no code implementations • 16 Mar 2024 • Fan Zhang, Zhaohan Wang, Xin Lyu, Siyuan Zhao, Mengjian Li, Weidong Geng, Naye Ji, Hui Du, Fuxing Gao, Hao Wu, Shunman Li
Finally, we employ the diffusion model to train and infer various gestures.
no code implementations • 28 Feb 2024 • Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010).
no code implementations • 23 Feb 2024 • Xin Lyu, Hongxun Wu, Junzhao Yang
Karbasi and Larsen showed that "significant" parallelization must incur exponential blow-up: Any boosting algorithm either interacts with the weak learner for $\Omega(1 / \gamma)$ rounds or incurs an $\exp(d / \gamma)$ blow-up in the complexity of training, where $d$ is the VC dimension of the hypothesis class.
no code implementations • 4 Dec 2023 • Edith Cohen, Benjamin Cohen-Wang, Xin Lyu, Jelani Nelson, Tamas Sarlos, Uri Stemmer
Moreover, the knowledge of models is often encapsulated in the response distribution itself and preserving this diversity is critical for fluid and effective knowledge transfer from teachers to student.
no code implementations • 12 Oct 2023 • Xin Lyu, Avishay Tal, Hongxun Wu, Junzhao Yang
In this work, for any constant $q$, we prove tight memory-sample lower bounds for any parity learning algorithm that makes $q$ passes over the stream of samples.
no code implementations • 11 Nov 2022 • Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
The problem of learning threshold functions is a fundamental one in machine learning.
no code implementations • 28 Feb 2022 • Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer
CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements.