5 citations · 20 across the 11 of their papers we have counts for
15 papers
Iterative Vickrey Auctions via Linear Programming
Sébastien Lahaie, Benjamin Lubin
Building on the linear programming approach to competitive equilibrium pricing, we develop a general method for constructing iterative auctions that achieve Vickrey-Clarke-Groves (…
Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard +2
In experimental causal inference, we distinguish between two sources of uncertainty: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when t…
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie +5
We investigate the optimal design of experimental studies that have pre-treatment outcome data available. The average treatment effect is estimated as the difference between the we…
From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics
ChunJun Cao
In this note, I review a recent approach to quantum gravity that "gravitizes" quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insigh…
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…
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…
Learning to Clear the Market
Weiran Shen, Sébastien Lahaie, Renato Paes Leme
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices i…
Fast Iterative Combinatorial Auctions via Bayesian Learning
Gianluca Brero, Sébastien Lahaie, Sven Seuken
Iterative combinatorial auctions (CAs) are often used in multi-billion dollar domains like spectrum auctions, and speed of convergence is one of the crucial factors behind the choi…
A Bayesian Clearing Mechanism for Combinatorial Auctions
Gianluca Brero, Sébastien Lahaie
We cast the problem of combinatorial auction design in a Bayesian framework in order to incorporate prior information into the auction process and minimize the number of rounds to…
Fair Allocation of Indivisible Goods to Asymmetric Agents
Alireza Farhadi, Mohammad Ghodsi, MohammadTaghi Hajiaghayi +5
We study fair allocation of indivisible goods to agents with unequal entitlements. Fair allocation has been the subject of many studies in both divisible and indivisible settings.…
A Decomposition of Forecast Error in Prediction Markets
Miroslav Dudík, Sébastien Lahaie, Ryan Rogers +1
We analyze sources of error in prediction market forecasts in order to bound the difference between a security's price and the ground truth it estimates. We consider cost-function-…
Crowdsourced Outcome Determination in Prediction Markets
Rupert Freeman, Sebastien Lahaie, David M. Pennock
A prediction market is a useful means of aggregating information about a future event. To function, the market needs a trusted entity who will verify the true outcome in the end. M…
Arbitrage-Free Combinatorial Market Making via Integer Programming
Christian Kroer, Miroslav Dudík, Sébastien Lahaie +1
We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs. Although the problem of arbitrage-free pric…
Rate of Price Discovery in Iterative Combinatorial Auctions
Jacob Abernethy, Sébastien Lahaie, Matus Telgarsky
We study a class of iterative combinatorial auctions which can be viewed as subgradient descent methods for the problem of pricing bundles to balance supply and demand. We provide…
Information Aggregation in Exponential Family Markets
Jacob Abernethy, Sindhu Kutty, Sébastien Lahaie +1
We consider the design of prediction market mechanisms known as automated market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distri…