106 citations · 112 across the 13 of their papers we have counts for
5 papers · 1 filter
Randomness Helps Rigor: A Probabilistic Learning Rate Scheduler Bridging Theory and Deep Learning Practice
Dahlia Devapriya, Thulasi Tholeti, Janani Suresh +1
Learning rate schedulers have shown great success in speeding up the convergence of learning algorithms in practice. However, their convergence to a minimum has not been proven the…
The robust way to stack and bag: the local Lipschitz way
Thulasi Tholeti, Sheetal Kalyani
Recent research has established that the local Lipschitz constant of a neural network directly influences its adversarial robustness. We exploit this relationship to construct an e…
How to boost autoencoders?
Sai Krishna, Thulasi Tholeti, Sheetal Kalyani
Autoencoders are a category of neural networks with applications in numerous domains and hence, improvement of their performance is gaining substantial interest from the machine le…
On the Differentially Private Nature of Perturbed Gradient Descent
Thulasi Tholeti, Sheetal Kalyani
We consider the problem of empirical risk minimization given a database, using the gradient descent algorithm. We note that the function to be optimized may be non-convex, consisti…
Tune smarter not harder: A principled approach to tuning learning rates for shallow nets
Thulasi Tholeti, Sheetal Kalyani
Effective hyper-parameter tuning is essential to guarantee the performance that neural networks have come to be known for. In this work, a principled approach to choosing the learn…