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20212026
most citedLearning particle swarming models from data with Gaussian processes

4 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Layerwise Decoupling for Stable Structured Sparsification of Fully Connected Layers

Charles Kulick, Armenak Petrosyan, Sui Tang

We propose a decoupled, layerwise method for structurally sparsifying the fully connected layers of pretrained neural networks. Rather than penalizing all layers jointly, our appro…

cs.LG2026

Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression

Mingsong Yan, Dongyang Li, Charles Kulick +1

In this paper, we study in-context kernel ridge regression (KRR) with Gaussian kernels and show, both theoretically and empirically, that a standard softmax-attention transformer c…

stat.ML2025

Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications

Jinchao Feng, Charles Kulick, Sui Tang

We develop a Gaussian process framework for learning interaction kernels in multi-species interacting particle systems from trajectory data. Such systems provide a canonical settin…

cs.LG2025

On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks

Mingsong Yan, Charles Kulick, Sui Tang

Continuous-depth graph neural networks, also known as Graph Neural Differential Equations (GNDEs), combine the structural inductive bias of Graph Neural Networks (GNNs) with the co…

stat.ML2023★ 2 cited

Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures

Jinchao Feng, Charles Kulick, Sui Tang

In this paper, we focus on the data-driven discovery of a general second-order particle-based model that contains many state-of-the-art models for modeling the aggregation and coll…

math.CO2021

Permutations of point sets in

Alvaro Carbonero, Beth Anne Castellano, Gary Gordon +3

Given a set consisting of points in and one or two vantage points, we study the number of orderings of induced by measuring the distance (for one vantage…

stat.ML2021★ 4 cited

Learning particle swarming models from data with Gaussian processes

Jinchao Feng, Charles Kulick, Yunxiang Ren +1

Interacting particle or agent systems that display a rich variety of swarming behaviours are ubiquitous in science and engineering. A fundamental and challenging goal is to underst…