arXiv research

NDI learns from expert demonstrations by estimating occupancy measures.

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

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

New PCA method detects faults using occupation kernels.

New conditions prevent gaps in optimal control problems.

Paper uses Sinkhorn distances to improve imitation learning effectiveness.

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

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

Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases. In this paper we make three contributions towards me…

In this paper, we introduce the concept of \emph{Poissonian occupation times} below level $0$ of spectrally negative Lévy processes. In this case, occupation time is accumulated only when the process is observed to be negative at arrival epochs of an independent Poisson process. Our results extend some well known conti…

DSAC improves cooperative MARL with general utilities, converging faster than existing methods.

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

FORE evaluates occupancy ratios without requiring Bellman completeness.

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.

Text corpora are widely used resources for measuring societal biases and stereotypes. The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as gender…

Paper develops multilingual job classification for ISCO and KZiS.

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.

New framework for PMD convergence in non-tabular environments.

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…

Paper proposes a fast method to detect changes in audiometric function.

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry, occupation, and location-specific knowledge carried by workers from one establishment to the ne…

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…

Building performance discrepancies between building design and operation are one of the causes that lead many new designs fail to achieve their goals and objectives. One of main factors contributing to the discrepancy is occupant behaviors. Occupants responding to a new design are influenced by several factors. Existin…

Develops a new calculus for stochastic processes with occupation flows.

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

Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference rela…

We consider the problem of robustly maximizing the growth rate of investor wealth in the presence of model uncertainty. Possible models are all those under which the assets' region $E$ and instantaneous covariation $c$ are known, and where additionally the assets are stable in that their occupancy time measures converg…

We address the problem of finding an optimal policy in a Markov decision process under a restricted policy class defined by the convex hull of a set of base policies. This problem is of great interest in applications in which a number of reasonably good (or safe) policies are already known and we are only interested in…

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…

Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \textit{learning to avoid}, an objective opposite to imitation learning …

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

Neural Index Policy for multi-action bandits with heterogeneous budgets.

D2SRM solves complex PDEs using deep learning.

DFKI Cabin Simulator tests visual monitoring functions in vehicles.

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 …

Model predicts parking area states up to 60 mins ahead.

HCNAF models complex conditional distributions for probabilistic occupancy forecasting.

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

We provide new bounds on a flux integral over the portion of the boundary of one regular domain contained inside a second regular domain, based on properties of the second domain rather than the first one. This bound is amenable to numerical computation of a flux through the boundary of a domain, for example, when ther…

MOCK learns complex systems from trajectories efficiently.

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

Algorithm speeds up search for stationary targets with guaranteed accuracy.