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José Correa 0001

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers
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10

STOC Conference 2023 Conference Paper

A Constant Factor Prophet Inequality for Online Combinatorial Auctions

  • José Correa 0001
  • Andrés Cristi

In online combinatorial auctions m indivisible items are to be allocated to n agents who arrive online. Agents have random valuations for the different subsets of items and the goal is to allocate the items on the fly so as to maximize the total value of the assignment. A prophet inequality in this setting refers to the existence of an online algorithm guaranteed to obtain, in expectation, a certain fraction of the expected value obtained by an optimal solution in hindsight. The study of prophet inequalities for online combinatorial auctions has been an intensive area of research in recent years, and constant factor prophet inequalities are known when the agents’ valuation functions are submodular or fractionally subadditive. Despite many efforts, for the more general case of subadditive valuations, the best known prophet inequality has an approximation guarantee of O (loglog m ). In this paper, we prove the existence of a constant factor prophet inequality for the subadditive case, resolving a central open problem in the area.

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ICML Conference 2021 Conference Paper

Fairness and Bias in Online Selection

  • José Correa 0001
  • Andrés Cristi
  • Paul Dütting
  • Ashkan Norouzi-Fard

There is growing awareness and concern about fairness in machine learning and algorithm design. This is particularly true in online selection problems where decisions are often biased, for example, when assessing credit risks or hiring staff. We address the issues of fairness and bias in online selection by introducing multi-color versions of the classic secretary and prophet problem. Interestingly, existing algorithms for these problems are either very unfair or very inefficient, so we develop optimal fair algorithms for these new problems and provide tight bounds on their competitiveness. We validate our theoretical findings on real-world data.

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SODA Conference 2021 Conference Paper

The Secretary Problem with Independent Sampling

  • José Correa 0001
  • Andrés Cristi
  • Laurent Feuilloley
  • Tim Oosterwijk
  • Alexandros Tsigonias-Dimitriadis

In the secretary problem we are faced with an online sequence of elements with values. Upon seeing an element we have to make an irrevocable take-it-or-leave-it decision. The goal is to maximize the probability of picking the element of maximum value. The most classic version of the problem is that in which the elements arrive in random order and their values are arbitrary. Here, the optimal algorithm picks the maximum value with probability at least 1/ e. However, by varying the available information, new interesting problems arise. For instance, in the full information variant of the secretary problem the values are i. i. d. samples from a known distribution. Naturally, the best possible success probability increases and turns out to be approximately 0. 58. Also, the case in which the arrival order is adversarial instead of random leads to interesting variants that have been considered in the literature. In this paper we study both the random order and adversarial order secretary problems with an additional twist. The values are arbitrary, but before starting the online sequence we independently sample each element with a fixed probability p. The sampled elements become our information or history set and the game is played over the remaining elements. We call these problems the random order secretary problem with p -sampling (ROS p for short) and the adversarial order secretary problem with p -sampling (AOS p for short). Our main result is to obtain best possible algorithms for both problems and all values of p. As p grows to 1 the obtained guarantees converge to the optimal guarantees in the full information case. In the adversarial order setting, the best possible algorithm turns out to be a simple fixed threshold algorithm in which the optimal threshold is a function of p only. Therefore, even knowledge of the total number of elements is unnecessary. Proving that this algorithm is optimal involves a novel technique, which boils down to analyzing a related game in a conflict graph over binary sequences. In the random order setting we prove that the best possible algorithm is characterized by a fixed sequence of time thresholds, dictating at which point in time we should start accepting a value that is both a maximum of the online sequence and has a given ranking within the sampled elements. Surprisingly, this sequence of time thresholds arises from a separable and convex optimization problem whose solution is independent of p.

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SODA Conference 2020 Conference Paper

The Two-Sided Game of Googol and Sample-Based Prophet Inequalities

  • José Correa 0001
  • Andrés Cristi
  • Boris Epstein 0001
  • José A. Soto

The secretary problem or the game of Googol are classic models for online selection problems that have received significant attention in the last five decades. In this paper we consider a variant of the problem and explore its connections to data-driven online selection. Specifically, we are given n cards with arbitrary nonnegative numbers written on both sides. The cards are randomly placed on n consecutive positions on a table, and for each card, the visible side is also selected at random. The player sees the visible side of all cards and wants to select the card with the maximum hidden value. To this end, the player flips the first card, sees its hidden value and decides whether to pick it or drop it and continue with the next card. We study algorithms for two natural objectives. In the first one, similar to the secretary problem, the player wants to maximize the probability of selecting the maximum hidden value. We show that this can be done with probability at least 0. 45292. In the second objective, similar to the prophet inequality, the player wants to maximize the expectation of the selected hidden value. Here we show a guarantee of at least 0. 63518 with respect to the expected maximum hidden value. Our algorithms result from combining three basic strategies. One is to stop whenever we see a value larger than the initial n visible numbers. The second one is to stop the first time the last flipped card's value is the largest of the currently n visible numbers in the table. And the third one is similar to the latter but to stop it additionally requires that the last flipped value is larger than the value on the other side of its card. We apply our results to the prophet secretary problem with unknown distributions, but with access to a single sample from each distribution. In particular, our guarantee improves upon 1 – 1/ e for this problem, which is the currently best known guarantee and only works for the i. i. d. prophet inequality with samples.

