arXiv subjects

Neil Newman

Publications and source records attributed to Neil Newman.

6 recordsLinked to original sources

arXiv2024

Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning

Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to analyze, making it difficult for bidders to determine how to behave and for designers to optimize auction rules to ensure desirable outcomes such as high revenue or welfare. In this paper, we investigate whether multi-agent reinforcement learning (MARL) algorithms can be used to understand iterative combinatorial auctions, given that these algorithms have recently shown empirical success in several other domains. We find that MARL can indeed benefit auction analysis, but that deploying it effectively is nontrivial. We begin by describing modelling decisions that keep the resulting game tractable without sacrificing important features such as imperfect information or asymmetry between bidders. We also discuss how to navigate pitfalls of various MARL algorithms, how to overcome challenges in verifying convergence, and how to generate and interpret multiple equilibria. We illustrate the promise of our resulting approach by using it to evaluate a specific rule change to a clock auction, finding substantially different auction outcomes due to complex changes in bidders' behavior.

cs.GT↗

arXiv2022

Matching Papers and Reviewers at Large Conferences

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper studies a novel reviewer-paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022, and IJCAI 2022. This approach has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer-paper scores; (2) formulating and solving an optimization problem to find good reviewer-paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data -- including a comparison with the matching algorithm used in AAAI's previous (2020) iteration -- and supplements this with additional numerical experimentation.

cs.AI↗

arXiv2020

Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates

We study a dynamic non-bipartite matching problem. There is a fixed set of agent types, and agents of a given type arrive and depart according to type-specific Poisson processes. Agent departures are not announced in advance. The value of a match is determined by the types of the matched agents. We present an online algorithm that is (1/8)-competitive with respect to the value of the optimal-in-hindsight policy, for arbitrary weighted graphs. Our algorithm treats agents heterogeneously, interpolating between immediate and delayed matching in order to thicken the market while still matching valuable agents opportunistically.

cs.GT↗

arXiv2017

Assessing Economic Outcomes in Simulated Reverse Clock Auctions for Radio Spectrum

We investigate the economic outcomes that result under simulated bidder behavior in a model of the FCC's reverse auction for radio spectrum. In our simulations, limiting our notion of efficiency to the reverse auction in isolation, the reverse clock auction achieves very efficient solutions, the FCC's scoring rule greatly reduces the total payments to TV broadcasters at the cost of some efficiency, and using a poor feasibility checker can have grave consequences both in terms of the auction's cost and efficiency.

cs.GT↗

arXiv2017

Deep Optimization for Spectrum Repacking

Over 13 months in 2016-17 the FCC conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 UHF channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 billion (used to pay down the national debt) after covering costs. A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote. This paper describes the process by which we built SATFC. We adopted an approach we dub "deep optimization", taking a data-driven, highly parametric, and computationally intensive approach to solver design. More specifically, to build SATFC we designed software that could pair both complete and local-search SAT-encoded feasibility checking with a wide range of domain-specific techniques. We then used automatic algorithm configuration techniques to construct a portfolio of eight complementary algorithms to be run in parallel, aiming to achieve good performance on instances that arose in proprietary auction simulations. To evaluate the impact of our solver in this paper, we built an open-source reverse auction simulator. We found that within the short time budget required in practice, SATFC solved more than 95% of the problems it encountered. Furthermore, the incentive auction paired with SATFC produced nearly optimal allocations in a restricted setting and substantially outperformed other alternatives at national scale.

cs.AI↗

arXiv2017

MOCVD growth of N-polar GaN on on-axis sapphire substrate: impact of AlN nucleation layer on GaN surface hillock density

We report on the impact of growth conditions on surface hillock density of N-polar GaN grown on nominally on-axis (0001) sapphire substrate by metal organic chemical vapor deposition (MOCVD). Large reduction in hillock density was achieved by implementation of an optimized high temperature AlN nucleation layer and use of indium surfactant in GaN overgrowth. A reduction by more than a factor of five in hillock density from 1000 to 170 hillocks/cm$^{-2}$ was achieved as a result. Crystal quality and surface morphology of the resultant GaN films were characterized by high resolution x-ray diffraction and atomic force microscopy and found to be relatively unaffected by the buffer conditions. It is also shown that the density of smaller surface features is unaffected by AlN buffer conditions.

cond-mat.mtrl-sci↗