16 citations · 23 across the 8 of their papers we have counts for
10 papers
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Satyen Kale, Jason D. Lee, Chris De Sa +2
Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has b…
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs
Satyen Kale, Ayush Sekhari, Karthik Sridharan
Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the method of choice for learning with large over-parameterized models. A popular theory for explaining why SGD…
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Christoph Dann, Yishay Mansour, Mehryar Mohri +2
There have been many recent advances on provably efficient Reinforcement Learning (RL) in problems with rich observation spaces. However, all these works share a strong realizabili…
Neural Active Learning with Performance Guarantees
Pranjal Awasthi, Christoph Dann, Claudio Gentile +2
We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are stochastically generated from a class of functions on which w…
Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath +1
We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset drawn i.i.d. from an unknown distribution, and outputs a model $\widehat…
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
Yossi Arjevani, Yair Carmon, John C. Duchi +3
We design an algorithm which finds an -approximate stationary point (with ) using stochastic gradient and Hessian-vector products, matching gua…