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Found 23 papers, 7 papers with code
States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers
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.
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
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.
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Multiagent reinforcement learning (MARL) has benefited significantly from population-based and game-theoretic training regimes.
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process
Reinforcement learning has recently been used to approach well-known NP-hard combinatorial problems in graph theory.
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
We argue that such a network is a powerful component for many possible multiagent algorithms.
Game Theoretic Rating in N-player general-sum games with Equilibria
Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
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.
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering
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).
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium
In this work, we develop a game-theoretic formulation of the top-$k$ SGEP whose Nash equilibrium is the set of generalized eigenvectors.
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index
We propose a vocabulary selection method that views words as members of a team trying to maximize the model{'}s performance.
EigenGame Unloaded: When playing games is better than optimizing
We build on the recently proposed EigenGame that views eigendecomposition as a competitive game.
EigenGame: PCA as a Nash Equilibrium
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function.
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms.
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference learning algorithms.
Social diversity and social preferences in mixed-motive reinforcement learning
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity.
Neural Design of Contests and All-Pay Auctions using Multi-Agent Simulation
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.
WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS
We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective.
Global Convergence to the Equilibrium of GANs using Variational Inequalities
In optimization, the negative gradient of a function denotes the direction of steepest descent.
Online Monotone Games
Algorithmic game theory (AGT) focuses on the design and analysis of algorithms for interacting agents, with interactions rigorously formalized within the framework of games.
Generative Multi-Adversarial Networks
Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game.
Ranked #67 on
Image Generation
on CIFAR-10
(Inception score metric)
Online Monotone Optimization
This paper presents a new framework for analyzing and designing no-regret algorithms for dynamic (possibly adversarial) systems.
Inverting Variational Autoencoders for Improved Generative Accuracy
Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets ($\mathbf{x},\mathbf{y}$) to large unlabeled ones ($\mathbf{x}$).
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces
In this paper, we set forth a new vision of reinforcement learning developed by us over the past few years, one that yields mathematically rigorous solutions to longstanding important questions that have remained unresolved: (i) how to design reliable, convergent, and robust reinforcement learning algorithms (ii) how to guarantee that reinforcement learning satisfies pre-specified "safety" guarantees, and remains in a stable region of the parameter space (iii) how to design "off-policy" temporal difference learning algorithms in a reliable and stable manner, and finally (iv) how to integrate the study of reinforcement learning into the rich theory of stochastic optimization.
