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2000, Proceedings of the thirty-second annual ACM symposium on Theory of computing - STOC '00
We consider a class of problems in which an algorithm seeks to compute a function f over a set of n inputs, where each input has an associated price. The algorithm queries inputs sequentially, trying to learn the value of the function for the minimum cost. We apply the competitive analysis of algorithms to this framework, designing algorithms that incur large cost only when the cost of the cheapest "proof" for the value of f is also large. We provide algorithms that achieve the optimal competitive ratio for functions that include arbitrary Boolean AND/OR trees, and for the problem of searching in a sorted array. We also investigate a model for pricing in this framework, constructing a set of prices for any AND/OR tree that satisfies a very strong type of equilibrium property.
Journal of Computer and System Sciences, 2002
We consider a class of problems in which an algorithm seeks to compute a function over a set of inputs, where each input has an associated price. The algorithm queries inputs sequentially, trying to learn the value of the function for the minimum cost. We apply the competitive analysis of algorithms to this framework, designing algorithms that incur large cost only when the cost of the cheapest "proof" for the value of is also large. We provide algorithms that achieve the optimal competitive ratio for functions that include arbitrary Boolean AND/OR trees, and for the problem of searching in a sorted array. We also investigate a model for pricing in this framework, constructing a set of prices for any AND/OR tree that satisfies a very strong type of equilibrium property. § ¨ §
2001
We consider a class of problems in which an algorithm seeks to compute a function f over a set of n inputs, where each input has an associated price. The algorithm queries inputs sequentially, trying to learn the value of the function for the minimum cost. We apply the competitive analysis of algorithms to this framework, designing algorithms that incur
2003
Query optimization that involves expensive predicates have received considerable attention in the database community. Typically, the output to a database query is a set of tuples that satisfy certain conditions, and, with expensive predicates, these conditions may be computationally costly to verify. In the simplest case, when the query looks for the set of tuples that simultaneously satisfy k expensive predicates, the problem reduces to ordering the evaluation of the predicates so as to minimize the time to output the set of tuples comprising the answer to the query. Here, we give a simple and fast deterministic k-approximation algorithm for this problem, and prove that k is the best possible approximation ratio for a deterministic algorithm, even if exponential time algorithms are allowed. We also propose a randomized, polynomial time algorithm with expected approximation ratio 1+ √ 2/2 ≈ 1.707 for k = 2, and prove that 3/2 is the best possible expected approximation ratio for randomized algorithms.
ACM Transactions on Algorithms, 2007
Query optimization that involves expensive predicates has received considerable attention in the database community. Typically, the output to a database query is a set of tuples that satisfy certain conditions, and, with expensive predicates, these conditions may be computationally costly to verify. In the simplest case, when the query looks for the set of tuples that simultaneously satisfy k expensive predicates, the problem reduces to ordering the evaluation of the predicates so as to minimize the time to output the set of tuples comprising the answer to the query. We study different cases of the problem: the sequential case , in which a single processor is available to evaluate the predicates, and the distributed case , in which there are k processors available, each dedicated to a different attribute (column) of the database, and there is no communication cost between the processors. For the sequential case, we give a simple and fast deterministic k -approximation algorithm, and...
Journal of the ACM, 2015
Data is increasingly being bought and sold online, and Web-based marketplace services have emerged to facilitate these activities. However, current mechanisms for pricing data are very simple: buyers can choose only from a set of explicit views, each with a specific price. In this article, we propose a framework for pricing data on the Internet that, given the price of a few views, allows the price of any query to be derived automatically. We call this capability query-based pricing . We first identify two important properties that the pricing function must satisfy, the arbitrage-free and discount-free properties. Then, we prove that there exists a unique function that satisfies these properties and extends the seller's explicit prices to all queries. Central to our framework is the notion of query determinacy, and in particular instance-based determinacy : we present several results regarding the complexity and properties of it. When both the views and the query are unions of c...
2019
We study the problem of sorting under incomplete information, when queries are used to resolve uncertainties. Each of n data items has an unknown value, which is known to lie in a given interval. We can pay a query cost to learn the actual value, and we may allow an error threshold in the sorting. The goal is to find a nearly-sorted permutation by performing a minimum-cost set of queries. We show that an offline optimum query set can be found in polynomial time, and that both oblivious and adaptive problems have simple query-competitive algorithms. The query-competitiveness for the oblivious problem is n for uniform query costs, and unbounded for arbitrary costs; for the adaptive problem, the ratio is 2. We then present a unified adaptive strategy for uniform query costs that yields: (i) a 3/2-query-competitive randomized algorithm; (ii) a 5/3-query-competitive deterministic algorithm if the dependency graph has no 2-components after some preprocessing, which has query-competitive r...
