paper-with-me

홈 › Papers

Individually Conditional Individual Mutual Information Bound on Generalization Error

2020-12-17 · Ruida Zhou, Chao Tian, Tie Liu

We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al. and the conditional mutual information (CMI) construction of Steinke and Zakynthinou. In a previous work, Haghifam et al. proposed a different bound combining the two aforementioned techniques, which we refer to as the conditional individual mutual information (CIMI) bound. However, in a simple Gaussian setting, both the CMI and the CIMI bounds are order-wise worse than that by Bu et al.. This observation motivated us to propose the new bound, which overcomes this issue by reducing the conditioning terms in the conditional mutual information. In the process of establishing this bound, a conditional decoupling lemma is established, which also leads to a meaningful dichotomy and comparison among these information-theoretic bounds.

📄 PDF Abstract BibTeX arXiv:2012.09922

Code (0)

등록된 구현이 없습니다.

Tasks

LEMMA

Similar Papers 제목 키워드 기반

Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms

2020-04-27 · NeurIPS 2020 12 · Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy 외

The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error of a learning algorithm in terms of the mutual information between the algorithm's out…

Generalization Bounds

Individual Sensing can Gain more Fitness than its Information

2017-12-27

Mutual information and its causal variant, directed information, have been widely used to quantitatively characterize the performance of biological sensing and information transduction. However, once coupled with selecti…

Decision Making

Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective

2025-03-03 · Masoud Kavian, Romain Chor, Milad Sefidgaran, Abdellatif Zaidi

In this paper, we investigate the effect of data heterogeneity across clients on the performance of distributed learning systems, i.e., one-round Federated Learning, as measured by the associated generalization error. Sp…

Federated Learning

Conditional Mutual Information Neural Estimator

2019-11-06 · Sina Molavipour, Germán Bassi, Mikael Skoglund

Several recent works in communication systems have proposed to leverage the power of neural networks in the design of encoders and decoders. In this approach, these blocks can be tailored to maximize the transmission rat…

Understanding Generalization via Leave-One-Out Conditional Mutual Information

2022-06-29 · Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite

We study the mutual information between (certain summaries of) the output of a learning algorithm and its $n$ training data, conditional on a supersample of $n+1$ i.i.d. data from which the training data is chosen at ran…

Transductive Learning