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05/25/2023
Explainability Techniques for Chemical Language Models
Explainability techniques are crucial in gaining insights into the reaso...
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02/01/2023
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Multiagent reinforcement learning (MARL) has benefited significantly fro...
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09/22/2022
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory Multi-Agent team at DeepMind studies several aspects...
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08/18/2022
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
In many multi-agent settings, participants can form teams to achieve col...
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07/29/2022
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering
Large graphs commonly appear in social networks, knowledge graphs, recom...
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12/13/2021
Role of Human-AI Interaction in Selective Prediction
Recent work has shown the potential benefit of selective prediction syst...
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10/21/2021
Statistical discrimination in learning agents
Undesired bias afflicts both human and algorithmic decision making, and ...
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06/02/2021
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
Nash equilibrium is a central concept in game theory. Several Nash solve...
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12/15/2020
Open Problems in Cooperative AI
Problems of cooperation–in which agents seek ways to jointly improve the...
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10/20/2020
Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often...
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10/01/2020
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
Even in simple multi-agent systems, fixed incentives can lead to outcome...
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06/08/2020
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Recent advances in deep reinforcement learning (RL) have led to consider...
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02/27/2020
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Zero-sum games have long guided artificial intelligence research, since ...
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02/14/2020
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Adversarial training, a special case of multi-objective optimization, is...
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12/11/2019
Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises...
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07/11/2019
A Neural Architecture for Designing Truthful and Efficient Auctions
Auctions are protocols to allocate goods to buyers who have preferences ...
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01/23/2019
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that e...
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07/05/2017
An Attention Mechanism for Answer Selection Using a Combined Global and Local View
We propose a new attention mechanism for neural based question answering...
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02/14/2017
Agent Failures in All-Pay Auctions
All-pay auctions, a common mechanism for various human and agent interac...
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02/10/2017
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural networ...
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05/29/2016
Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
We consider the task of predicting various traits of a person given an i...
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06/27/2012
How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
We propose a new probabilistic graphical model that jointly models the d...
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02/14/2012
Solving Cooperative Reliability Games
Cooperative games model the allocation of profit from joint actions, fol...
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07/24/2009
