arXiv research

The study evaluates different probability models for uncertainty visualization using entropy calculations.

Paper speeds up visualization of uncertain data.

Agent learns to navigate uncertain 3D maps using a hybrid planner.

Interactive steering improves hierarchical clustering for diverse user needs.

TIE framework detects out-of-distribution samples and estimates uncertainty without external datasets.

The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visua…

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

The paper tackles robust control with uncertain dependence using data-driven methods.

This paper improves learning uncertain Bayesian networks from incomplete data.

Federated learning uses worst-case optimization to handle uncertain local data impacts.

New algorithm for uncertain time series classification.

Paper introduces a new method for learning Bregman divergence from data.

Unified framework denoises data and abstains from uncertain predictions.

The paper explores how to handle uncertain evidence in probabilistic models.

New method calculates Shapley values for uncertain functions.

Paper proposes online optimization for uncertain systems using machine learning and DRO.

Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfe…

New clustering method for uncertain data using Wasserstein barycenters.

uMoE trains NNs with uncertain data by embedding uncertainty into training.

For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. Towards these capabilities, we present a probabilistic forecasting model of future interactions b…

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

We use copulas to improve SLAM in uncertain environments.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

A machine learning approach to compute Black-Scholes prices with uncertain volatility.

This paper rates robustness of multi-modal time-series forecasting models.

Proposes a spectral method to assess and combine multiple data visualizations.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

Paper introduces imprecise logistic regression for handling uncertain data.

The abstract discusses how humans use visualizations in machine learning.

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…

Study on learning strategies in matching markets with uncertain preferences.

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In …

This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any orga…

Visualizes futures markets using particle physics tools.

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

GTSNE improves data visualization for high-dimensional data.

We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets with annotations such as image class labels, bounding boxes and region descriptions are good sources for learning rich and diverse visual conce…

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

New method visualizes noisy data better than existing techniques.

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep r…

Extends partitioned local depth concept with probabilistic considerations.

Consensus dimension reduction combines multiple visualizations to identify shared patterns.

Bayesian filtering optimizes portfolio weights over time with uncertain parameters.