4 papers
Private Model Personalization Revisited
Conor Snedeker, Xinyu Zhou, Raef Bassily
We study model personalization under user-level differential privacy (DP) in the shared representation framework. In this problem, there are users whose data is statistically h…
Environment-Aware Dynamic Pruning for Pipelined Edge Inference
Austin O'Quinn, Conor Snedeker, Siyuan Zhang +1
IoT and edge-based inference systems require unique solutions to overcome resource limitations and unpredictable environments. In this paper, we propose an environment-aware dynami…
Optimal AdaBoost Converges
Conor Snedeker
The following work is a preprint collection of formal proofs regarding the convergence properties of the AdaBoost machine learning algorithm's classifier and margins. Various math…
Limit Cycles of AdaBoost
Conor Snedeker
The iterative weight update for the AdaBoost machine learning algorithm may be realized as a dynamical map on a probability simplex. When learning a low-dimensional data set this a…