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Rui Luo

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10 papers

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AAAI Conference 2026 Conference Paper

Fast Conformal Prediction Using Conditional Interquantile Intervals

Naixin Guo
Rui Luo
Zhixin Zhou

We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interquantile ranges, transforming these estimates into compact prediction intervals while achieving approximate conditional coverage. We further propose CIR+ (Conditional Interquantile Regression with More Comparison), which enhances CIR by incorporating a width-based selection rule for interquantile intervals. This refinement yields narrower prediction intervals while maintaining comparable coverage, though at the cost of slightly increased computational time. Both methods address key limitations of existing distributional conformal prediction approaches: they handle skewed distributions more effectively than Conformalized Quantile Regression, and they achieve substantially higher computational efficiency than Conformal Histogram Regression by eliminating the need for histogram construction. Extensive experiments on synthetic and real-world datasets demonstrate that our methods optimally balance predictive accuracy and computational efficiency compared to existing approaches.

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TMLR Journal 2026 Journal Article

Game-Theoretic Defenses for Adversarially Robust Conformal Prediction

Rui Luo
Jie Bao
Suqun Cao
Chuangyin Dang
Zhixin Zhou

Adversarial attacks pose major challenges to the reliability of deep learning models in safety-critical domains such as medical imaging and autonomous driving. In such high-stakes applications, providing reliable uncertainty quantification alongside adversarial robustness becomes crucial for safe deployment. Although conformal prediction can provide certain guarantees for model performance under such conditions, unknown attacks may violate the exchangeability assumption, resulting in the loss of coverage guarantees or excessively large predictive uncertainty. To address this, we propose a synergistic framework that integrates conformal prediction with game-theoretic defense strategies by modeling the adversarial interaction as a discrete, zero-sum game between attacker and defender. Our framework yields a Nash Equilibrium defense strategy, which we prove maintains valid coverage while minimizing the worst-case prediction set size against an optimal adversary operating within the defined attack space. Experimental results on CIFAR-10, CIFAR-100, and ImageNet further demonstrate that, under Nash equilibrium, defense models within our framework achieve valid coverage and minimal prediction set size. By bridging adversarial robustness and uncertainty quantification from a game-theoretic perspective, this work provides a verifiable defense paradigm for deploying safety-critical deep learning systems, particularly when adversarial distributions are unknown or dynamically evolving but contained within a known attack space.

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AAAI Conference 2026 Conference Paper

Zero-shot Implicit Neural Manifold Representation (INMR) for Ultra-high Temporal Resolution Dynamic MRI

Jie Feng
Rui Luo
Tian Zeng
Xin Shen
Haikun Qi
Yuyao Zhang
Dong Liang
Hongjiang Wei

Capturing accurate dynamic information of moving organs is essential for functional assessment using non-invasive imaging modalities. Achieving high temporal resolution visualization of physiological processes remains a critical challenge in dynamic magnetic resonance imaging (MRI) when reconstructing from extremely limited acquisitions. We introduce an unsupervised zero-shot reconstruction framework combining Implicit Neural Representation (INR) with manifold learning, capable of reconstructing dynamic MRI data at unprecedented temporal resolutions (less than 10 ms per frame for 2D imaging, less than 400 ms per frame for 3D imaging). The framework employs learnable low-dimensional manifold vectors to autonomously capture motion in real time directly from undersampled data, and dynamically condition coordinate-based spatial representations to generate high-fidelity image sequences. Through a novel spatiotemporal coarse-to-fine (C2F) optimization strategy, our method outperforms current state-of-the-art (SOTA) techniques across multiple imaging scenarios, including cardiac, speech and dynamic-contrast-enhanced (DCE) abdominal MRI, demonstrating robust performance under challenging motion patterns and contrast dynamics. The learned manifolds additionally provide intuitive visualization of motion and contrast evolution during imaging. These advances indicate strong clinical potential for applications requiring extreme temporal resolution while maintaining both anatomical and temporal fidelity.

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EAAI Journal 2025 Journal Article

An electric vehicle sales hybrid forecasting method based on improved sentiment analysis model and secondary decomposition

Jinpei Liu
Hui Pan
Rui Luo
Huayou Chen
Zhifu Tao
Zhijing Wu

The swift proliferation of electric vehicles has triggered profound shifts in consumer behavior, emphasizing the critical role of precise sales forecasts as the cornerstone for data-driven policy and production planning by both governments and electric vehicle manufacturers. However, extant forecasting models face challenges in accurately capturing consumer sentiment conveyed by online reviews and effectively extracting multiscale features of high-frequency sequences. Therefore, an electric vehicle sales hybrid forecasting method based on BERT-Bi-LSTM (Bidirectional Encoder Representations from Transformers-Bidirectional long short-term memory) sentiment analysis and secondary decomposition is proposed. First, the BERT-Bi-LSTM model is developed to perform sentiment analysis on online reviews. The model can better capture the relationship between each word and its surrounding words in text information. Second, a secondary decomposition model is constructed to decompose multisource data series, it can extract the seasonal components of the series and high-frequency complex data features, also solve potential issues of incomplete decomposition that may arise from a single decomposition. Finally, machine learning methods are utilized for hybrid forecasting. To verify the effectiveness of the proposed model, multiple sets of comparative experiments are conducted. The empirical results indicate the proposed model has higher prediction accuracy and robustness.

