arXiv research

Develops an equilibrium model for securities pricing in a mixed cooperative and non-cooperative market.

Develops methods for cooperative Bayesian inference.

The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly bas…

Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise …

Multi-cell cooperative processing with limited backhaul traffic is studied for cellular uplinks. Aiming at reduced backhaul overhead, a sparsity-regularized multi-cell receive-filter design problem is formulated. Both unstructured distributed cooperation as well as clustered cooperation, in which base station groups ar…

We consider in a market model the cooperative emergence of value due to a positive feedback between perception of needs and demand. Here we consider also a negative feedback from production of the traded products, and find that this cooperativity is robust, provided that the production rate is slow. Cooperativity is fo…

Asynchronous cooperative learning rules ensure all agents converge to correct hypothesis.

Maker-taker fees can prevent algorithmic cooperation in market making, but not always.

Stable cooperation emerges in fluctuating environments.

Study shows cooperation can improve everyone's market efficiency.

The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.

We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem. For the distributed cooperative MAB problem, we design the cooperative UCB algorithm that comprises two interleaved distributed processes: (i) running consensus algorithms for estim…

Proposes a method to cluster tasks for constructive cooperative multi-tasking.

CSAC enables cooperative reinforcement learning for multi-stage tasks.

Cooperation is a persistent behavioral pattern of entities pooling and sharing resources. Its ubiquity in nature poses a conundrum. Whenever two entities cooperate, one must willingly relinquish something of value to the other. Why is this apparent altruism favored in evolution? Classical solutions assume a net fitness…

This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the ene…

We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but i…

New method attributes feature uncertainty in ML models using cooperative game theory.

Facing a heavy task, any single person can only make a limited contribution and team cooperation is needed. As one enjoys the benefit of the public goods, the potential benefits of the project are not always maximized and may be partly wasted. By incorporating individual ability and project benefit into the original pu…

New algorithm learns efficiently in multi-agent settings.

New method combines heuristics and search techniques to speed up cooperative planning for autonomous vehicles.

Kernel method improves cooperative decision-making among agents.

Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Co…

Meta-CoTGAN improves adversarial text generation by preventing mode collapse.

Paper explores alternative cooperative game theory methods for machine learning feature attribution.

A new algorithm reduces regret in cooperative multi-agent bandits with heavy-tailed data.

New algorithm reduces individual regret and communication costs in cooperative bandits.

Cooperation information sharing is important to theories of human learning and has potential implications for machine learning. Prior work derived conditions for achieving optimal Cooperative Inference given strong, relatively restrictive assumptions. We relax these assumptions by demonstrating convergence for any disc…

A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…

Enhances cooperative multi-task SemCom for distributed users.

Income redistribution is the transfer of income from some individuals to others directly or indirectly by means of social mechanisms, such as taxation, public services and so on. Employing a spatial public goods game, we study the influence of income redistribution on the evolution of cooperation. Two kinds of evolutio…

I2C enables agents to learn efficient communication without redundancy.

Agents cooperate to make decisions in multi-armed bandits over a graph.

ComEx protocol reduces communication costs in cooperative bandits.

CGAs estimate team performance from data, simplifying SV computation.

A new method for combining multiple data views in supervised learning.

Analyzes gaming in federated learning systems and provides design principles.

Hierarchies of temporally decoupled policies present a promising approach for enabling structured exploration in complex long-term planning problems. To fully achieve this approach an end-to-end training paradigm is needed. However, training these multi-level policies has had limited success due to challenges arising f…

Study how communication and feedback graphs affect learning outcomes.

Communication-efficient SGD algorithms, which allow nodes to perform local updates and periodically synchronize local models, are highly effective in improving the speed and scalability of distributed SGD. However, a rigorous convergence analysis and comparative study of different communication-reduction strategies rem…

Study shows market makers can cooperate without communication.

New cooperative dynamics enhances retrieval performance in neural networks.

We propose a framework for the derivation and evaluation of distributed iterative algorithms for receiver cooperation in interference-limited wireless systems. Our approach views the processing within and collaboration between receivers as the solution to an inference problem in the probabilistic model of the whole sys…

Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representations of mutual interp…

Improved exploration in cooperative multi-agent reinforcement learning.

Unified framework for randomized exploration in cooperative MARL.

Flexible decentralized MARL framework for cooperative multi-agent learning.

Study finds root vertex in large networks with high probability.