Valeriy M., PhD, MBA, CQFWhether you're a researcher, practitioner, or just curious about conformal techniques, you're welcome. See you there! 🔥
#MachineLearning #ConformalPrediction #UncertaintyEstimation #AIResearch #Reddit
Whether you're a researcher, practitioner, or just curious about conformal techniques, you're welcome. See you there! 🔥
https://www.reddit.com/r/conformalprediction/hot/
#MachineLearning #ConformalPrediction #UncertaintyEstimation #AIResearch #Reddit
Ask yourself: Can I afford to fall behind in a world where precise uncertainty matters? Let’s turn “what if” into “what’s next.”
#MachineLearning #DataScience #AI #ConformalPrediction #CareerGrowth #LearnWithTheBest
Let’s build the future—confidently. 🛠️🔮
My course 'Applied Conformal Prediction' starts 12th May -> https://maven.com/valeriy-manokhin/applied-conformal-prediction
#TimeSeries #Forecasting #ConformalPrediction #MachineLearning #Python #Nixtla
* There's huge potential for compressed models + conformal prediction in real-world, resource-limited deployments.
* The future of AI is not just about prediction — it’s about trustworthy prediction.
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction
This paper presents a comprehensive empirical analysis of conformal prediction methods on a challenging aerial image dataset featuring diverse events in unconstrained environments. Conformal prediction is a powerful post-hoc technique that takes the output of any classifier and transforms it into a set of likely labels, providing a statistical guarantee on the coverage of the true label. Unlike evaluations on standard benchmarks, our study addresses the complexities of data-scarce and highly variable real-world settings. We investigate the effectiveness of leveraging pretrained models (MobileNet, DenseNet, and ResNet), fine-tuned with limited labeled data, to generate informative prediction sets. To further evaluate the impact of calibration, we consider two parallel pipelines (with and without temperature scaling) and assess performance using two key metrics: empirical coverage and average prediction set size. This setup allows us to systematically examine how calibration choices influence the trade-off between reliability and efficiency. Our findings demonstrate that even with relatively small labeled samples and simple nonconformity scores, conformal prediction can yield valuable uncertainty estimates for complex tasks. Moreover, our analysis reveals that while temperature scaling is often employed for calibration, it does not consistently lead to smaller prediction sets, underscoring the importance of careful consideration in its application. Furthermore, our results highlight the significant potential of model compression techniques within the conformal prediction pipeline for deployment in resource-constrained environments. Based on our observations, we advocate for future research to delve into the impact of noisy or ambiguous labels on conformal prediction performance and to explore effective model reduction strategies.
It's time for the actuarial profession to evolve — or risk being left behind
Conformal prediction of future insurance claims in the regression problem
In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially suffer from several drawbacks: (i) model misspecification, (ii) selection effect, and (iii) lack of finite-sample validity. This article addresses these three issues simultaneously by employing conformal prediction -- a general machine learning strategy for valid predictions. The proposed method is both model-free and tuning-parameter-free. It also guarantees finite-sample validity at a pre-assigned coverage probability level. Examples, based on both simulated and real data, are provided to demonstrate the excellent performance of the proposed method and its applications in insurance, especially regarding meeting the solvency capital requirement of European insurance regulation, Solvency II.
Hot take: Conformal prediction isn’t just math—it’s the antidote to AI hallucinations.
👇 Agree? Disagree? Let’s debate in the comments.
#AI #MachineLearning #LLMs #ConformalPrediction #ResponsibleAI #TechInnovation