
D3C: Reducing the Price of Anarchy in MultiAgent Learning
Even in simple multiagent systems, fixed incentives can lead to outcome...
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Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (...
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Learning to Resolve Alliance Dilemmas in ManyPlayer ZeroSum Games
Zerosum games have long guided artificial intelligence research, since ...
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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
In this paper we investigate the Follow the Regularized Leader dynamics ...
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Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about MixedNash and Love Neural Nets
Adversarial training, a special case of multiobjective optimization, is...
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Smooth markets: A basic mechanism for organizing gradientbased learners
With the success of modern machine learning, it is becoming increasingly...
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LOGAN: Latent Optimisation for Generative Adversarial Networks
Training generative adversarial networks requires balancing of delicate ...
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Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
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Openended Learning in Symmetric Zerosum Games
Zerosum games such as chess and poker are, abstractly, functions that e...
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Stable Opponent Shaping in Differentiable Games
A growing number of learning methods are actually games which optimise m...
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Reevaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
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The Mechanics of nPlayer Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
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The Shattered Gradients Problem: If resnets are the answer, then what is the question?
A longstanding obstacle to progress in deep learning is the problem of ...
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StronglyTyped Agents are Guaranteed to Interact Safely
As artificial agents proliferate, it is becoming increasingly important ...
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Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Modern convolutional networks, incorporating rectifiers and maxpooling,...
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Deep ReconstructionClassification Networks for Unsupervised Domain Adaptation
In this paper, we propose a novel unsupervised domain adaptation algorit...
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Deep Online Convex Optimization with Gated Games
Methods from convex optimization are widely used as building blocks for ...
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ComplianceAware Bandits
Motivated by clinical trials, we study bandits with observable noncompl...
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StronglyTyped Recurrent Neural Networks
Recurrent neural networks are increasing popular models for sequential l...
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Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
This paper addresses classification tasks on a particular target domain ...
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Semantics, Representations and Grammars for Deep Learning
Deep learning is currently the subject of intensive study. However, fund...
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Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies
This paper proposes GProp, a deep reinforcement learning algorithm for c...
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Deep Online Convex Optimization by Putting Forecaster to Sleep
Methods from convex optimization such as accelerated gradient descent ar...
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Domain Generalization for Object Recognition with Multitask Autoencoders
The problem of domain generalization is to take knowledge acquired from ...
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Cortical prediction markets
We investigate cortical learning from the perspective of mechanism desig...
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Correlated random features for fast semisupervised learning
This paper presents Correlated Nystrom Views (XNV), a fast semisupervis...
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Domain Generalization via Invariant Feature Representation
This paper investigates domain generalization: How to take knowledge acq...
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Towards a learningtheoretic analysis of spiketiming dependent plasticity
This paper suggests a learningtheoretic perspective on how synaptic pla...
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A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization pr...
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Falsification and future performance
We informationtheoretically reformulate two measures of capacity from s...
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Information, learning and falsification
There are (at least) three approaches to quantifying information. The fi...
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On the informationtheoretic structure of distributed measurements
The internal structure of a measuring device, which depends on what its ...
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