International Joint Conference on Artificial Intelligence, 2005
Query learning models from computational learning theory (CLT) can be adopted to perform elicitation in combinatorial auctions. Indeed, a recent elicitation framework demonstrated that the equivalence queries of CLT can be usefully simulated with price-based demand queries. In this paper, we validate the flexibility of this framework by defining a learning algorithm for atomic bidding languages, a class that includes XOR and OR. We also handle incentives, characterizing the communication requirements of the Vickrey-Clarke-Groves outcome rule. This motivates an extension to the earlier learning framework that brings truthful responses to queries into an equilibrium.
2003
We consider the revenue-maximization problem for a seller with an unlimited supply of identical goods, interacting sequentially with a population of n buyers through an on-line posted-price auction mechanism, a paradigm which is frequently available to vendors selling goods over the Internet. For each buyer, the seller names a price between 0 and 1; the buyer decides whether or not to buy the item at the specified price, based on her privately-held valuation. The price offered is allowed to vary as the auction proceeds, as the seller gains information from interactions with the earlier buyers.
Arxiv preprint arXiv:1002.4019, 2010
In query learning, the goal is to identify an unknown object while minimizing the number of "yes" or "no" questions (queries) posed about that object. A well-studied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize the expected number of queries needed to identify the unknown object. We also generalize GBS in two ways. First, we consider the case where the cost of querying grows exponentially in the number of queries and the goal is to minimize the expected exponential cost. Then, we consider the case where the objects are partitioned into groups, and the objective is to identify only the group to which the object belongs. We derive algorithms to address these issues in a common, information-theoretic framework. In particular, we present an exact formula for the objective function in each case involving Shannon or Rényi entropy, and develop a greedy algorithm for minimizing it. Our algorithms are demonstrated on two applications of query learning, active learning and emergency response.
INFORMS Journal on Computing, 2002
T his paper addresses the problem of completely reconstructing deterministic monotone Boolean functions via membership queries. The minimum average query complexity is guaranteed via recursion, where partially ordered sets (posets) make up the overlapping subproblems. For problems with up to 4 variables, the posets' optimality conditions are summarized in the form of an evaluative criterion. The evaluative criterion extends the computational feasibility to problems involving up to about 20 variables. A framework for unbiased average case comparison of monotone Boolean function inference algorithms is developed using unequal probability sampling. The unbiased empirical results show that an implementation of the subroutine considered as a standard in the literature performs almost twice as many queries as the evaluative criterion on the average. It should also be noted that the first algorithm ever designed for this problem performed consistently within two percentage points of the evaluative criterion. As such, it prevails, by far, as the most efficient of the many preexisting algorithms.
2001
The (unit-cost) comparison tree model has long been the basis of evaluating the performance of algorithms for fundamental problems like sorting and searching. In this model, the assumption is that elements of some total order are not given to us directly, but only through a black-box, which performs comparisons between the elements and outputs the result of the comparison.
Information Processing Letters, 1998
We consider the problem of identifying an unknown value x ∈ {1, 2,. .. , n} by asking "Yes-No" questions about x. The goal is to minimize the number of questions required in the worst case, taking into account that no more than B questions may receive answer "Yes" and no more than E answers may be erroneous. We consider two versions of this problem: the discrete and the continuous version. In the discrete case x is a member of the finite set {1, 2,. .. , n}; in the continuous case x is a member of the half-open real interval (0, 1]. In the continuous case we will not in general be able to identify x exactly with a finite number of questions; rather we fix a size ε and then we compute the exact value of the minimal number of questions to get a subset of (0, 1] of size ε which is known to contain x. The solution of the continuous version allows us to derive a lower bound for the minimal number of questions required for the discrete version of the problem.
Proceedings of the AAAI Conference on Artificial Intelligence
In the online (time-series) search problem, a player is presented with a sequence of prices which are revealed in an online manner. In the standard definition of the problem, for each revealed price, the player must decide irrevocably whether to accept or reject it, without knowledge of future prices (other than an upper and a lower bound on their extreme values), and the objective is to minimize the competitive ratio, namely the worst case ratio between the maximum price in the sequence and the one selected by the player. The problem formulates several applications of decision-making in the face of uncertainty on the revealed samples. Previous work on this problem has largely assumed extreme scenarios in which either the player has almost no information about the input, or the player is provided with some powerful, and error-free advice. In this work, we study learning-augmented algorithms, in which there is a potentially erroneous prediction concerning the input. Specifically, we ...
