. We establish two tight ratios and one tighter bound: 1. SPM/AP. This ratio studies the power of discrimination in pricing schemes. We obtain the tight ratio of roughly 2. 62, closing the previous known bounds [ e /( e – 1), e ]. 2. AR/AP. This ratio studies the relative power of auction vs. pricing schemes, when no discrimination is allowed. We get the tight ratio of π 2 /6 ≈ 1. 64, closing the previous known bounds [ e /( e – 1), e ]. 3. OPT/AR. This ratio studies the power of discrimination in auctions. Previously, the revenue gap is known to be in interval [2, e ], and the lower-bound of 2 is conjectured to be tight [38, 37, 4]. We disprove this conjecture by obtaining a better lower-bound of 2. 15.

v2026.09.13