7 citations · 15 across the 18 of their papers we have counts for
20 papers
Learning Anonymous Pricing for Online Resource Allocation
Yifeng Teng, Yifan Wang
We study the online resource allocation problem, where a seller sequentially receives independent requests for types of resources with limited supplies from heterogeneous a…
Learning Optimal Posted Prices for a Unit-Demand Buyer
Yifeng Teng, Yifan Wang
We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions.…
Allocating Variance to Maximize Expectation
Renato Purita Paes Leme, Cliff Stein, Yifeng Teng +1
We design efficient approximation algorithms for maximizing the expectation of the supremum of families of Gaussian random variables. In particular, let $\mathrm{OPT}:=\max_{σ_1,\c…
U-Calibration: Forecasting for an Unknown Agent
Robert Kleinberg, Renato Paes Leme, Jon Schneider +1
We consider the problem of evaluating forecasts of binary events whose predictions are consumed by rational agents who take an action in response to a prediction, but whose utility…
Prophet Secretary Against the Online Optimal
Paul Dütting, Evangelia Gergatsouli, Rojin Rezvan +2
We study the prophet secretary problem, a well-studied variant of the classic prophet inequality, where values are drawn from independent known distributions but arrive in uniforml…
Description Complexity of Regular Distributions
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
Myerson's regularity condition of a distribution is a standard assumption in economics. In this paper, we study the complexity of describing a regular distribution within a small s…
Buy-Many Mechanisms for Many Unit-Demand Buyers
Shuchi Chawla, Rojin Rezvan, Yifeng Teng +1
A recent line of research has established a novel desideratum for designing approximately-revenue-optimal multi-item mechanisms, namely the buy-many constraint. Under this constrai…
Pricing Query Complexity of Revenue Maximization
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most wit…
Online Allocation and Display Ads Optimization with Surplus Supply
Melika Abolhassani, Hossein Esfandiari, Yasamin Nazari +3
In this work, we study a scenario where a publisher seeks to maximize its total revenue across two sales channels: guaranteed contracts that promise to deliver a certain number of…
Worst-Case Welfare of Item Pricing in the Tollbooth Problem
Zihan Tan, Yifeng Teng, Mingfei Zhao
We study the worst-case welfare of item pricing in the \emph{tollbooth problem}. The problem was first introduced by Guruswami et al, and is a special case of the combinatorial auc…
Learning to Price Against a Moving Target
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood whe…
Pricing Ordered Items
Shuchi Chawla, Rojin Rezvan, Yifeng Teng +1
We study the revenue guarantees and approximability of item pricing. Recent work shows that with heterogeneous items, item-pricing guarantees an approximation to th…
Menu-size Complexity and Revenue Continuity of Buy-many Mechanisms
Shuchi Chawla, Yifeng Teng, Christos Tzamos
We study the multi-item mechanism design problem where a monopolist sells heterogeneous items to a single buyer. We focus on buy-many mechanisms, a natural class of mechanisms…
Why Do Competitive Markets Converge to First-Price Auctions?
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng
We consider a setting in which bidders participate in multiple auctions run by different sellers, and optimize their bids for the \emph{aggregate} auction. We analyze this setting…
Pandora's Box with Correlations: Learning and Approximation
Shuchi Chawla, Evangelia Gergatsouli, Yifeng Teng +2
The Pandora's Box problem and its extensions capture optimization problems with stochastic input where the algorithm can obtain instantiations of input random variables at some cos…
Revenue Maximization for Query Pricing
Shuchi Chawla, Shaleen Deep, Paraschos Koutris +1
Buying and selling of data online has increased substantially over the last few years. Several frameworks have already been proposed that study query pricing in theory and practice…
Buy-many mechanisms are not much better than item pricing
Shuchi Chawla, Yifeng Teng, Christos Tzamos
Multi-item mechanisms can be very complex offering many different bundles to the buyer that could even be randomized. Such complexity is thought to be necessary as the revenue gaps…
Pricing for Online Resource Allocation: Intervals and Paths
Shuchi Chawla, J. Benjamin Miller, Yifeng Teng
We present pricing mechanisms for several online resource allocation problems which obtain tight or nearly tight approximations to social welfare. In our settings, buyers arrive on…
Tight Bound on Randomness for Violating the CHSH Inequality
Yifeng Teng, Shenghao Yang, Siwei Wang +1
Free will (or randomness) has been studied to achieve loophole-free Bell's inequality test and to provide device-independent quantum key distribution security proofs. The required…
Computational issues in time-inconsistent planning
Pingzhong Tang, Yifeng Teng, Zihe Wang +2
Time-inconsistency refers to a paradox in decision making where agents exhibit inconsistent behaviors over time. Examples are procrastination where agents tends to costly postpone…