14 citations · 18 across the 7 of their papers we have counts for
8 papers
Recommending to Strategic Users
Andreas Haupt, Dylan Hadfield-Menell, Chara Podimata
Recommendation systems are pervasive in the digital economy. An important assumption in many deployed systems is that user consumption reflects user preferences in a static sense:…
Opaque Contracts
Andreas Haupt, Zoe Hitzig
Firms have access to abundant data on market participants. They use these data to target contracts to agents with specific characteristics, and describe these contracts in opaque t…
Towards Psychologically-Grounded Dynamic Preference Models
Mihaela Curmei, Andreas Haupt, Dylan Hadfield-Menell +1
Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior an…
Risk Preferences of Learning Algorithms
Andreas Haupt, Aroon Narayanan
Agents' learning from feedback shapes economic outcomes, and many economic decision-makers today employ learning algorithms to make consequential choices. This note shows that a wi…
Contextually Private Mechanisms
Andreas Haupt, Zoë Hitzig
We introduce a framework for comparing the privacy of different mechanisms. A mechanism designer employs a dynamic protocol to elicit agents' private information. Protocols produce…
The Optimality of Upgrade Pricing
Dirk Bergemann, Alessandro Bonatti, Andreas Haupt +1
We consider a multiproduct monopoly pricing model. We provide sufficient conditions under which the optimal mechanism can be implemented via upgrade pricing -- a menu of product bu…
Prior-Independent Auctions for the Demand Side of Federated Learning
Andreas Haupt, Vaikkunth Mugunthan
Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. While largely decentra…
Classification on Large Networks: A Quantitative Bound via Motifs and Graphons
Andreas Haupt, Mohammad Khatami, Thomas Schultz +1
When each data point is a large graph, graph statistics such as densities of certain subgraphs (motifs) can be used as feature vectors for machine learning. While intuitive, motif…