4 citations · 10 across the 7 of their papers we have counts for
8 papers
LEAP: Gradient Flow Augmented Lagrangian Trajectory Optimization for Contact Constrained Robots
César E. Ramos Chuquiure, Ryan Tsai, Yutong Zhou +8
Generating dynamically feasible motions for legged and manipulating robots requires solving a large, nonlinear, non-convex optimal control problem (OCP) in which contact wrenches m…
Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
Zhengbao He, Ruiqi Ding, Zhehao Huang +3
Low-rank adaptation (LoRA) enables parameter-efficient specialization of foundation models, but the proliferation of task-specific adapters fragments capabilities across many adapt…
Phasing Through the Flames: Rapid Motion Planning with the AGHF PDE for Arbitrary Objective Functions and Constraints
Challen Enninful Adu, César E. Ramos Chuquiure, Yutong Zhou +5
The generation of optimal trajectories for high-dimensional robotic systems under constraints remains computationally challenging due to the need to simultaneously satisfy dynamic…
MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes
Ruikai Yang, Mingzhen He, Zhengbao He +2
Machine unlearning (MU) is to make a well-trained model behave as if it had never been trained on specific data. In today's over-parameterized models, dominated by neural networks,…
Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method
Ruikai Yang, Fan He, Mingzhen He +2
Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputa…
Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature
Ruikai Yang, Fan He, Mingzhen He +2
Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on co…
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
Xinwen Cheng, Zhehao Huang, Wenxin Zhou +4
Machine unlearning (MU) aims to remove the influence of specific training samples from a well-trained model, a task of growing importance due to the ``right to be forgotten.'' The…
A Decentralized Framework for Kernel PCA with Projection Consensus Constraints
Fan He, Ruikai Yang, Lei Shi +1
This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes and a fusion center is prohibited. Compared with…