Google TechTalks | Limitations of Stochastic Selection with Pairwise Independent Priors @GoogleTechTalks | Uploaded April 2024 | Updated October 2024, 1 week ago.
A Google TechTalk, presented by Neel Patel, 2024-04-02
Google Algorithms Seminar. ABSTRACT: Motivated by the growing interest in correlation-robust stochastic optimization, we investigate stochastic selection problems beyond independence. Specifically, we consider the instructive case of pairwise-independent priors and matroid constraints. We obtain essentially-optimal bounds for contention resolution and prophet inequalities. The impetus for our work comes from the recent work of Caragiannis et al., who derived a constant-approximation for the single-choice prophet inequality with pairwise-independent priors.
For general matroids, our results are tight and largely negative. For both contention resolution and prophet inequalities, our impossibility results hold for the full linear matroid over a finite field. We explicitly construct pairwise-independent distributions which rule out an omega(1/Rank)-balanced offline CRS and an omega(1/log Rank)-competitive prophet inequality against the (usual) oblivious adversary. For both results, we employ a generic approach for constructing pairwise-independent random vectors -- one which unifies and generalizes existing pairwise-independence constructions from the literature on universal hash functions and pseudorandomness. Specifically, our approach is based on our observation that random linear maps turn linear independence into stochastic independence.
We then examine the class of matroids which satisfy the so-called partition property -- these include most common matroids encountered in optimization. We obtain positive results for both online contention resolution and prophet inequalities with pairwise-independent priors on such matroids, approximately matching the corresponding guarantees for fully independent priors. These algorithmic results hold against the almighty adversary for both problems.
ABOUT THE SPEAKER: I am Neel, a third-year CS PhD student in the USC Theory Group, where I am being advised by Shaddin Dughmi and David Kempe. I am broadly interested in Combinatorial Optimization in the presence of Uncertainty or Incentives, Algorithmic Contract Theory and Computational Economics. Recently, I have been also working on problems in designing (fast) Dynamic Algorithms for Graph Problems. I have also worked on designing AI/ML algorithms with the consideration of ethical aspects.
Before starting my Ph.D., I spent a wonderful one and a half years at the National University of Singapore as a Research Assistant working with Yair Zick and Reza Shokri. Before that, I completed my B.Stat at Indian Statistical Institute, Kolkata.
A Google TechTalk, presented by Neel Patel, 2024-04-02
Google Algorithms Seminar. ABSTRACT: Motivated by the growing interest in correlation-robust stochastic optimization, we investigate stochastic selection problems beyond independence. Specifically, we consider the instructive case of pairwise-independent priors and matroid constraints. We obtain essentially-optimal bounds for contention resolution and prophet inequalities. The impetus for our work comes from the recent work of Caragiannis et al., who derived a constant-approximation for the single-choice prophet inequality with pairwise-independent priors.
For general matroids, our results are tight and largely negative. For both contention resolution and prophet inequalities, our impossibility results hold for the full linear matroid over a finite field. We explicitly construct pairwise-independent distributions which rule out an omega(1/Rank)-balanced offline CRS and an omega(1/log Rank)-competitive prophet inequality against the (usual) oblivious adversary. For both results, we employ a generic approach for constructing pairwise-independent random vectors -- one which unifies and generalizes existing pairwise-independence constructions from the literature on universal hash functions and pseudorandomness. Specifically, our approach is based on our observation that random linear maps turn linear independence into stochastic independence.
We then examine the class of matroids which satisfy the so-called partition property -- these include most common matroids encountered in optimization. We obtain positive results for both online contention resolution and prophet inequalities with pairwise-independent priors on such matroids, approximately matching the corresponding guarantees for fully independent priors. These algorithmic results hold against the almighty adversary for both problems.
ABOUT THE SPEAKER: I am Neel, a third-year CS PhD student in the USC Theory Group, where I am being advised by Shaddin Dughmi and David Kempe. I am broadly interested in Combinatorial Optimization in the presence of Uncertainty or Incentives, Algorithmic Contract Theory and Computational Economics. Recently, I have been also working on problems in designing (fast) Dynamic Algorithms for Graph Problems. I have also worked on designing AI/ML algorithms with the consideration of ethical aspects.
Before starting my Ph.D., I spent a wonderful one and a half years at the National University of Singapore as a Research Assistant working with Yair Zick and Reza Shokri. Before that, I completed my B.Stat at Indian Statistical Institute, Kolkata.
