Tootfinder

Efficient and Provably Convergent Computation of Information Bottleneck: A Semi-Relaxed Approach
Lingyi Chen, Shitong Wu, Jiachuan Ye, Huihui Wu, Wenyi Zhang, Hao Wu
https://arxiv.org/abs/2404.04862

Efficient and Provably Convergent Computation of Information Bottleneck: A Semi-Relaxed Approach
Information Bottleneck (IB) is a technique to extract information about one target random variable through another relevant random variable. This technique has garnered significant interest due to its broad applications in information theory and deep learning. Hence, there is a strong motivation to develop efficient numerical methods with high precision and theoretical convergence guarantees. In this paper, we propose a semi-relaxed IB model, where the Markov chain and transition probability co…

A Monte Carlo algorithm for spin foam intertwiners
Sebastian Steinhaus
https://arxiv.org/abs/2403.04836 https://arxiv.org/pdf/2403.04…

A Monte Carlo algorithm for spin foam intertwiners
Monte Carlo algorithms are barely considered in spin foam quantum gravity. Due to the quantum nature of spin foam amplitudes one cannot readily apply them, and the present sign problem is a threat to convergence and thus efficiency. Yet, ultimately the severity of the sign problem in spin foams is not known. In this article we propose a new probability distribution for coherent (boundary) intertwiners, which we use to define a Markov Chain Monte Carlo algorithm. We apply this algorithm to the S…

Multi-fidelity Hamiltonian Monte Carlo
Dhruv V. Patel, Jonghyun Lee, Matthew W. Farthing, Peter K. Kitanidis, Eric F. Darve
https://arxiv.org/abs/2405.05033

Multi-fidelity Hamiltonian Monte Carlo
Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian Monte Carlo (HMC) method has emerged as a state-of-the-art Markov chain Monte Carlo technique, exploiting the shape of such high-dimensional target distributions to efficiently generate samples. Despite its impressive empirical success and increasing popularity, its wide-scale adoption remains limited due to the high …

Probabilistically Informed Robot Object Search with Multiple Regions
Matthew Collins, Jared J. Beard, Nicholas Ohi, Yu Gu
https://arxiv.org/abs/2404.04186 …

Probabilistically Informed Robot Object Search with Multiple Regions
The increasing use of autonomous robot systems in hazardous environments underscores the need for efficient search and rescue operations. Despite significant advancements, existing literature on object search often falls short in overcoming the difficulty of long planning horizons and dealing with sensor limitations, such as noise. This study introduces a novel approach that formulates the search problem as a belief Markov decision processes with options (BMDP-O) to make Monte Carlo tree search…

This https://arxiv.org/abs/2301.03196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior
Multiple-input multiple-output (MIMO) systems will play a crucial role in future wireless communication, but improving their signal detection performance to increase transmission efficiency remains a challenge. To address this issue, we propose extending the discrete signal detection problem in MIMO systems to a continuous one and applying the Hamiltonian Monte Carlo method, an efficient Markov chain Monte Carlo algorithm. In our previous studies, we have used a mixture of normal distributions …

Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
https://arxiv.org/abs/2405.01737

Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable model, such as a Hidden Markov model (HMM), these methods are designed to only estimate the parameters, rather than the joint distribution of the parameters and the hidden states. Naive application of these methods to a HMM, ignoring the inference of this joint po…

Multi-fidelity Hamiltonian Monte Carlo
Dhruv V. Patel, Jonghyun Lee, Matthew W. Farthing, Peter K. Kitanidis, Eric F. Darve
https://arxiv.org/abs/2405.05033

Multi-fidelity Hamiltonian Monte Carlo
Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian Monte Carlo (HMC) method has emerged as a state-of-the-art Markov chain Monte Carlo technique, exploiting the shape of such high-dimensional target distributions to efficiently generate samples. Despite its impressive empirical success and increasing popularity, its wide-scale adoption remains limited due to the high …

Optimal Control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks
Yu Chen, Xuanyuan Yin, Shaoyuan Li, Xiang Yin
https://arxiv.org/abs/2403.18632

Optimal Control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks
We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to fulfill a qualitative surveillance task in the sense that a specific region of interest can be visited infinitely often with probability one. Furthermore, to quantify the performance of the system, we consider the concept of efficiency, which is defined as the ratio between rewards and costs. This measure is…

Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning
Qiaosheng Zhang, Chenjia Bai, Shuyue Hu, Zhen Wang, Xuelong Li
https://arxiv.org/abs/2404.19292

Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning
This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS). These algorithms draw inspiration from foundational concepts in information theory, and are proven to be sample efficient in MARL settings such as two-player zero-sum Markov games (MGs) and multi-player general-sum MGs. For episodic two-player zero-sum MGs, we present three sample-efficient algorithms for learning Nash equilibrium.…

Exposure-Conscious Path Planning for Equal-Exposure Corridors
Eugene T. Hamzezadeh, John G. Rogers, Neil T. Dantam, Andrew J. Petruska
https://arxiv.org/abs/2403.02450

Exposure-Conscious Path Planning for Equal-Exposure Corridors
While maximizing line-of-sight coverage of specific regions or agents in the environment is a well explored path planning objective, the converse problem of minimizing exposure to the entire environment during navigation is especially interesting in the context of minimizing detection risk. This work demonstrates that minimizing line-of-sight exposure to the environment is non-Markovian, which cannot be efficiently solved optimally with traditional path planning. The optimality gap of the graph…