# Generative Conformal Prediction with Optimized Coverage Allocation

arXiv: https://arxiv.org/abs/2410.13735 · HTML: https://mlsanity.com/paper/2410.13735/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2024.

## Built on by (most cited first, 9 of 9)

- [Topology-Aware Conformal Prediction for Stream Networks](https://mlsanity.com/paper/2503.04981/) (2025)
- [Conformalized Decision Risk Assessment](https://mlsanity.com/paper/2505.13243/) (2025)
- [Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption](https://mlsanity.com/paper/2411.12193/) (2024)
- [Computing High-dimensional Confidence Sets for Arbitrary Distributions](https://mlsanity.com/paper/2504.02723/) (2025)
- [Flow-Based Conformal Predictive Distributions](https://mlsanity.com/paper/2602.07633/) (2026)
- [Learning Polyhedral Conformal Sets for Robust Optimization](https://mlsanity.com/paper/2605.08506/) (2026)
- [Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set](https://mlsanity.com/paper/2609.11073/) (2026)
- [Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series](https://mlsanity.com/paper/2605.26569/) (2026)
- [Sample-Efficient Generative Conformal Prediction](https://mlsanity.com/paper/2610.11349/) (2026)

## Abstract (arXiv)

Conformal prediction provides model-agnostic uncertainty quantification with guaranteed coverage, but conventional methods often yield overly conservative uncertainty sets, particularly in multimodal or heterogeneous settings. This inefficiency arises from two sources: (i) limited expressiveness of the predictive model and (ii) simplistic nonconformity score design. Most existing approaches advance only one of these axes, leaving the other underexplored. We propose generative conformal prediction with Optimized Ranking and Coverage Allocation (ORCA), a three-stage framework that advances both aspects jointly. ORCA leverages generative models to capture the full conditional distribution and introduces a rank-dependent optimization procedure that adaptively allocates coverage for efficiency while maintaining validity. We cast this coverage allocation as an optimization problem, derive an exact mixed-integer linear programming formulation, and show that the solution converges asymptotically to the oracle density-level set. Across synthetic, semi-synthetic, and real datasets, ORCA produces substantially more efficient uncertainty sets than state-of-the-art baselines, demonstrating robust gains in scenarios where conventional conformal prediction methods fail.