99 citations · 203 across the 27 of their papers we have counts for
32 papers
Computing Perfect Bayesian Equilibria in Sequential Auctions with Verification
Vinzenz Thoma, Vitor Bosshard, Sven Seuken
We present an algorithm for computing pure-strategy epsilon-perfect Bayesian equilibria in sequential auctions with continuous action and value spaces. Importantly, our algorithm i…
Machine Learning-Powered Combinatorial Clock Auction
Ermis Soumalias, Jakob Weissteiner, Jakob Heiss +1
We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address th…
Machine Learning-Powered Course Allocation
Ermis Soumalias, Behnoosh Zamanlooy, Jakob Weissteiner +1
We study the course allocation problem, where universities assign course schedules to students. The current state-of-the-art mechanism, Course Match, has one major shortcoming: stu…
Bayesian Optimization-based Combinatorial Assignment
Jakob Weissteiner, Jakob Heiss, Julien Siems +1
We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponent…
Non-decreasing Payment Rules for Combinatorial Auctions
Vitor Bosshard, Ye Wang, Sven Seuken
Combinatorial auctions are used to allocate resources in domains where bidders have complex preferences over bundles of goods. However, the behavior of bidders under different paym…
Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement
Nils Olberg, Sven Seuken
The Swiss State Secretariat for Migration recently announced a pilot project for a machine learning-based assignment process for refugee resettlement. This approach has the potenti…
Market Design for Drone Traffic Management
Sven Seuken, Paul Friedrich, Ludwig Dierks
The rapid development of drone technology is leading to more and more use cases being proposed. In response, regulators are drawing up drone traffic management frameworks. However,…
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
Jakob Weissteiner, Jakob Heiss, Julien Siems +1
Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the…
Shapley-Based Core-Selecting Payment Rules
Vitor Bosshard, Sven Seuken
In this research note, we lay some groundwork for analyzing the manipulability of core-selecting payment rules in combinatorial auctions. In particular, we focus on payment rules b…
Search and Matching for Adoption from Foster Care
Ludwig Dierks, Nils Olberg, Sven Seuken +2
To find families for the more than 70,000 children in need of adoptive placements, most United States child welfare agencies have employed a family-driven search approach in which…
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss, Jakob Weissteiner, Hanna Wutte +2
We study methods for estimating model uncertainty for neural networks (NNs) in regression. To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce t…
The Cost of Simple Bidding in Combinatorial Auctions
Vitor Bosshard, Sven Seuken
We study a class of manipulations in combinatorial auctions where bidders fundamentally misrepresent what goods they are interested in. Prior work has largely assumed that bidders…
iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding
Benjamin Lubin, Manuel Beyeler, Gianluca Brero +1
Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learni…
Fourier Analysis-based Iterative Combinatorial Auctions
Jakob Weissteiner, Chris Wendler, Sven Seuken +2
Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of…
Revenue Maximization for Consumer Software: Subscription or Perpetual License?
Ludwig Dierks, Sven Seuken
We study the revenue maximization problem of a publisher selling consumer software. We assume that the publisher sells either traditional perpetual licenses, subscription licenses,…
An Axiomatic Decomposition of Strategyproofness for Ordinal Mechanism with Indifferences
Timo Mennle, Sven Seuken
We study mechanism which operate on ordinal preference information (i.e., rank ordered lists of alternatives) on the full domain of weak preferences that admits indifferences. We p…
Two New Impossibility Results for the Random Assignment Problem
Timo Mennle, Sven Seuken
In this note, we prove two new impossibility results for random assignment mechanisms: Bogomolnaia and Moulin (2001) showed that no assignment mechanism can satisfy strategyproofne…
Local Sufficiency for Partial Strategyproofness
Timo Mennle, Sven Seuken
In (Mennle and Seuken, 2017), we have introduced partial strategyproofness, a new, relaxed notion of strategyproofness, to study the incentive properties of non-strategyproof assig…
The Competitive Effects of Variance-based Pricing
Ludwig Dierks, Sven Seuken
In many markets, like electricity or cloud computing markets, providers incur large costs for keeping sufficient capacity in reserve to accommodate demand fluctuations of a mostly…
Machine Learning-powered Iterative Combinatorial Auctions
Gianluca Brero, Benjamin Lubin, Sven Seuken
We present a machine learning-powered iterative combinatorial auction (MLCA). The main goal of integrating machine learning (ML) into the auction is to improve preference elicitati…
Monotonic and Non-Monotonic Solution Concepts for Generalized Circuits
Steffen Schuldenzucker, Sven Seuken
Generalized circuits are an important tool in the study of the computational complexity of equilibrium approximation problems. However, in this paper, we reveal that they have a co…
Deep Learning-powered Iterative Combinatorial Auctions
Jakob Weissteiner, Sven Seuken
In this paper, we study the design of deep learning-powered iterative combinatorial auctions (ICAs). We build on prior work where preference elicitation was done via kernelized sup…
Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification
Vitor Bosshard, Benedikt Bünz, Benjamin Lubin +1
We present a new algorithm for computing pure-strategy -Bayes-Nash equilibria (-BNEs) in combinatorial auctions with continuous value and action spaces. A…
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…