The project led to the publication of approximately 125 papers, including contributions in top-tier conferences such as ACM STOC (6), ACM-SIAM SODA (5), EC (5), ICALP (2), NeurIPS (9), ICML (5), COLT (1), ICLR (1), AAAI (6), IJCAI (7), KDD (1), The Web Conference (4), CIKM (2), SPAA (1), WINE (5), and WSDM (1), as well as in leading academic journals.
A key contribution in the first project objective was the design of algorithms and mechanisms for two-sided markets involving strategic buyers and sellers. Traditionally, economic models assume full market knowledge, but we developed simple, efficient mechanisms that require minimal information—just a single sample from prior distributions representing buyers and sellers. Another notable problem we addressed is devising an online efficient and truthful mechanism for two-sided matching markets, where bidders arrive over time, and items must be allocated immediately. We provided an optimal truthful mechanism for such online matching markets.
The problem of fair division of items has a long history and is often studied under the assumption that agents reveal their true preferences. Recognizing that this isn't always realistic, we examined fair division through simple algorithms like round-robin in scenarios where agents can misreport valuations. Surprisingly, we found that the equilibria of this game still maintained strong fairness properties. Additionally, economic algorithms are increasingly used not only for pricing goods but also for motivating agents, both human and artificial, to complete tasks. We advanced algorithmic contract theory by designing new algorithms to encourage team effort on joint projects, incorporating inspection mechanisms to penalize non-compliant workers.
We also investigated the learning rate of bidding mechanisms for advertisers on online platforms using a first-price auction model. Our work fully characterizes the impact of the type of information disclosed by the platform—such as the valuation of the advertising opportunity and the competing bids—on the learning rate.
In the second project objective, we focused on decision-making algorithms under uncertainty in dynamic online environments. We revisited the classic Pandora’s Box problem, which models search strategies in information markets where there is a cost to explore each alternative. We developed simple, efficient strategies that are optimal or near-optimal when there are precedence constraints among alternatives, or when selecting combinations of alternatives with associated values.
We also tackled the classical scheduling problem of assigning tasks arriving online to a server to minimize average wait times. Traditionally, this problem assumes perfect information on processing times, but we developed the first efficient approximation algorithms that work under uncertainty about job processing times.
Additionally, over a sequence of papers, we studied the bilateral trade problem in two-sided markets where prior information is learned online over a sequence of interactions between buyers and sellers. Our work provides a comprehensive characterization of learning rates for different feedback models in both stochastic and adversarial settings.
For the third project objective, we focused on large-scale problems involving optimal or near-optimal matching, clustering, and submodular optimization, especially in online market applications. We developed efficient algorithms for matching freelance and hired workers to tasks in large-scale crowdsourcing systems. We also introduced the first algorithms capable of maintaining a near-maximum (1 + ε) approximate cardinality matching with O(1) update time during edge insertions. Furthermore, we showed that a data-oblivious random projection onto a smaller number of dimensions can approximate the cost of all k-means clusterings within a (1 ± ε) factor for large datasets in Euclidean space.
Submodular optimization is a critical component of many machine learning algorithms. We demonstrated that a simple randomized greedy approach can deliver fast, near-optimal solutions for maximizing a nonmonotone submodular function with a knapsack constraint, even in adaptive and stochastic settings. We also developed efficient algorithms for submodular optimization in dynamic, consistent, and fairness-oriented contexts.
All the results of the AMDROMA project are available on the project website:
https://sites.google.com/a/uniroma1.it/stefanoleonardi/(s’ouvre dans une nouvelle fenêtre).