Lecture Notes in Computer Science, 2013
There exists a growing market for structured data on the Internet today, and this motivates a theoretical study of how relational data should be priced. We advocate for a framework where the seller defines a pricing scheme, by essentially stipulating the price of some queries, and the buyer is allowed to purchase data expressed by any query they wish: the system will derive the price automatically from the pricing scheme. We show that, in order to understand pricing, one needs to understand determinacy first. We also discuss some other open problems in pricing relational data.
LATIN 2020: Theoretical Informatics, 2020
We study problems with stochastic uncertainty data on intervals for which the precise value can be queried by paying a cost. The goal is to devise an adaptive decision tree to find a correct solution to the problem in consideration while minimizing the expected total query cost. We show that sorting in this scenario can be performed in polynomial time, while finding the data item with minimum value seems to be hard. This contradicts intuition, since the minimum problem is easier both in the online setting with adversarial inputs and in the offline verification setting. However, the stochastic assumption can be leveraged to beat both deterministic and randomized approximation lower bounds for the online setting. Although some literature has been devoted to minimizing query/probing costs when solving uncertainty problems with stochastic input, none of them have considered the setting we describe. Our approach is closer to the study of query-competitive algorithms, and it gives a better perspective on the impact of the stochastic assumption.
Algorithmica, 2009
Online search is a basic online problem. The fact that its optimal deterministic/randomized solutions are given by simple formulas (however with difficult analysis) makes the problem attractive as a target to which other practical online problems can be transformed to find optimal solutions. However, since the upper/lower bounds of prices in available models are constant, natural online problems in which these bounds vary with time do not fit in the available models. We present two new models where the bounds of prices are not constant but vary with time in certain ways. The first model, where the upper and lower bounds of (logarithmic) prices have decay speed, arises from a problem in concurrent data structures, namely to maximize the (appropriately defined) freshness of data in concurrent objects. For this model we present an optimal deterministic algorithm with competitive ratio √ D, where D is the known duration of the game, and a nearly-optimal randomized algorithm with competitive ratio ln D 1+ln 2− 2 D. We also prove that the lower bound of competitive ratios of randomized algorithms is asymptotically ln D 4. The second model is inspired by the fact that some applications do not utilize the decay speed of the lower bound of prices in the first model. In the second model, only the upper bound decreases arbitrarily with time and the lower bound is constant. Clearly, the lower bound of competitive ratios proved for the first model holds
International Journal of Intelligent Systems, 2010
This paper introduces a new type of database queries involving preferences. The idea is to consider competitive conditional preference clauses structured as a tree, of the type "preferably P 1 or • • • or P n ; if P 1 then preferably P 1,1 or. . .; if P 2 then preferably P 2,1 or. .. ," where the P i s are not exclusive (thus the notion of competition). The paper defines two possible interpretations of such queries and outlines two evaluation techniques which follow from them.
Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 2013
We resolve the complexity of revenue-optimal deterministic auctions in the unit-demand single-buyer Bayesian setting, i.e., the optimal item pricing problem, when the buyer's values for the items are independent. We show that the problem of computing a revenue-optimal pricing can be solved in polynomial time for distributions of support size 2, and its decision version is NP-complete for distributions of support size 3. We also show that the problem remains NP-complete for the case of identical distributions. * Columbia University.
2020
State-of-the-art posted-price mechanisms for submodular bidders with $m$ items achieve approximation guarantees of $O((\log \log m)^3)$ [Assadi and Singla, 2019]. Their truthfulness, however, requires bidders to compute an NP-hard demand-query. Some computational complexity of this form is unavoidable, as it is NP-hard for truthful mechanisms to guarantee even an $m^{1/2-\varepsilon}$-approximation for any $\varepsilon > 0$ [Dobzinski and Vondr\'ak, 2016]. Together, these establish a stark distinction between computationally-efficient and communication-efficient truthful mechanisms. We show that this distinction disappears with a mild relaxation of truthfulness, which we term implementation in advised strategies, and that has been previously studied in relation to "Implementation in Undominated Strategies" [Babaioff et al, 2009]. Specifically, advice maps a tentative strategy either to that same strategy itself, or one that dominates it. We say that a player follows...
2021
12 We present elements of a Coq/SSReflect proof of the truthfulness of the Vickrey-Clarke-Groves 13 (VCG) auction algorithm for sponsored search (VCG for Search), variants of which are daily used by 14 companies such as Google and Facebook for their advertising engines. We start from a formalization 15 of the more general VCG mechanism, for which proving truthfulness, i.e., that bidders get the best 16 utility by bidding their true value, is somewhat easy. We then show how VCG for Search can be 17 seen as a functional instance of this mechanism, thus getting among other properties and for almost 18 free a proof of a restricted version of the truthfulness of VCG for Search. Future work will focus on 19 extending this preliminary result to the full theorem. 2
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