# Artificial Intelligence and Statistics

arXiv: https://arxiv.org/abs/1712.03779 · HTML: https://mlsanity.com/paper/1712.03779/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2017.

## Built on by (most cited first, 30 of 43)

- [StyleSwin: Transformer-based GAN for High-resolution Image Generation](https://mlsanity.com/paper/2112.10762/) (2021)
- [ALIP: Adaptive Language-Image Pre-training with Synthetic Caption](https://mlsanity.com/paper/2308.08428/) (2023)
- [FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning](https://mlsanity.com/paper/2111.08211/) (2021)
- [Learning on Arbitrary Graph Topologies via Predictive Coding](https://mlsanity.com/paper/2201.13180/) (2022)
- [Learning Causal Effects on Hypergraphs](https://mlsanity.com/paper/2207.04049/) (2022)
- [A Primer on Bayesian Neural Networks: Review and Debates](https://mlsanity.com/paper/2309.16314/) (2023)
- [Fixed-Budget Best-Arm Identification in Structured Bandits](https://mlsanity.com/paper/2106.04763/) (2021)
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://mlsanity.com/paper/2106.11593/) (2021)
- [Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization](https://mlsanity.com/paper/2404.00710/) (2024)
- [SCOTCH and SODA: A Transformer Video Shadow Detection Framework](https://mlsanity.com/paper/2211.06885/) (2022)
- [Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks](https://mlsanity.com/paper/2102.00200/) (2021)
- [Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences](https://mlsanity.com/paper/2305.05986/) (2023)
- [Causal Entropy Optimization](https://mlsanity.com/paper/2208.10981/) (2022)
- [Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks](https://mlsanity.com/paper/2110.13567/) (2021)
- [Loss Tolerant Federated Learning](https://mlsanity.com/paper/2105.03591/) (2021)
- [Boosting Adversarial Training via Fisher-Rao Norm-based Regularization](https://mlsanity.com/paper/2403.17520/) (2024)
- [CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning](https://mlsanity.com/paper/2204.01542/) (2022)
- [Game State and Spatio-temporal Action Detection in Soccer using Graph Neural Networks and 3D Convolutional Networks](https://mlsanity.com/paper/2502.15462/) (2025)
- [CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models](https://mlsanity.com/paper/2507.13984/) (2025)
- [Unbinned Profiled Unfolding](https://mlsanity.com/paper/2302.05390/) (2023)
- [Distortion-Disentangled Contrastive Learning](https://mlsanity.com/paper/2303.05066/) (2023)
- [Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons](https://mlsanity.com/paper/2305.15670/) (2023)
- [Differentially Private Federated Combinatorial Bandits with Constraints](https://mlsanity.com/paper/2206.13192/) (2022)
- [TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences](https://mlsanity.com/paper/2306.14114/) (2023)
- [InstanceV: Instance-Level Video Generation](https://mlsanity.com/paper/2511.23146/) (2025)
- [Stochastic Block-ADMM for Training Deep Networks](https://mlsanity.com/paper/2105.00339/) (2021)
- [Exchangeability-Aware Sum-Product Networks](https://mlsanity.com/paper/2110.05165/) (2021)
- [Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector](https://mlsanity.com/paper/2310.15764/) (2023)
- [A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks](https://mlsanity.com/paper/2310.20398/) (2023)
- [A Clustering-based Framework for Classifying Data Streams](https://mlsanity.com/paper/2106.11823/) (2021)

## Abstract (arXiv)

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and research. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research.