24 citations · 86 across the 22 of their papers we have counts for
6 papers · 1 filter
The complexity of non-stationary reinforcement learning
Christos Papadimitriou, Binghui Peng
The problem of continual learning in the domain of reinforcement learning, often called non-stationary reinforcement learning, has been identified as an important challenge to the…
Memory Bounds for Continual Learning
Xi Chen, Christos Papadimitriou, Binghui Peng
Continual learning, or lifelong learning, is a formidable current challenge to machine learning. It requires the learner to solve a sequence of different learning tasks, one af…
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
Binghui Peng, Andrej Risteski
Continual learning is an emerging paradigm in machine learning, wherein a model is exposed in an online fashion to data from multiple different distributions (i.e. environments), a…
Shuffle Private Stochastic Convex Optimization
Albert Cheu, Matthew Joseph, Jieming Mao +1
In shuffle privacy, each user sends a collection of randomized messages to a trusted shuffler, the shuffler randomly permutes these messages, and the resulting shuffled collection…
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
Xiaoxiao Li, Yangsibo Huang, Binghui Peng +2
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data…
Training (Overparametrized) Neural Networks in Near-Linear Time
Jan van den Brand, Binghui Peng, Zhao Song +1
The slow convergence rate and pathological curvature issues of first-order gradient methods for training deep neural networks, initiated an ongoing effort for developing faster $\m…