15 papers
Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts
Cheng Peng, Ruixi Luo, Zhi Chen +1
Large language models (LLMs) are increasingly used to forecast real-world events, but access to the strongest individual forecaster may be costly or otherwise constrained. We study…
The Limits of Price Discrimination with a Bayesian Seller
Yuan Deng, Yilin Li, Wei Tang +1
We study the limits of third-degree price discrimination when the production cost is Bayesian and private to the seller, generalizing the seminal work of Bergemann, Brooks and Morr…
Algorithmic Expert Aggregation
Wei Tang, Hanrui Zhang
Forecast aggregation aims to combine information from multiple Bayesian experts' forecasts into an aggregate forecast. In much of this literature, however, the aggregate forecast i…
Prior-Agnostic Robust Forecast Aggregation
Zhi Chen, Cheng Peng, Wei Tang
Robust forecast aggregation combines the predictions of multiple information sources to perform well in the worst case across all possible information structures. Previous work lar…
Rethinking Trust Region Bayesian Optimization in High Dimensions
Wei-Ting Tang, Joel A. Paulson
Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate l…
An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
Wei-Ting Tang, Akshay Kudva, Calvin Tsay +1
We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the posterior mean function has a compac…
Private Private Information in Second-Price Auction
Boyu Liu, Wei Tang, Zihe Wang +1
Classic results show that even an arbitrarily small correlation across bidders' information can enable full surplus extraction in auctions and related mechanism design settings. Mo…
Pricing Query Complexity of Multiplicative Revenue Approximation
Wei Tang, Yifan Wang, Mengxiao Zhang
We study the pricing query complexity of revenue maximization for a single buyer whose private valuation is drawn from an unknown distribution. In this setting, the seller must lea…
Simple and Robust Quality Disclosure: The Power of Quantile Partition
Shipra Agrawal, Yiding Feng, Wei Tang
Quality information on online platforms is often conveyed through simple, percentile-based badges and tiers that remain stable across different market environments. Motivated by th…
No-Regret Online Autobidding Algorithms in First-price Auctions
Yuan Deng, Yilin Li, Wei Tang +1
Automated bidding to optimize online advertising with various constraints, e.g. ROI constraints and budget constraints, is widely adopted by advertisers. A key challenge lies in de…
Explainable Information Design
Yiling Chen, Tao Lin, Wei Tang +1
Optimal signaling schemes in information design (Bayesian persuasion) often involve randomization or disconnected partitions of state space, which might be too intricate to be audi…
Is This Predictor More Informative than Another? A Decision-Theoretical Comparison
Yiding Feng, Liuhan Qian, Wei Tang
In many real-world applications, a model provider provides probabilistic forecasts to downstream decision-makers who use them to make decisions under diverse payoff objectives. The…
Persuasive Calibration
Yiding Feng, Wei Tang
We introduce and study the persuasive calibration problem, where a principal aims to provide trustworthy predictions about underlying events to a downstream agent to make desired d…
Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation
Siqing Zhang, Yuchen Ding, Wei Tang +3
Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a repr…
Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling
Wei Tang, Haifeng Xu, Ruimin Zhang +1
Prophet inequality concerns a basic optimal stopping problem and states that simple threshold stopping policies -- i.e., accepting the first reward larger than a certain threshold…