7 citations · 30 across the 20 of their papers we have counts for
22 papers
Euler Cutting Planes for Fractional CVaR Programming
Pranay Seshadri, Hubert Dzieciol, Zhe Feng +1
We maximise expected reward per unit of marginal tail risk, a reward-to-CVaR ratio against a baseline. The standard Dinkelbach route re-solves a Rockafellar--Uryasev LP or QP with…
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Zhe Feng, Christopher Liaw, Zixin Zhou
In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate…
Exploring the Dynamics of the Specialty Insurance Market Using a Novel Discrete Event Simulation Framework: a Lloyd's of London Case Study
Sedar Olmez, Akhil Ahmed, Keith Kam +2
This research presents a novel Discrete Event Simulation (DES) of the Lloyd's of London specialty insurance market, exploring complex market dynamics that have not been previously…
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
Boxiang Lyu, Zhe Feng, Zachary Robertson +1
We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions. Existing works either only focus on CTR predictions withou…
A Field Guide for Pacing Budget and ROS Constraints
Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng +4
Budget pacing is a popular service that has been offered by major internet advertising platforms since their inception. Budget pacing systems seek to optimize advertiser returns su…
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization
Yang Cai, Zhe Feng, Christopher Liaw +2
We propose a new Markov Decision Process (MDP) model for ad auctions to capture the user response to the quality of ads, with the objective of maximizing the long-term discounted r…
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers
Zhe Feng, Swati Padmanabhan, Di Wang
Online advertising has recently grown into a highly competitive and complex multi-billion-dollar industry, with advertisers bidding for ad slots at large scales and high frequencie…
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li +1
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advert…
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
Jibang Wu, Zixuan Zhang, Zhe Feng +4
In today's economy, it becomes important for Internet platforms to consider the sequential information design problem to align its long term interest with incentives of the gig ser…
A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan, Jingwu Tang, Yutong Yin +4
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have en…
Learning to Bid in Contextual First Price Auctions
Ashwinkumar Badanidiyuru, Zhe Feng, Guru Guruganesh
In this paper, we investigate the problem about how to bid in repeated contextual first price auctions. We consider a single bidder (learner) who repeatedly bids in the first price…
Robust Clearing Price Mechanisms for Reserve Price Optimization
Zhe Feng, Sébastien Lahaie
Setting an effective reserve price for strategic bidders in repeated auctions is a central question in online advertising. In this paper, we investigate how to set an anonymous res…
Deep Learning for Two-Sided Matching
Sai Srivatsa Ravindranath, Zhe Feng, Shira Li +3
We initiate the study of deep learning for the automated design of two-sided matching mechanisms. What is of most interest is to use machine learning to understand the possibility…
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
Zhe Feng, Guru Guruganesh, Christopher Liaw +2
The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produ…
Welfare-Preserving -BIC to BIC Transformation with Negligible Revenue Loss
Vincent Conitzer, Zhe Feng, David C. Parkes +1
In this paper, we provide a transform from an -BIC mechanism into an exactly BIC mechanism without any loss of social welfare and with additive and negligible revenue…
Reserve Price Optimization for First Price Auctions
Zhe Feng, Sébastien Lahaie, Jon Schneider +1
The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers…
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng, David C. Parkes, Haifeng Xu
Motivated by economic applications such as recommender systems, we study the behavior of stochastic bandits algorithms under \emph{strategic behavior} conducted by rational actors,…
Online Learning for Measuring Incentive Compatibility in Ad Auctions
Zhe Feng, Okke Schrijvers, Eric Sodomka
In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an es…
Learning to Bid Without Knowing your Value
Zhe Feng, Chara Podimata, Vasilis Syrgkanis
We address online learning in complex auction settings, such as sponsored search auctions, where the value of the bidder is unknown to her, evolving in an arbitrary manner and obse…
Optimal Auctions through Deep Learning: Advances in Differentiable Economics
Paul Dütting, Zhe Feng, Harikrishna Narasimhan +2
Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but…
Power-Law Distributions in a Two-sided Market and Net Neutrality
Xiaotie Deng, Zhe Feng, Christos H. Papadimitriou
"Net neutrality" often refers to the policy dictating that an Internet service provider (ISP) cannot charge content providers (CPs) for delivering their content to consumers. Many…
An adaptive independence sampler MCMC algorithm for infinite dimensional Bayesian inferences
Zhe Feng, Jinglai Li
Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, many standard…