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.
SOLBP extends efficient inference to uncertain Bayesian networks.
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths …
Bayesian attention improves model performance and robustness.
New method learns priors for Bayesian neural networks from datasets.
Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single…
Paper compares hard and soft EM for BN learning from incomplete data.
When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observ…
This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-co…
Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.
This work investigates the case of a network of agents that attempt to learn some unknown state of the world amongst the finitely many possibilities. At each time step, agents all receive random, independently distributed private signals whose distributions are dependent on the unknown state of the world. However, it m…
NBF combines deep learning with classical filtering for better belief tracking.
Asynchronous cooperative learning rules ensure all agents converge to correct hypothesis.
Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural netw…
Bayesian classification improves with explicit aleatoric uncertainty.
ColaBO accelerates optimization with user beliefs.
Recent reports have described that learning Bayesian networks are highly sensitive to the chosen equivalent sample size (ESS) in the Bayesian Dirichlet equivalence uniform (BDeu). This sensitivity often engenders some unstable or undesirable results. This paper describes some asymptotic analyses of BDeu to explain the …
POLAR learns efficient data acquisition policies using pretrained belief representations.
This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function. Similar to probabilistic filtering, the model maintains a Gaussian belief over the optimal weight parameters. Unlike traditional Bayesian updates, the m…
A neural network derived from first principles using MaxEnt.
Optimal algorithms identified for semi-supervised classification on graphs.
Cold posteriors improve Bayesian neural networks by reducing overestimation of aleatoric uncertainty.
GraphBSI generates graphs by refining a belief in continuous space, outperforming existing models.
Generalizes information theory to evolving belief.
Bayesian principles improve agentic AI decision-making.
In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tractable, in the sense that the posterior is also decomposable and can be completely determined analytically in polynomial time. This follows from…
Two EP frameworks ensure integrable beliefs in Bayesian estimation problems.
We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations …
Unified deep learning from noisy crowds using BP and MF.
Bayesian priors for neural networks are improved by incorporating weight correlations and tail behavior.
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data- that is, in the presence of missing values or hidden variables. In a recent paper, I introduced an algorithm…
Bayesian optimization guided by experimenter intuition and beliefs.
Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper intro…
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk …
This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms a…
The paper tackles approximate unlearning from a subset of training data using variational inference.
We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independ…
We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports pure exploration, exploration--exploitation, and two-phase bandit problems. The knowledge on bandit arms and their reward distributions is su…
We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework. The nodes take a Bayesian-like approach via the introduction of a belief over the model parameter space. We propose a distributed learning algorithm in which nodes update their belief by aggregate in…
BNNpriors library improves Bayesian neural network inference with various prior distributions.
New framework analyzes belief evolution in social networks.
πBO augments BO with user beliefs for better hyperparameter optimization.
Iterative Proportional Fitting (IPF), combined with EM, is commonly used as an algorithm for likelihood maximization in undirected graphical models. In this paper, we present two iterative algorithms that generalize upon IPF. The first one is for likelihood maximization in discrete chain factor graphs, which we define …
Adapts BP-based algorithms for deep learning, improving performance and accuracy.
RS-NN predicts belief functions for classification, improving accuracy and uncertainty estimation.
Novel technique reduces Bayesian network complexity while preserving inference accuracy.
Bayesian neural networks use temperature adjustments to improve predictive performance.
New method optimises worst-case risk under model uncertainty.