research
∙
08/30/2023
Knowledge-grounded Natural Language Recommendation Explanation
Explanations accompanied by a recommendation can assist users in underst...
share
research
∙
07/10/2023
Exploring the Dynamics of the Specialty Insurance Market Using a Novel Discrete Event Simulation Framework: a Lloyd's of London Case Study
This research presents a novel Discrete Event Simulation (DES) of the Ll...
research
∙
06/01/2023
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
We study the design of loss functions for click-through rates (CTR) to o...
research
∙
02/16/2023
Joint Feedback Loop for Spend and Return-On-Spend Constraints
Budget pacing is a popular service that has been offered by major intern...
research
∙
02/16/2023
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization
We propose a new Markov Decision Process (MDP) model for ad auctions to ...
research
∙
08/29/2022
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers
Online advertising has recently grown into a highly competitive and comp...
research
∙
06/02/2022
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Incrementality, which is used to measure the causal effect of showing an...
research
∙
02/22/2022
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
In today's economy, it becomes important for Internet platforms to consi...
research
∙
01/29/2022
A Context-Integrated Transformer-Based Neural Network for Auction Design
One of the central problems in auction design is developing an incentive...
research
∙
10/15/2021
Modeling Endorsement for Multi-Document Abstractive Summarization
A crucial difference between single- and multi-document summarization is...
research
∙
09/07/2021
Learning to Bid in Contextual First Price Auctions
In this paper, we investigate the problem about how to bid in repeated c...
research
∙
07/09/2021
Robust Clearing Price Mechanisms for Reserve Price Optimization
Setting an effective reserve price for strategic bidders in repeated auc...
research
∙
07/07/2021
Deep Learning for Two-Sided Matching
We initiate the use of a multi-layer neural network to model two-sided m...
research
∙
07/05/2021
Weakly Supervised Named Entity Tagging with Learnable Logical Rules
We study the problem of building entity tagging systems by using a few r...
research
∙
04/13/2021
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Instead of using expensive manual annotations, researchers have proposed...
research
∙
04/05/2021
A New Approach to Overgenerating and Scoring Abstractive Summaries
We propose a new approach to generate multiple variants of the target su...
research
∙
09/14/2020
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
The connection between games and no-regret algorithms has been widely st...
research
∙
07/19/2020
Welfare-Preserving ε-BIC to BIC Transformation with Negligible Revenue Loss
In this paper, we investigate the problem of transforming an ε-BIC mecha...
research
∙
06/11/2020
Reserve Price Optimization for First Price Auctions
The display advertising industry has recently transitioned from second- ...
research
∙
11/23/2019
Controlling the Amount of Verbatim Copying in Abstractive Summarization
An abstract must not change the meaning of the original text. A single m...
research
∙
06/04/2019
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
We study the behavior of stochastic bandits algorithms under strategic b...
research
∙
05/09/2019
Maximum A Posteriori Probability (MAP) Joint Fine Frequency Offset and Channel Estimation for MIMO Systems with Channels of Arbitrary Correlation
Channel and frequency offset estimation is a classic topic with a large ...
research
∙
01/21/2019
Online Learning for Measuring Incentive Compatibility in Ad Auctions
In this paper we investigate the problem of measuring end-to-end Incenti...
research
∙
06/18/2018
An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification
In this work, we propose an ensemble of classifiers to distinguish betwe...
research
∙
06/15/2018
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection
Automatic heart sound abnormality detection can play a vital role in the...
research
∙
11/03/2017
Learning to Bid Without Knowing your Value
We address online learning in complex auction settings, such as sponsore...
research
∙
06/12/2017