Search Results for author: Yoram Bachrach

Found 30 papers, 7 papers with code

States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers

1 code implementation24 Jan 2024 Ian Gemp, Yoram Bachrach, Marc Lanctot, Roma Patel, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, SiQi Liu, Karl Tuyls

A suitable model of the players, strategies, and payoffs associated with linguistic interactions (i. e., a binding to the conventional symbolic logic of game theory) would enable existing game-theoretic algorithms to provide strategic solutions in the space of language.

Imitation Learning

Evaluating Agents using Social Choice Theory

1 code implementation5 Dec 2023 Marc Lanctot, Kate Larson, Yoram Bachrach, Luke Marris, Zun Li, Avishkar Bhoopchand, Thomas Anthony, Brian Tanner, Anna Koop

We argue that many general evaluation problems can be viewed through the lens of voting theory.

Using Cooperative Game Theory to Prune Neural Networks

no code implementations17 Nov 2023 Mauricio Diaz-Ortiz Jr, Benjamin Kempinski, Daphne Cornelisse, Yoram Bachrach, Tal Kachman

We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks.

Explainability Techniques for Chemical Language Models

1 code implementation25 May 2023 Stefan Hödl, William Robinson, Yoram Bachrach, Wilhelm Huck, Tal Kachman

Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models.

Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples

1 code implementation NeurIPS 2023 Marco Jiralerspong, Avishek Joey Bose, Ian Gemp, Chongli Qin, Yoram Bachrach, Gauthier Gidel

The past few years have seen impressive progress in the development of deep generative models capable of producing high-dimensional, complex, and photo-realistic data.

Density Estimation

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

no code implementations22 Sep 2022 Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.

reinforcement-learning Reinforcement Learning (RL)

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

no code implementations18 Aug 2022 Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, Tal Kachman

Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team.

Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering

no code implementations29 Jul 2022 Elise van der Pol, Ian Gemp, Yoram Bachrach, Richard Everett

A core step of spectral clustering is performing an eigendecomposition of the corresponding graph Laplacian matrix (or equivalently, a singular value decomposition, SVD, of the incidence matrix).

Clustering Decision Making +3

Role of Human-AI Interaction in Selective Prediction

1 code implementation13 Dec 2021 Elizabeth Bondi, Raphael Koster, Hannah Sheahan, Martin Chadwick, Yoram Bachrach, Taylan Cemgil, Ulrich Paquet, Krishnamurthy Dvijotham

Using real-world conservation data and a selective prediction system that improves expected accuracy over that of the human or AI system working individually, we show that this messaging has a significant impact on the accuracy of human judgements.

Statistical discrimination in learning agents

no code implementations21 Oct 2021 Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo

Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.

Decision Making Multi-agent Reinforcement Learning

Open Problems in Cooperative AI

no code implementations15 Dec 2020 Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel

We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.


A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets

no code implementations14 Feb 2020 Gauthier Gidel, David Balduzzi, Wojciech Marian Czarnecki, Marta Garnelo, Yoram Bachrach

Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in reinforcement learning which have been applied to complex games such as Go or Poker.

Starcraft Starcraft II

Biases for Emergent Communication in Multi-agent Reinforcement Learning

no code implementations NeurIPS 2019 Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks.

Multi-agent Reinforcement Learning reinforcement-learning +1

Neural Design of Contests and All-Pay Auctions using Multi-Agent Simulation

no code implementations25 Sep 2019 Thomas Anthony, Ian Gemp, Janos Kramar, Tom Eccles, Andrea Tacchetti, Yoram Bachrach

In contrast to auctions designed manually by economists, our method searches the possible design space using a simulation of the multi-agent learning process, and can thus handle settings where a game-theoretic equilibrium analysis is not tractable.

Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution

no code implementations25 Sep 2019 Yoram Bachrach, Tor Lattimore, Marta Garnelo, Julien Perolat, David Balduzzi, Thomas Anthony, Satinder Singh, Thore Graepel

We show that MARL converges to the desired outcome if the rewards are designed so that exerting effort is the iterated dominance solution, but fails if it is merely a Nash equilibrium.

reinforcement-learning Reinforcement Learning (RL)

Learning Truthful, Efficient, and Welfare Maximizing Auction Rules

no code implementations11 Jul 2019 Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach

From social networks to supply chains, more and more aspects of how humans, firms and organizations interact is mediated by artificial learning agents.

Open-ended Learning in Symmetric Zero-sum Games

no code implementations23 Jan 2019 David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.

Neural Clustering By Predicting And Copying Noise

no code implementations ICLR 2018 Sam Coope, Andrej Zukov-Gregoric, Yoram Bachrach

We propose a neural clustering model that jointly learns both latent features and how they cluster.


An Attention Mechanism for Answer Selection Using a Combined Global and Local View

no code implementations5 Jul 2017 Yoram Bachrach, Andrej Zukov-Gregoric, Sam Coope, Ed Tovell, Bogdan Maksak, Jose Rodriguez, Conan McMurtie

We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input.

Answer Selection

Batch Policy Gradient Methods for Improving Neural Conversation Models

no code implementations10 Feb 2017 Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter

We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain.

Chatbot Policy Gradient Methods +2

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