arXiv research

Prob-GNN quantifies travel demand uncertainty with deep learning.

New model predicts travel demand uncertainty with high accuracy.

Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.

The paper addresses uncertainty in demand prediction for dynamic pricing.

Study optimizes pricing under uncertainty and capacity constraints.

Paper proposes a method for predicting any quantile of short-term electricity demand.

We establish explicit socially optimal rules for an irreversible investment deci- sion with time-to-build and uncertainty. Assuming a price sensitive demand function with a random intercept, we provide comparative statics and economic interpreta- tions for three models of demand (arithmetic Brownian, geometric Brownian…

We propose and analyze numerically a simple dynamical model that describes the firm behaviors under uncertainty of demand forecast. Iterating this simple model and varying some parameters values we observe a wide variety of market dynamics such as equilibria, periodic and chaotic behaviors. Interestingly the model is a…

Dynamic pricing learns demand model from sparse product networks.

Clarifies challenges in machine learning uncertainty quantification.

New approach uses deep generative models for inventory and pricing decisions.

Study finds consumers are more price-sensitive before livestreams than after.

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …

Enhances load forecasting for multiple entities with dynamic similarities.

Study of insurer games with model uncertainty in reinsurance and investment strategies.

Paper optimizes demand aggregation for low-level electricity markets.

Study optimizes smart contract adoption under high demand variability using Negative Binomial models.

Graph Neural Networks improve demand forecasting by considering article relationships.

Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to lar…

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such cont…

Study develops smart contract framework for procurement under demand variability.

STOIC improves energy demand forecasting with reliable uncertainty estimates.

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o…

There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there are enormous benefits to a system that is not only accurate but also has a sense for when it is not sure. Existing proposals center around Ba…

Model forecasts water demand with probabilistic multi-step-ahead approach.

Select-DC reduces GFLOPS for uncertainty estimation in neural networks.

URL benchmark evaluates uncertainty quantification in pretrained models.

Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.

Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in time and geographic space. The method exhibits superior accuracy to MEDIC…

CUQ-GNN adapts uncertainty quantification for graph data, improving on GPN.

A framework for the generation of bridge-specific fragility utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and…

A new method optimizes complex engineering designs under uncertainty efficiently.

Quantized BNNs maintain uncertainty estimation quality despite reduced precision.

Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.

The M5 competition tackles overdispersed retail sales forecasting with GAMLSS.

This paper demonstrates two novel methods to estimate the global SNR of speech signals. In both methods, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic model used in speech recognition systems is leveraged for the additional task of SNR estimation. In the first method, the entropy of the DNN-HMM output is c…

Efficiently estimates uncertainty for LLM-based entity linking in tabular data.

Post-hoc uncertainty quantification improves on pre-trained neural networks without underfitting.

We derive the optimal investment decision in a project where both demand and investment costs are stochastic processes, eventually subject to shocks. We extend the approach used in Dixit and Pindyck (1994), chapter 6.5, to deal with two sources of uncertainty, but assuming that the underlying processes are no longer ge…

Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.

We can overcome uncertainty with uncertainty. Using randomness in our choices and in what we control, and hence in the decision making process, could potentially offset the uncertainty inherent in the environment and yield better outcomes. The example we develop in greater detail is the news-vendor inventory management…

Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next samplin…

The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…

CONTINA provides adaptive confidence intervals for traffic demand prediction.

Paper develops a new RL method for MDPs with uncertainty, achieving better regret bounds.

Deep learning model reduces food waste by stabilizing online food delivery supply chains.

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assu…

With the wide development of black-box machine learning algorithms, particularly deep neural network (DNN), the practical demand for the reliability assessment is rapidly rising. On the basis of the concept that `Bayesian deep learning knows what it does not know,' the uncertainty of DNN outputs has been investigated a…