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Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-11-16 Jiyue Huang , Rania Talbi , Zilong Zhao , Sara Boucchenak , Lydia Y. Chen , Stefanie Roos

Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant…

Software Engineering · Computer Science 2023-06-22 Giacomo Welsch , Peter Kowalczyk

The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individual desires a positive classification---including many…

Computer Science and Game Theory · Computer Science 2019-10-10 Lydia T. Liu , Ashia Wilson , Nika Haghtalab , Adam Tauman Kalai , Christian Borgs , Jennifer Chayes

We explore the behaviour emerging from learning agents repeatedly interacting strategically for a wide range of learning dynamics, including $Q$-learning, projected gradient, replicator and log-barrier dynamics. Going beyond the better…

Computer Science and Game Theory · Computer Science 2026-03-04 Galit Askenazi-Golan , Domenico Mergoni Cecchelli , Edward Plumb , Clemens Possnig

Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating reinforcement-learning systems that can build abstractions…

Machine Learning · Computer Science 2022-11-08 Lucas Lehnert , Michael J. Frank , Michael L. Littman

Competitive non-cooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications. To ensure sustainable resource behavior, we introduce a novel…

Optimization and Control · Mathematics 2020-10-22 Ezra Tampubolon , Holger Boche

Algorithmic collusion has emerged as a central question in AI: Will the interaction between different AI agents deployed in markets lead to collusion? More generally, understanding how emergent behavior, be it a cartel or market dominance…

Multiagent Systems · Computer Science 2025-10-31 Ziyi Wang , Carmine Ventre , Maria Polukarov

Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative…

Machine Learning · Computer Science 2024-05-24 Qian Shao , Pradeep Varakantham , Shih-Fen Cheng

When learning is used to inform decisions about humans, such as for loans, hiring, or admissions, this can incentivize users to strategically modify their features, at a cost, to obtain positive predictions. The common assumption is that…

Machine Learning · Computer Science 2025-08-15 Yonatan Sommer , Ivri Hikri , Lotan Amit , Nir Rosenfeld

We study whether large language models acting as autonomous bidders can tacitly collude by coordinating when to accept platform posted payouts in repeated Dutch auctions, without any communication. We present a minimal repeated auction…

Computer Science and Game Theory · Computer Science 2025-12-01 Sriram Tolety

Recently, there has been a surge of interest in analyzing the non-asymptotic behavior of model-free reinforcement learning algorithms. However, the performance of such algorithms in non-ideal environments, such as in the presence of…

Machine Learning · Computer Science 2024-09-06 Sreejeet Maity , Aritra Mitra

Constrained reinforcement learning has achieved promising progress in safety-critical fields where both rewards and constraints are considered. However, constrained reinforcement learning methods face challenges in striking the right…

Machine Learning · Computer Science 2024-10-29 Jianmina Ma , Jingtian Ji , Yue Gao

Motivated by online advertising auctions, we study auction design in repeated auctions played by simple Artificial Intelligence algorithms (Q-learning). We find that first-price auctions with no additional feedback lead to tacit-collusive…

Theoretical Economics · Economics 2022-02-15 Martino Banchio , Andrzej Skrzypacz

Deep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. With deep reinforcement learning being an active area…

Machine Learning · Computer Science 2018-10-17 Winfried Lötzsch

Alignment in artificial intelligence pursues the consistency between model responses and human preferences as well as values. In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment…

Computation and Language · Computer Science 2024-10-14 Yiju Guo , Ganqu Cui , Lifan Yuan , Ning Ding , Zexu Sun , Bowen Sun , Huimin Chen , Ruobing Xie , Jie Zhou , Yankai Lin , Zhiyuan Liu , Maosong Sun

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an…

Machine Learning · Computer Science 2021-01-01 Joshua Lee , Yuheng Bu , Prasanna Sattigeri , Rameswar Panda , Gregory Wornell , Leonid Karlinsky , Rogerio Feris

A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels…

Reinforcement learning has achieved great success in many decision-making tasks, and traditional reinforcement learning algorithms are mainly designed for obtaining a single optimal solution. However, recent works show the importance of…

Machine Learning · Computer Science 2023-08-24 Fanqi Lin , Shiyu Huang , Weiwei Tu

We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another…

Machine Learning · Computer Science 2017-08-08 Shahin Jabbari , Matthew Joseph , Michael Kearns , Jamie Morgenstern , Aaron Roth

Competitive analysis of online algorithms has commonly been applied to understand the behaviour of real-time systems during overload conditions. While competitive analysis provides insight into the behaviour of certain algorithms, it is…

Performance · Computer Science 2018-06-06 Sathish Gopalakrishnan