More Like this

1. A Classification Method for Ranking and Selection with Covariates

   https://doi.org/10.1109/WSC57314.2022.10015235  

   Keslin, Gregory; Nelson, Barry L.; Plumlee, Matthew; Pagnoncelli, Bernardo K.; Rahimian, Hamed (December 2022, Proceedings of the 2022 Winter Simulation Conference)

   Feng, B.; Pedrielli, G; Peng, Y.; Shashaani, S.; Song, E.; Corlu, C.; Lee, L.; Chew, E.; Roeder, T.; Lendermann, P. (Ed.)

   Ranking & selection (R&S) procedures are simulation-optimization algorithms for making one-time decisions among a finite set of alternative system designs or feasible solutions with a statistical assurance of a good selection. R&S with covariates (R&S+C) extends the paradigm to allow the optimal selection to depend on contextual information that is obtained just prior to the need for a decision. The dominant approach for solving such problems is to employ offline simulation to create metamodels that predict the performance of each system or feasible solution as a function of the covariate. This paper introduces a fundamentally different approach that solves individual R&S problems offline for various values of the covariate, and then treats the real-time decision as a classification problem: given the covariate information, which system is a good solution? Our approach exploits the availability of efficient R&S procedures, requires milder assumptions than the metamodeling paradigm to provide strong guarantees, and can be more efficient. 

   more » « less
2. Fair Division of Indivisible Goods for a Class of Concave Valuations

   https://doi.org/10.1613/jair.1.12911  

   Chaudhury, Bhaskar Ray; Cheung, Yun Kuen; Garg, Jugal; Garg, Naveen; Hoefer, Martin; Mehlhorn, Kurt (May 2022, Journal of Artificial Intelligence Research)

   We study the fair and efficient allocation of a set of indivisible goods among agents, where each good has several copies, and each agent has an additively separable concave valuation function with a threshold. These valuations capture the property of diminishing marginal returns, and they are more general than the well-studied case of additive valuations. We present a polynomial-time algorithm that approximates the optimal Nash social welfare (NSW) up to a factor of e1/e ≈ 1.445. This matches with the state-of-the-art approximation factor for additive valuations. The computed allocation also satisfies the popular fairness guarantee of envy-freeness up to one good (EF1) up to a factor of 2 + ε. For instances without thresholds, it is also approximately Pareto-optimal. For instances satisfying a large market property, we show an improved approximation factor. Lastly, we show that the upper bounds on the optimal NSW introduced in Cole and Gkatzelis (2018) and Barman et al. (2018) have the same value. 

   more » « less
3. EVALUATION OF bi-PASS FOR PARALLEL SIMULATION OPTIMIZATION

   Pei, L; Nelson, B L; Hunter, S R (December 2020, Proceedings of the Winter Simulation Conference)

   Bae, K-H; Feng, B; Kim, S; Lazarova-Molnar, S; Zheng, Z; Roeder, T; Thiesing, R (Ed.)

   Cheap parallel computing has greatly extended the reach of ranking & selection (R&S) for simulation optimization. In this paper we present an evaluation of bi-PASS, a R&S procedure created specifically for parallel implementation and very large numbers of system designs. We compare bi-PASS to the state-ofthe- art Good Selection Procedure and an easy-to-implement subset selection procedure. This is one of the few papers to consider both computational and statistical comparison of parallel R&S procedures. 

   more » « less
4. Stratified Random Sampling over Streaming and Stored Data

   https://doi.org/10.5441/002/edbt.2019.04  

   Nguyen, T; Shih, M; Srivastava, D; Tirthapura, S; Xu, B (March 2019, Advances in Database Technology - 22nd International Conference on Extending Database Technology (EDBT))

   Stratified random sampling (SRS) is a widely used sampling technique for approximate query processing. We consider SRS on continuously arriving data streams, and make the following contributions. We present a lower bound that shows that any streaming algorithm for SRS must have (in the worst case) a variance that is Ω(r) factor away from the optimal, where r is the number of strata. We present S-VOILA, a streaming algorithm for SRS that is locally variance-optimal. Results from experiments on real and synthetic data show that S-VOILA results in a variance that is typically close to an optimal offline algorithm, which was given the entire input beforehand. We also present a variance-optimal offline algorithm VOILA for stratified random sampling. VOILA is a strict generalization of the well-known Neyman allocation, which is optimal only under the assumption that each stratum is abundant, i.e. has a large number of data points to choose from. Experiments show that VOILA can have significantly smaller variance (1.4x to 50x) than Neyman allocation on real-world data. 

   more » « less
5. Getting to "rate optimal" in ranking & selection

   Avci, Harun; Nelson, Barry L; Waechter, A (December 2021, Proceedings of the 2021 Winter Simulation Conference)

   In their 2004 seminal paper, Glynn and Juneja formally and precisely established the rate-optimal, probability of incorrect-selection, replication allocation scheme for selecting the best of k simulated systems. In the case of independent, normally distributed outputs this allocation has a simple form that depends in an intuitively appealing way on the true means and variances. Of course the means and (typically) variances are unknown, but the rate-optimal allocation provides a target for implementable, dynamic, data-driven policies to achieve. In this paper we compare the empirical behavior of four related replication-allocation policies: mCEI from Chen and Rzyhov and our new gCEI policy that both converge to the Glynn and Juneja allocation; AOMAP from Peng and Fu that converges to the OCBA optimal allocation; and TTTS from Russo that targets the rate of convergence of the posterior probability of incorrect selection. We find that these policies have distinctly different behavior in some settings. 

   more » « less