19 citations · 49 across the 16 of their papers we have counts for
16 papers
Robust Temporal Guarantees in Budgeted Sequential Auctions
Giannis Fikioris, Robert Kleinberg, Yoav Kolumbus +2
In modern advertising platforms, learning algorithms are deployed by budget-constrained bidders to maximize their accumulated value. These algorithms often offer classical utility…
Learning in Strategic Queuing Systems with Small Buffers
Ariana Abel, Yoav Kolumbus, Jeronimo Martin Duque +2
We consider learning outcomes in games with carryover effects between rounds: when outcomes in the present round affect the game in the future. An important example of such systems…
Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners
David Easley, Yoav Kolumbus, Eva Tardos
We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian learners and no-regret learners who co…
Learning in Budgeted Auctions with Spacing Objectives
Giannis Fikioris, Robert Kleinberg, Yoav Kolumbus +3
In many repeated auction settings, participants care not only about how frequently they win but also how their winnings are distributed over time. This problem arises in various pr…
Games with Payments between Learning Agents
Yoav Kolumbus, Joe Halpern, Éva Tardos
In repeated games, such as auctions, players rely on autonomous learning agents to choose their actions. We study settings in which players have their agents make monetary transfer…
Contracting with a Learning Agent
Guru Guruganesh, Yoav Kolumbus, Jon Schneider +4
Many real-life contractual relations differ completely from the clean, static model at the heart of principal-agent theory. Typically, they involve repeated strategic interactions…
Game Manipulators -- the Strategic Implications of Binding Contracts
Maria Alejandra Ramirez, Yoav Kolumbus, Rosemarie Nagel +2
Commitment devices are powerful tools that can influence and incentivise certain behaviours by linking them to rewards or punishments. These devices are particularly useful in deci…
Asynchronous Proportional Response Dynamics in Markets with Adversarial Scheduling
Yoav Kolumbus, Menahem Levy, Noam Nisan
We study Proportional Response Dynamics (PRD) in linear Fisher markets where participants act asynchronously. We model this scenario as a sequential process in which in every step,…
Explainable Reinforcement Learning via Model Transforms
Mira Finkelstein, Lucy Liu, Nitsan Levy Schlot +4
Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision ma…
How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents
Yoav Kolumbus, Noam Nisan
The usage of automated learning agents is becoming increasingly prevalent in many online economic applications such as online auctions and automated trading. Motivated by such appl…
Auctions Between Regret-Minimizing Agents
Yoav Kolumbus, Noam Nisan
We analyze a scenario in which software agents implemented as regret-minimizing algorithms engage in a repeated auction on behalf of their users. We study first-price and second-pr…
Optimal Collaterals in Multi-Enterprise Investment Networks
Moshe Babaioff, Yoav Kolumbus, Eyal Winter
We study a market of investments on networks, where each agent (vertex) can invest in any enterprise linked to her, and at the same time, raise capital for her firm's enterprise fr…
On the Effectiveness of Tracking and Testing in SEIR Models
Yoav Kolumbus, Noam Nisan
We study the effectiveness of tracking and testing in mitigating or suppressing epidemic outbreaks, in combination with or as an alternative to quarantines and global lockdowns. We…
Neural Networks for Predicting Human Interactions in Repeated Games
Yoav Kolumbus, Gali Noti
We consider the problem of predicting human players' actions in repeated strategic interactions. Our goal is to predict the dynamic step-by-step behavior of individual players in p…
Phase Transition in the Maximal Influence Problem: When Do We Need Optimization?
Yoav Kolumbus, Sorin Solomon
Considerable efforts were made in recent years in devising optimization algorithms for influence maximization in networks. Here we ask: "When do we need optimization?" We use resul…
Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making
Gali Noti, Effi Levi, Yoav Kolumbus +1
A large body of work in behavioral fields attempts to develop models that describe the way people, as opposed to rational agents, make decisions. A recent Choice Prediction Competi…