Algorithms for Distribution Matching

Algorithms for Distribution Matching - mediaTUM

The proposed algorithmic solutions use enumerative coding and shell mapping to index the optimal code books. We next consider constant composition distribution ...

Distribution Matching | David I. Inouye

Distribution matching (DM, also known as distribution alignment or domain-invariant representation learning) has the opposite objective of classification.

How Does Distribution Matching Help Domain Generalization - arXiv

While gradient or representation matching algorithms have achieved remarkable success, these methods generally lack generalization guarantees or ...

YBZh/EFDM: Official PyTorch codes of CVPR2022 Oral - GitHub

... distribution matching methods usually match the mean and standard deviation of features. However, the feature distributions of real-world data are usually ...

How Does Distribution Matching Help Domain Generalization - arXiv

While gradient or representation matching algorithms have achieved remarkable success, these methods generally lack generalization guarantees or depend on ...

Distribution-Matching Embedding for Visual Domain Adaptation

While these methods have proven effective in practice, they suffer from the fact that they do not explicitly try to match the probability distributions of the ...

Colloquium - David Inouye, "Towards Trustworthy Machine Learning ...

Abstract: Distribution matching (DM) has emerged as a cornerstone of trustworthy machine learning, finding application in fairness, robustness, causality, ...

Decentralized Multi-Agent Reinforcement Learning via Distribution ...

Third, each agent learns via a single- agent imitation learning algorithm such that it improves its distribution matching reward at each step. Next, we ...

Probabilistic Shaping for Finite Blocklengths: Distribution Matching ...

In this paper, we provide a systematic comparison of distribution matching (DM) and sphere shaping (SpSh) algorithms for short blocklength probabilistic ...

Distribution Matching for Transduction - NIPS papers

Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of ...

A State-Distribution Matching Approach to Non-Episodic ...

While reinforcement learning (RL) provides a framework for learning through trial and error, translating RL algorithms into the real world has.

Improved Distribution Matching for Dataset Condensation

Illustration of optimization-oriented methods and distri- bution matching methods. L: classification loss; g: gradient; O: output of models; Lmatching: the ...

Distribution Matching for Crowd Counting

Crowd counting methods can be divided into three categories: detection-then-count, direct count regression, and density map estimation. Early methods [22, 19, ...

Imitation learning by state-only distribution matching

Imitation learning (IL) describes methods that learn optimal behavior that is represented by a collection of expert demonstrations. In standard ...

Distribution Matching via Multiplication-free Arithmetic Coding

AC is a source coding algorithm that represents nonuniform source sequences by subintervals of the unit interval [12, Sec. 6.2]. Since DM is the ...

Multi-Level Distribution Matching | IEEE Journals & Magazine

Abstract: A general distribution matching architecture based on a multi-level structure is presented. It allows to generate arbitrary symbol ...

Huffman-coded Sphere Shaping and Distribution Matching ...

The arithmetic coding (AC) algorithm used for CCDM is an inherently sequential method [24]. The combination of long blocks required for low loss ...

Mapping structure of distribution matching with parallel amplitudes...

A distribution matcher (DM) maps a binary input sequence into a block of nonuniformly distributed symbols. To facilitate the implementation of shaped ...

Transformed Distribution Matching for Missing Value Imputation

Our algorithm has fewer hyperparameters to fine-tune and generates high-quality imputations regardless of how missing values are generated. Extensive.

Distribution Matching for Probabilistic Shaping and Stealth ... - TSpace

Abstract (summary): Distribution matching refers to the reversible approximation of non-uniform sources using a uniform memoryless source.