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.
Wisdom of the crowd revealed a striking fact that the majority answer from a crowd is often more accurate than any individual expert. We observed the same story in machine learning--ensemble methods leverage this idea to combine multiple learning algorithms to obtain better classification performance. Among many popula…
Paper proposes government indemnification for AI risks to solve judgment-proof problem.
Poisoning datasets can reveal private details of other users' training points.
The problem of estimating event truths from conflicting agent opinions in a social network is investigated. An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations. A Bayesian network model is proposed to guide the learning process by modeling the relationship of…
Bayesian neural networks improve cancer dynamics prediction.
We investigate the problem of truth discovery based on opinions from multiple agents who may be unreliable or biased. We consider the case where agents' reliabilities or biases are correlated if they belong to the same community, which defines a group of agents with similar opinions regarding a particular event. An age…
Bayesian meta learning improves uncertainty quantification in regression.
In this article, we investigate large sample properties of model selection procedures in a general Bayesian framework when a closed form expression of the marginal likelihood function is not available or a local asymptotic quadratic approximation of the log-likelihood function does not exist. Under appropriate identifi…
A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in …
A method for inferring ground-truth signals from degraded sensor data.
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
Improved VB algorithm for high-dimensional logistic regression with theoretical guarantees.
It is generally believed that ensemble approaches, which combine multiple algorithms or models, can outperform any single algorithm at machine learning tasks, such as prediction. In this paper, we propose Bayesian convex and linear aggregation approaches motivated by regression applications. We show that the proposed a…
Paper proposes incentives for federated learning to ensure truthful contributions.
The paper proposes a semi-parametric Bayesian network model using Gaussian Processes and Horseshoe priors.
metabeta uses neural networks to speed up Bayesian mixed-effects regression.
Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual workers cannot be wholly trusted to provide reliable annotations. Research into models of annotation aggregation attempts to infer a latent `tru…
We consider a fundamental dynamic allocation problem motivated by the problem of in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource to clients, each of whom has a priv…
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction task, several formulations exist, depending on how one chooses the time of prediction and the prediction horizon. In this paper, we show how…
Bayesian algorithms improve crowdsourcing with label and instance constraints.
Paper adapts Bayesian Hui-Walter method for unlabeled data.
Monte-Carlo Tree Search (MCTS) methods are drawing great interest after yielding breakthrough results in computer Go. This paper proposes a Bayesian approach to MCTS that is inspired by distributionfree approaches such as UCT [13], yet significantly differs in important respects. The Bayesian framework allows potential…
Paper proposes efficient method for evaluating Bayesian models in imaging.
Novel framework for Bayesian neural networks incorporating task-specific constraints.
This paper considers the problem of learning the parameters in Bayesian networks of discrete variables with known structure and hidden variables. Previous approaches in these settings typically use expectation maximization; when the network has high treewidth, the required expectations might be approximated using Monte…
VBMC+VIQR outperforms noisy models in Bayesian inference.
Eye Movement analysis with Hidden Markov Models (EMHMM) is a method for modeling eye fixation sequences using hidden Markov models (HMMs). In this report, we run a simulation study to investigate the estimation error for learning HMMs with variational Bayesian inference, with respect to the number of sequences and the …
New method for hyperparameter tuning in sparse matrix factorization.
Bayesian approach scores influential training examples for model predictions.
This paper introduces NPR, a technique to improve Bayesian inference for multi-modal, high-dimensional simulations.
Bayesian framework for online consensus prediction from expert feedback.
This work embeds annotations into a multidimensional space to measure classification difficulty.
Bayesian model explains and improves black-box estimators for class distribution.
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several B…
This work develops rigorous theoretical basis for the fact that deep Bayesian neural network (BNN) is an effective tool for high-dimensional variable selection with rigorous uncertainty quantification. We develop new Bayesian non-parametric theorems to show that a properly configured deep BNN (1) learns the variable im…
nFBST tests neural networks using Bayesian methods.
New method improves ABC for Bayesian model comparison.
New truthful calibration errors improve model ranking in multiclass prediction.
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by assessing the relationship between the learnt and the ground truth graph. However, there is no agreed scoring metric to determine this relationsh…
A key challenge in crowdsourcing is inferring the ground truth from noisy and unreliable data. To do so, existing approaches rely on collecting redundant information from the crowd, and aggregating it with some probabilistic method. However, oftentimes such methods are computationally inefficient, are restricted to som…
Bayesian Topic Regression models causal inference with text and numerical data.
PClean automates Bayesian data cleaning for specific datasets.
A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while achieving comparabl…
New calibration measure SSCE ensures truthful prediction, unlike existing measures.
A new perfectly truthful calibration measure improves prediction reliability.
Bayesian deep learning accounts for input uncertainty using Errors-in-Variables models.
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models f…
Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capabil…