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SODA Conference 2019 Conference Paper

Prophet Secretary Through Blind Strategies

  • José Correa 0001
  • Raimundo Saona
  • Bruno Ziliotto

In the classic prophet inequality, a problem in optimal stopping theory, samples from independent random variables (possibly differently distributed) arrive online. A gambler that knows the distributions, but cannot see the future, must decide at each point in time whether to stop and pick the current sample or to continue and lose that sample forever. The goal of the gambler is to maximize the expected value of what she picks and the performance measure is the worst case ratio between the expected value the gambler gets and what a prophet, that sees all the realizations in advance, gets. In the late seventies, Krengel and Sucheston, and Garling [16], established that this worst case ratio is a constant and that 1/2 is the best possible such constant. In the last decade the theory of prophet inequalities has resurged as an important problem due to its connections to posted price mechanisms, frequently used in online sales. A particularly interesting variant is the so-called Prophet Secretary problem, in which the only difference is that the samples arrive in a uniformly random order. For this variant several algorithms are known to achieve a constant of 1 – 1/ e and very recently this barrier was slightly improved by Azar et al. [3]. In this paper we derive a way of analyzing multithreshold strategies that basically sets a nonincreasing sequence of thresholds to be applied at different times. The gambler will thus stop the first time a sample surpasses the corresponding threshold. Specifically we consider a class of very robust strategies that we call blind quantile strategies. These constitute a clever generalization of single threshold strategies and consist in fixing a function which is used to define a sequence of thresholds once the instance is revealed. Our main result shows that these strategies can achieve a constant of 0. 669 in the Prophet Secretary problem, improving upon the best known result of Azar et al. [3], and even that of Beyhaghi et al. [4] that works in the case the gambler can select the order of the samples. The crux of the analysis is a very precise analysis of the underlying stopping time distribution for the gambler's strategy that is inspired by the theory of Schur convex functions. We further prove that our family of blind strategies cannot lead to a constant better than 0. 675. Finally we prove that no nonadaptive algorithm for the gambler can achieve a constant better than 0. 732, which also improves upon a recent result of Azar et al. [3]. Here, a nonadaptive algorithm is an algorithm whose decision to stop can depend on the index of the random variable being sampled, on the value sampled, and on the time, but not on the history that has been observed.

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STOC Conference 2011 Conference Paper

Inner product spaces for MinSum coordination mechanisms

  • Richard Cole 0001
  • José Correa 0001
  • Vasilis Gkatzelis
  • Vahab Mirrokni
  • Neil Olver

We study coordination mechanisms aiming to minimize the weighted sum of completion times of jobs in the context of selfish scheduling problems. Our goal is to design local policies that achieve a good price of anarchy in the resulting equilibria for unrelated machine scheduling. To obtain these approximation bounds, we introduce a new technique that while conceptually simple, seems to be quite powerful. The method entails mapping strategy vectors into a carefully chosen inner product space; costs are shown to correspond to the norm in this space, and the Nash condition also has a simple description. With this structure in place, we are able to prove a number of results, as follows. First, we consider Smith's Rule, which orders the jobs on a machine in ascending processing time to weight ratio, and show that it achieves an approximation ratio of 4. We also demonstrate that this is the best possible for deterministic non-preemptive strongly local policies. Since Smith's Rule is always optimal for a given fixed assignment, this may seem unsurprising, but we then show that better approximation ratios can be obtained if either preemption or randomization is allowed. We prove that ProportionalSharing, a preemptive strongly local policy, achieves an approximation ratio of 2.618 for the weighted sum of completion times, and an approximation ratio of 2.5 in the unweighted case. We also observe that these bounds are tight. Next, we consider Rand, a natural non-preemptive but randomized policy. We show that it achieves an approximation ratio of at most 2.13; moreover, if the sum of the weighted completion times is negligible compared to the cost of the optimal solution, this improves to π/2. Finally, we show that both ProportionalSharing and Rand induce potential games, and thus always have a pure Nash equilibrium (unlike Smith's Rule). This allows us to design the first combinatorial constant-factor approximation algorithm minimizing weighted completion time for unrelated machine scheduling. It achieves a factor of 2+ε for any ε > 0, and involves imitating best response dynamics using a variant of ProportionalSharing as the policy.

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STOC Conference 2004 Conference Paper

An approximate König's theorem for edge-coloring weighted bipartite graphs

  • José Correa 0001
  • Michel X. Goemans

We consider a generalization of edge coloring bipartite graphs in which every edge has a weight in [0,1] and the coloring of the edges must satisfy that the sum of the weights of the edges incident to a vertex v of any color must be at most 1. For unit weights, König's theorem says that the number of colors needed is exactly the maximum degree. For this generalization, we show that 2. 557 n + o ( n ) colors are sufficient where n is the maximum total weight adjacent to any vertex, improving the previously best bound of 2. 833 n + O (1) due to Du et al. This question is motivated by the question of the rearrangeability of 3-stage Clos networks. In that context, the corresponding parameter n of interest in the edge coloring problem is the maximum over all vertices of the number of unit-sized bins needed to pack the weights of the incident edges. In that setting, we are able to improve the bound to 2. 5480 n + o ( n ), also improving a bound of 2. 5625 n + O (1) of Du et al. Our analysis is interesting in its own and involves a novel decomposition result for bipartite graphs and the introduction of an associated continuous one-dimensional bin packing instance which we can prove allows perfect packing.

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