arXiv research

Paper tackles non-uniform coverage planning for robots.

Enhanced route planning with probabilistic prediction and uncertainty sets.

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

Study improves traffic prediction intervals for minor roads.

New $L_1$-Coverage objective simplifies exploration in reinforcement learning.

CONTINA provides adaptive confidence intervals for traffic demand prediction.

Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the informa…

New algorithm learns optimal policies in strategic MDPs with private types.

Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.

Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the169 MHz band. Unluckily, software tools commonly adopted for radio planning in cellular communication systems cannot be employed to solve this problem because of the substantially lower transmissi…

SPLICE generates accurate time-series imputations with reliable prediction intervals.

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…

CSP improves time-series forecasting without training, outperforming DeepNPTS in speed and accuracy.

Robust forecast framework reduces distribution error by 63%.

New RL method explores environments without rewards, achieving efficient policy generation.

New method uses label-weighted conformal prediction for macro-coverage guarantees in classification.

Algorithm balances learning and coverage for multi-robots over unknown fields.

Research uses CPS to estimate uncertainty in ML radio metric models.

Improved conformal prediction for better conditional coverage of classifier predictions.

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose AdaSearch, a general algor…

Adaptive coverage policies improve conformal prediction accuracy.

New metrics improve understanding of predictive system reliability.

Kandinsky conformal prediction expands conditional coverage guarantees.

ST-BCP narrows the coverage gap in BCP by transforming nonconformity scores.

Improves conditional coverage of regression models using conformal prediction.

New method designs fairer transport plans with uncertainty.

Improves RL planning by proposing sub-goals hierarchically.

This study uses AI to analyze financial market coverage from YouTube videos.

Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditional coverage, that is, coverage for every new testing point. However, without strong assumptions, conditional coverage is unachievable. Giv…

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then trans…

Study integrates reliability constraints into generation planning models.

TQA improves prediction intervals for time series data by adjusting quantiles for both cross-sectional and longitudinal coverage.

New conformal prediction methods for long-tailed classification problems.

New coverage conditions improve sample efficiency in online reinforcement learning.

New approach improves black-box planning efficiency by discovering focused macros.

We introduce the concept of coverage risk as an error measure for density ridge estimation. The coverage risk generalizes the mean integrated square error to set estimation. We propose two risk estimators for the coverage risk and we show that we can select tuning parameters by minimizing the estimated risk. We study t…

Study robustness of split conformal prediction under adversarial attacks.

Study motion planning for points avoiding obstacles in a plane.

STOIC improves energy demand forecasting with reliable uncertainty estimates.

Selective planning with imperfect models reduces harmful effects of model inadequacy.

CoMPNetX uses neural networks to efficiently solve constrained motion planning problems.

New method improves conditional coverage of conformal prediction.

This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and…

Audited Conformal Prediction improves conditional coverage in pretrained models under distribution shift.

Pre-training improves model coverage, crucial for downstream performance.

Paper tightens optimization bounds using conformal prediction.

Adapts conformal prediction for missing data, ensuring valid coverage.