output
20092024
most citedA Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients

227 citations

Showing 2016Show all

13 papers · 1 filter

cs.IR2016★ 27 cited

Predicting drug recalls from Internet search engine queries

Elad Yom-Tov

Batches of pharmaceutical are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detect…

cs.IR2016★ 4 cited

CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations

Oren Barkan, Noam Koenigstein, Eylon Yogev +1

In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user…

cs.IR2016★ 24 cited

Inferring individual attributes from search engine queries and auxiliary information

Luca Soldaini, Elad Yom-Tov

Internet data has surfaced as a primary source for investigation of different aspects of human behavior. A crucial step in such studies is finding a suitable cohort (i.e., a set of…

cs.LG2016★ 1 cited

Predicting Counterfactuals from Large Historical Data and Small Randomized Trials

Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov

When a new treatment is considered for use, whether a pharmaceutical drug or a search engine ranking algorithm, a typical question that arises is, will its performance exceed that…

cs.GT2016★ 2 cited

Efficient Empirical Revenue Maximization in Single-Parameter Auction Environments

Yannai A. Gonczarowski, Noam Nisan

We present a polynomial-time algorithm that, given samples from the unknown valuation distribution of each bidder, learns an auction that approximately maximizes the auctioneer's r…

cs.GT2016★ 1 cited

Gibbard-Satterthwaite Success Stories and Obvious Strategyproofness

Sophie Bade, Yannai A. Gonczarowski

The Gibbard-Satterthwaite Impossibility Theorem holds that dictatorship is the only Pareto optimal and strategyproof social choice function on the full domain of preferences. Much…