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AAAI Conference 2025 Conference Paper

Conformal Thresholded Intervals for Efficient Regression

Rui Luo
Zhixin Zhou

This paper introduces Conformal Thresholded Intervals (CTI), a novel conformal regression method that aims to produce the smallest possible prediction set with guaranteed coverage. Unlike existing methods that rely on nested conformal frameworks and full conditional distribution estimation, CTI estimates the conditional probability density for a new response to fall into each interquantile interval using off-the-shelf multi-output quantile regression. By leveraging the inverse relationship between interval length and probability density, CTI constructs prediction sets by thresholding the estimated conditional interquantile intervals based on their length. The optimal threshold is determined using a calibration set to ensure marginal coverage, effectively balancing the trade-off between prediction set size and coverage. CTI's approach is computationally efficient and avoids the complexity of estimating the full conditional distribution. The method is theoretically grounded, with provable guarantees for marginal coverage and achieving the smallest prediction size given by Neyman-Pearson. Extensive experimental results demonstrate that CTI achieves superior performance compared to state-of-the-art conformal regression methods across various datasets, consistently producing smaller prediction sets while maintaining the desired coverage level. The proposed method offers a simple yet effective solution for reliable uncertainty quantification in regression tasks, making it an attractive choice for practitioners seeking accurate and efficient conformal prediction.

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AAAI Conference 2025 Conference Paper

Conformalized Interval Arithmetic with Symmetric Calibration

Rui Luo
Zhixin Zhou

Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-free prediction sets with valid coverage guarantees, it traditionally focuses on single predictions. This paper introduces novel conformal prediction methods for estimating the sum or average of unknown labels over specific index sets. We develop conformal prediction intervals for single target to the prediction interval for sum of multiple targets. Under permutation invariant assumptions, we prove the validity of our proposed method. We also apply our algorithms on class average estimation and path cost prediction tasks, and we show that our method outperforms existing conformalized approaches as well as non-conformal approaches.

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AAAI Conference 2025 Conference Paper

Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training

Ting Wang
Zhixin Zhou
Rui Luo

Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their application in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed uncertainty estimates by using the classifier's probability estimates to obtain prediction sets, which contains the true class with a user-specified probability. In this paper, we propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the trustworthiness of GNNs in the node classification scenario. By exploiting rank information of the classifier's outcome, prediction sets with desired coverage rate can be efficiently constructed. The strategy of CP during training with differentiable rank-based conformity loss function is further explored to adapt prediction sets according to network topology information. In this way, the composition of prediction sets can be guided by the goal of jointly reducing inefficiency and probability estimation errors. Extensive experiments on several real-world datasets show that our model achieves any pre-defined target marginal coverage while significantly reducing the inefficiency compared with state-of-the-art methods.

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NeurIPS Conference 2020 Conference Paper

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets

Rui Luo
Qiang Zhang
Yaodong Yang
Jun Wang

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise. This is achieved by simulating a collection of replicas in parallel with different temperatures and periodically swapping them. When evolving the replicas' states, the Nos\'e-Hoover dynamics is applied, which adaptively neutralizes the mini-batch noise. To perform proper exchanges, a new protocol is developed with a noise-aware test of acceptance, by which the detailed balance is reserved in an asymptotic way. While its efficacy on complex multimodal posteriors has been illustrated by testing over synthetic distributions, experiments with deep Bayesian neural networks on large-scale datasets have shown its significant improvements over strong baselines.

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AAAI Conference 2018 Conference Paper

A Neural Stochastic Volatility Model

Rui Luo
Weinan Zhang
Xiaojun Xu
Jun Wang

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in ﬁnance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e. g. , deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based stochvol as well as the Gaussian-processbased, on average negative log-likelihood.

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NeurIPS Conference 2018 Conference Paper

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

Rui Luo
Jianhong Wang
Yaodong Yang
Jun Wang
Zhanxing Zhu

In this paper, we propose a novel sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for the purpose of multimodal Bayesian learning. It simulates a noisy dynamical system by incorporating both a continuously-varying tempering variable and the Nos\'e-Hoover thermostats. A significant benefit is that it is not only able to efficiently generate i. i. d. samples when the underlying posterior distributions are multimodal, but also capable of adaptively neutralising the noise arising from the use of mini-batches. While the properties of the approach have been studied using synthetic datasets, our experiments on three real datasets have also shown its performance gains over several strong baselines for Bayesian learning with various types of neural networks plunged in.

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