Clustering
Decomposing Novel into Known: Part Concept Learning For 3D Novel Class Discovery
The key challenge of 3D NCD is that learned features by known class recognition are heavily biased and hinder generalization to novel classes.
Evaluating the Moral Beliefs Encoded in LLMs
This paper presents a case study on the design, administration, post-processing, and evaluation of surveys on large language models (LLMs). It comprises two components: (1) A statistical method for eliciting beliefs encoded in LLMs.
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
Differentiable Random Partition Models
Partitioning a set of elements into an unknown number of mutually exclusive subsets is essential in many machine learning problems.
Algorithm 1: GNNs with the CIT mechanismInput: Graph G = (A, X), label Y Params: the probability of transfer p, the epochtimes k, the number of clusters m, total iterations T Initialize: GNN model f
Randomly sample n p nodes to calculate Eq. (9) The nodes represent papers and are classified into three classes. The edges represent their citation relationships. Node attributes are representations of the papers. The edges are citation links. It consists of nearly twenty thousand nodes.
DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
Jaramillo-Civill, Mariona, Wu, Peng, Closas, Pau
Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a global model and fully personalized models. However, most CFL methods require the number of clusters K to be fixed a priori, which is impractical when the latent structure is unknown. We propose DPMM-CFL, a CFL algorithm that places a Dirichlet Process (DP) prior over the distribution of cluster parameters. This enables nonparametric Bayesian inference to jointly infer both the number of clusters and client assignments, while optimizing per-cluster federated objectives. This results in a method where, at each round, federated updates and cluster inferences are coupled, as presented in this paper. The algorithm is validated on benchmark datasets under Dirichlet and class-split non-IID partitions.