Xerox Corporation

Microsoft Technology Licensing, LLC

Onno R Zoeter

Christopher R Dance

Guillaume M Bouchard

Jean-Marc Andreoli

Shengbo Guo

Victor Ciriza

Michael James Taylor

John Guiver

Nicholas Eric Craswell

Venkatesh Guru Rao

Martin Szummer

Yu-An Sun

Cedric Archambeau

Edward Lloyd Snelson

Mihajlo Grbovic

A system and method for generating an occupancy model are disclosed. The model is learned using occupancy data for zones, each zone including cells, which are occupied or not at a given time, each with a sensor, which may be reporting or not. The data provides an observed occupancy corresponding to a number of cells in the respective zone which have reporting sensors, and the number of those sensors which are reporting that the respective cell is occupied. The occupancy model is based on a demand model and a sensor noise model which accounts for behavior of the non-reporting sensors. The noise model assumes that the probability of a sensor being in the reporting state is dependent on whether the respective cell is occupied or not. The model can fit the occupancy data better than one which assumes that non-reporting cells are occupied with the same frequency as reporting ones.

System and method for generating an occupancy model

Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D&#x2267;1 and P&#x2267;1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.

Multi-task learning using bayesian model with enforced sparsity and leveraging of task correlations

A system and method of localizing elements (shared devices and/or their users) in a device infrastructure, such as a printing network, are provided. The method includes mapping a structure in which the elements of a device infrastructure are located, the elements comprising shared devices and users of the shared devices. Probable locations of fewer than all of the elements in the structure are mapped, with at least some of the elements being initially assigned to an unknown location. Usage logs for a plurality of the shared devices are acquired. The acquired usage log for each device includes a user identifier for each of a set of uses of the device, each of the uses being initiated from a respective location within the mapped structure by one of the users. Based on the acquired usage logs and the input probable locations of some of the elements, locations of at least some of the elements initially assigned to an unknown location are predicted. The prediction is based a model which infers that for each of a plurality of the users, a usage of at least some of the shared devices by the user is a function of respective distances between the user and each of those devices.

User and device localization using probabilistic device log trilateration

A method and apparatus for paying for an existing report are provided. In the method, an existing report for which a first structure of entities is identified is received. Thereafter, a set of other reports for which respective second structures of entities are identified is received. A weighting for other reports in the set of other reports is assigned, based on the respective structures. A payment for the existing report is extracted based on the weighting, a selected scoring rule, and the set of other reports.

Method for estimation of a payment for an existing report based on subsequent reports which provides incentives for reporters to report truthfully

A system and method are provided. The method includes establishing a current belief about the multidimensional distribution of buyers' valuations for at least one item, and, based on the current belief, proposing at least one pricing mechanism, each pricing mechanism establishing a price for the at least one item. Observed buyers' responses to at least one of the set of proposed pricing mechanisms are stored. Region censored updates to the belief about the multidimensional distribution of buyers' valuations are conducted, based on the observed responses, to generate a new belief about the multidimensional distribution of buyers' valuations. Based on the new belief, a pricing mechanism establishing a price for the at least one item is proposed, that is expected to improve a seller's welfare under the new belief, relative to the originally proposed mechanism or mechanisms.

Multi-dimensional price determination

A system and method for conducting a lottery for at least one item are provided. The method includes, for each of a plurality of buyers, receiving a buyer's declared valuation for each of at least one item being offered in a lottery by a seller, the item having an assigned non-deterministic probability of being allocated to the buyer, providing insured prices for outcomes of the lottery which are a function of the buyer's declared valuation of the at least one item, randomly drawing an allocation of each of the at least one item to a respective one of the buyers, based on its assigned non-deterministic probability, and allocating the insured prices to the buyers based on respective outcomes of the random drawing.

Limited lottery insurance

According to various embodiments, the present teachings include inventory control policies that are defined in terms of functions of aggregate cost rates, involving thresholds &#x3a9; and an order-up-to point S. An embodiment of the present teachings includes a method. The method includes tracking an inventory position of each of the plurality of items by a logistics network and determining an item cost rate for each of the plurality of items based on the tracked inventory position. The method also includes determining an aggregate cost rate for the plurality of items based on the determined item cost rates, comparing the aggregate cost rate with a cost rate threshold &#x3a9;, and ordering the plurality of items to an order-up-to point S if the compared aggregate cost rate is greater than or equal to the cost rate threshold &#x3a9;.

Methods for supply chain management

A valuation optimization method includes generating offeree decision information (buyer decision information, by way of illustrative example) by presenting a sequence of mechanisms to a sequence of offerees wherein the mechanisms comprise menus of transaction offers (sale offer menus, by way of illustrative example). Actual transactions (sale transactions, by way of illustrative example) are conducted responsive to acceptances of transaction offers by buyers. At a selected time in the generating, an offeree valuation distribution belief and the current mechanism are updated optimize an offeree's utility. The offeree's utility comprises an offeree's utility function constrained by a robust learning constraint computed based on a local differential of an earlier offeree's utility function with respect to the earlier offeree's valuation.

Price optimization with robust learning constraint

A method comprises: partitioning a region of interest into a plurality of soft bin regions that span the region of interest; estimating an integral over each soft bin region of a function defined over the region of interest; and outputting a value equal to or derived from the sum of the estimated integrals over the soft bin regions spanning the region of interest. The method may further comprise: integrating a Bayesian theorem function using the partitioning, estimating, and outputting operations, and classifying an object to be classified using a classifier trained using the Bayesian machine learning. The method may further comprise performing optimal control by iteratively minimizing a controlled system cost function to determine optimized control inputs using the partitioning, estimating, and outputting with the function equal to the controlled system cost function having the selected control inputs, and controlling the controlled system using the optimized control inputs.

Split variational inference

An information retrieval system is described for retrieving a list of documents such as web pages or other items from a document index in response to a user query. In an embodiment a prediction engine is used to predict both explicit relevance information such as judgment labels and implicit relevance information such as click data. In an embodiment the predicted relevance information is applied to a stored utility function that describes user satisfaction with a search session. This produces utility scores for proposed lists of documents. Using the utility scores one of the lists of documents is selected. In this way different sources of relevance information are combined into a single information retrieval system in a principled and effective manner which gives improved performance.

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Grenoble, France

Onno R Zoeter