arXiv research

GANs improve building performance model accuracy by integrating occupant behaviors.

A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…

Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.

We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representation bias. To do so, we study the impact on occupation classification of…

Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards …

The paper defines and analyzes Poissonian occupation times for negative Lévy processes.

Aims to teach agents to avoid dangerous behaviors observed in experts.

NDI learns from expert demonstrations by estimating occupancy measures.

Paper analyzes AI's impact on job tasks, predicting future demands.

FORE evaluates occupancy ratios without requiring Bellman completeness.

A new method learns continuous occupancy fields efficiently using the Ising model.

In this short paper, in order to price occupation-time options, such as (double-barrier) step options and quantile options, we derive various joint distributions of a mixed-exponential jump-diffusion process and its occupation times of intervals.

New PCA method detects faults using occupation kernels.

Paper develops multilingual job classification for ISCO and KZiS.

Study measures gender bias in machine translation using multiple reference points.

In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level $0$) up to an (independent) exponential horizon for spectrally negative Lévy risk processes and refracted spectrally negative Lévy risk processes. This result improves the existing literature in which only…

Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.

A new method estimates SDEs using occupation kernels.

ROCK method generalizes MOCK for learning dynamical systems efficiently.

Spacematch matches office workers with suitable workspaces based on their preferences.

Method learns SDEs from data snapshots.

Study predicts room occupancy using machine learning, achieving high accuracy.

We find the optimal investment strategy to minimize the expected time that an individual's wealth stays below zero, the so-called {\it occupation time}. The individual consumes at a constant rate and invests in a Black-Scholes financial market consisting of one riskless and one risky asset, with the risky asset's price…

We introduce cylindrical projections to simulate infinite-dimensional occupation flows of diffusions.

Develops a new calculus for stochastic processes with occupation flows.

Investment strategies in occupational pension plans are optimized for non-tradable income risk.

The paper learns personalized thermal preferences using Bayesian active learning.

The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…

We present for the first time an asymptotic convergence analysis of two time-scale stochastic approximation driven by `controlled' Markov noise. In particular, both the faster and slower recursions have non-additive controlled Markov noise components in addition to martingale difference noise. We analyze the asymptotic…

D2SRM solves complex PDEs using deep learning.

DFKI Cabin Simulator tests visual monitoring functions in vehicles.

Method reduces bias in occupation classification without protected attribute data.

The paper develops a method to learn navigation costs from expert demonstrations in partially observable environments.

Paper uses Sinkhorn distances to improve imitation learning effectiveness.

HCNAF models complex conditional distributions for probabilistic occupancy forecasting.

New conditions prevent gaps in optimal control problems.

This work explores efficient reinforcement learning with density features in low-rank MDPs.

MOCK learns complex systems from trajectories efficiently.

Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.

A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture …

EBIL simplifies IL by estimating expert energy as reward, achieving effective performance.

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

End-to-end learnable network for safer self-driving with interpretable intermediate representations.

Paper tackles dynamic pricing in a geometrically decaying environment, achieving better occupancy with lower rates.

New offline RL algorithms tackle partial data coverage with optimal performance and practicality.

Debiasing techniques can worsen gender bias in text classification, but a tweak improves both.

Challenge forecasts EV charging station usage accurately.

Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. The…