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Found 33 papers, 7 papers with code
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
Our MLGym framework makes it easy to add new tasks, integrate and evaluate models or agents, generate synthetic data at scale, as well as develop new learning algorithms for training agents on AI research tasks.
Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration.
Soft Condorcet Optimization for Ranking of General Agents
This optimal ranking is the maximum likelihood estimate when evaluation data (which we view as votes) are interpreted as noisy samples from a ground truth ranking, a solution to Condorcet's original voting system criteria.
Steering Language Models with Game-Theoretic Solvers
Specifically, by modelling the players, strategies and payoffs in a "game" of dialogue, we create a binding from natural language interactions to the conventional symbolic logic of game theory.
Evaluating Agents using Social Choice Theory
We argue that many general evaluation problems can be viewed through the lens of voting theory.
Using Cooperative Game Theory to Prune Neural Networks
We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks.
TacticAI: an AI assistant for football tactics
The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC.
Explainability Techniques for Chemical Language Models
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 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.
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.
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
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
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).
Role of Human-AI Interaction in Selective Prediction
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
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.
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.
Open Problems in Cooperative AI
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.
Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals.
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.
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
A Limited-Capacity Minimax Theorem for Non-Convex 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 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.
Biases for Emergent Communication in Multi-agent Reinforcement Learning
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
+2
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.
Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution
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.
Learning Truthful, Efficient, and Welfare Maximizing Auction Rules
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
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.
Named Entity Recognition With Parallel Recurrent Neural Networks
We present a new architecture for named entity recognition.
Neural Clustering By Predicting And Copying Noise
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
We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input.
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain.
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 image of their face.
