Maplebear Inc.

Columbia University

Changyao Chen

Peng Qi

Weian Sheng

James Hone

Sami Rosenblatt

Tilman Drerup

Jonathan Gu

Sunwoo Lee

Amin Akbari

Nour Alkhatib

An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.

Automated policy function adjustment using reinforcement learning algorithm

An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

Training a model to predict likelihoods of users performing an action after being presented with a content item

An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a generic item description. When the online concierge system receives a generic item description from a user for inclusion in an order, the online concierge system uses the taxonomy to select a set of items associated with the generic item description. Based on probabilities of the user purchasing various items of the set, the online concierge system selects an item of the set for inclusion in the order For example, the online concierge system selects an item of the set for which the user has a maximum probability of being purchased. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item of the set.

Selecting an item for inclusion in an order from a user of an online concierge system from a generic item description received from the user

An online concierge system maintains historical orders received from a user that include one or more items. For items included in one more historical orders, the online concierge system determines an interval between orders including an item, providing an indication of a frequency with which the user orders the item. When the online concierge system receives a request to create an order from the user, in response to an amount of time between a most recently received order including the item and a time when the request was received is within a threshold duration of the interval between orders including the item, the online concierge system selects an item from a category including the item. The selected item may be the item or an alternative item in the category. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item.

User interface that pre-populates items in an order module for a user of an online concierge system using a prediction model

A nano-electro-mechanical systems (NEMS) oscillator can include an insulating substrate, a source electrode and a drain electrode, a metal local gate electrode, and a micron-sized, atomically thin graphene resonator. The source electrode and drain electrode can be disposed on the insulating substrate. The metal local gate electrode can be disposed on the insulating substrate. The graphene resonator can be suspended over the metal local gate electrode and define a vacuum gap between the graphene resonator and the metal local gate electrode.

Systems and methods for graphene mechanical oscillators with tunable frequencies

A fabricated electromechanical device is disclosed herein. An exemplary device includes, a substrate, at least one layer of a high-transconductance material separated from the substrate by a dielectric medium, a first electrode in electrical contact with the at least one layer of a high-transconductance material and separated from the substrate by at least one first supporting member, a second electrode in electrical contact with the layer of a high-transconductance material and separated from the substrate by at least one second supporting member, where the first electrode is electrically separate from the second electrode, and a third electrode separated from the at least one layer of high-transconductance material by a dielectric medium and separated from each of the first electrode and the second electrode by a dielectric medium.

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New York, NY, United States of America

Changyao Chen