15 citations · 37 across the 10 of their papers we have counts for
10 papers
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees
Arindam Banerjee, Qiaobo Li, Yingxue Zhou
Generalization and optimization guarantees on the population loss often rely on uniform convergence based analysis, typically based on the Rademacher complexity of the predictors.…
RecMind: Large Language Model Powered Agent For Recommendation
Yancheng Wang, Ziyan Jiang, Zheng Chen +7
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting…
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
Arindam Banerjee, Tiancong Chen, Xinyan Li +1
Recent years have seen advances in generalization bounds for noisy stochastic algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on stability (Mou et al., 20…
Noisy Truncated SGD: Optimization and Generalization
Yingxue Zhou, Xinyan Li, Arindam Banerjee
Recent empirical work on stochastic gradient descent (SGD) applied to over-parameterized deep learning has shown that most gradient components over epochs are quite small. Inspired…
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee
Differentially private SGD (DP-SGD) is one of the most popular methods for solving differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each g…
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds
Yingxue Zhou, Xiangyi Chen, Mingyi Hong +2
We study differentially private (DP) algorithms for stochastic non-convex optimization. In this problem, the goal is to minimize the population loss over a -dimensional space gi…
De-randomized PAC-Bayes Margin Bounds: Applications to Non-convex and Non-smooth Predictors
Arindam Banerjee, Tiancong Chen, Yingxue Zhou
In spite of several notable efforts, explaining the generalization of deterministic non-smooth deep nets, e.g., ReLU-nets, has remained challenging. Existing approaches for determi…
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
Xinyan Li, Qilong Gu, Yingxue Zhou +2
While stochastic gradient descent (SGD) and variants have been surprisingly successful for training deep nets, several aspects of the optimization dynamics and generalization are s…
Distributed Private Online Learning for Social Big Data Computing over Data Center Networks
Chencheng Li, Pan Zhou, Yingxue Zhou +3
With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massiv…
Differentially Private Online Learning for Cloud-Based Video Recommendation with Multimedia Big Data in Social Networks
Pan Zhou, Yingxue Zhou, Dapeng Wu +1
With the rapid growth in multimedia services and the enormous offers of video contents in online social networks, users have difficulty in obtaining their interests. Therefore, var…