Yoram Bachrach | DeepAI

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Mateusz Malinowski
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∙ 05/25/2023

Explainability Techniques for Chemical Language Models

Explainability techniques are crucial in gaining insights into the reaso...

0 Stefan Hödl, et al. ∙

∙ 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...

0 Zun Li, et al. ∙

∙ 09/22/2022

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

The Game Theory Multi-Agent team at DeepMind studies several aspects...

3 Ian Gemp, et al. ∙

∙ 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...

0 Daphne Cornelisse, et al. ∙

∙ 07/29/2022

Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering

Large graphs commonly appear in social networks, knowledge graphs, recom...

0 Elise van der Pol, et al. ∙

∙ 12/13/2021

Role of Human-AI Interaction in Selective Prediction

Recent work has shown the potential benefit of selective prediction syst...

22 Elizabeth Bondi, et al. ∙

∙ 10/21/2021

Statistical discrimination in learning agents

Undesired bias afflicts both human and algorithmic decision making, and ...

0 Edgar A. Duéñez-Guzmán, et al. ∙

∙ 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...

0 Ian Gemp, et al. ∙

∙ 12/15/2020

Open Problems in Cooperative AI

Problems of cooperation–in which agents seek ways to jointly improve the...

0 Allan Dafoe, et al. ∙

∙ 10/20/2020

Negotiating Team Formation Using Deep Reinforcement Learning

When autonomous agents interact in the same environment, they must often...

0 Yoram Bachrach, et al. ∙

∙ 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...

0 Ian Gemp, et al. ∙

∙ 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...

0 Thomas Anthony, et al. ∙

∙ 02/27/2020

Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games

Zero-sum games have long guided artificial intelligence research, since ...

0 Edward Hughes, et al. ∙

∙ 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...

0 Gauthier Gidel, et al. ∙

∙ 12/11/2019

Biases for Emergent Communication in Multi-agent Reinforcement Learning

We study the problem of emergent communication, in which language arises...

0 Tom Eccles, et al. ∙

∙ 07/11/2019

A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences ...

0 Andrea Tacchetti, et al. ∙

∙ 01/23/2019

Open-ended Learning in Symmetric Zero-sum Games

Zero-sum games such as chess and poker are, abstractly, functions that e...

16 David Balduzzi, et al. ∙

∙ 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...

0 Yoram Bachrach, et al. ∙

∙ 02/14/2017

Agent Failures in All-Pay Auctions

All-pay auctions, a common mechanism for various human and agent interac...

0 Yoad Lewenberg, et al. ∙

∙ 02/10/2017

Batch Policy Gradient Methods for Improving Neural Conversation Models

We study reinforcement learning of chatbots with recurrent neural networ...

0 Kirthevasan Kandasamy, et al. ∙

∙ 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...

0 Yoad Lewenberg, et al. ∙

∙ 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...

0 Yoram Bachrach, et al. ∙

∙ 02/14/2012

Solving Cooperative Reliability Games

Cooperative games model the allocation of profit from joint actions, fol...

0 Yoram Bachrach, et al. ∙

∙ 07/24/2009

The Cost of Stability in Coalitional Games

A key question in cooperative game theory is that of coalitional stabili...

0 Yoram Bachrach, et al. ∙

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