A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New cooperative dynamics enhances retrieval performance in neural networks.
Analyzes gaming in federated learning systems and provides design principles.
The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.
We consider a system of diffusion processes that interact through their empirical mean and have a stabilizing force acting on each of them, corresponding to a bistable potential. There are three parameters that characterize the system: the strength of the intrinsic stabilization, the strength of the external random per…
This thesis analyzes MACL systems with low-regret learning algorithms for sequential decision making.
3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, l…
The paper addresses private and Byzantine-proof cooperative decision-making in multi-agent systems.
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
Paper develops a private algorithm for multi-agent learning in bandits.
Most real life systems have a random component: the multitude of endogenous and exogenous factors influencing them result in stochastic fluctuations of the parameters determining their dynamics. These empirical systems are in many cases subject to noise of multiplicative nature. The special properties of multiplicative…
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…
A UCB algorithm reduces regret in cooperative multi-agent graph bandits.
Framework for robust control in cooperative systems with uncertain common noise.
A scalable MARL algorithm using local rewards for cooperative multi-agent learning.
Unified theories for colored sl(2) knot homology.
Paper tackles delays in multi-agent reinforcement learning, improving performance.
Agents learn to communicate and solve navigation tasks efficiently.
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…
Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of coope…
Develops an equilibrium model for securities pricing in a mixed cooperative and non-cooperative market.
Unified framework for randomized exploration in cooperative MARL.
Flexible decentralized MARL framework for cooperative multi-agent learning.
Agents cooperate to make decisions in multi-armed bandits over a graph.
New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.
Stable cooperation emerges in fluctuating environments.
A MARL system improves productivity on a metallurgical pickling line.
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…
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…
CSAC enables cooperative reinforcement learning for multi-stage tasks.
In this work, we systematically investigate mean field games and mean field type control problems with multiple populations using a coupled system of forward-backward stochastic differential equations of McKean-Vlasov type stemming from Pontryagin's stochastic maximum principle. Although the same cost functions as well…
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…
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 …
Efficient algorithms for planning in cooperative multi-agent reinforcement learning with combinatorial action spaces.
The standard theory of coherent risk measures fails to consider individual institutions as part of a system which might itself experience instability and spread new sources of risk to the market participants. In compliance with an approach adopted by Shapley and Shubik (1969), this paper proposes a cooperative market g…
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…
Kernel method improves cooperative decision-making among agents.
A new approach for cooperative multi-agent reinforcement learning with limited communication, reducing the number of communication rounds.
Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolution…
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…
Bayesian network approach for efficient cooperative MARL.
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…
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agent…
Asynchronous cooperative learning rules ensure all agents converge to correct hypothesis.
We consider the problem of governing systemic risk in a banking system model. The banking system model consists in an initial value problem for a system of stochastic differential equations whose dependent variables are the log-monetary reserves of the banks as functions of time. The banking system model considered gen…
Study shows cooperation can improve everyone's market efficiency.
We study the dynamics of exchange value in a system composed of many interacting agents. The simple model we propose exhibits cooperative emergence and collapse of global value for individual goods. We demonstrate that the demand that drives the value exhibits non Gaussian "fat tails" and typical fluctuations which gro…