Search Results for author: Rahul Ramesh

Found 8 papers, 3 papers with code

The Value of Out-of-Distribution Data

no code implementations23 Aug 2022 Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein

We demonstrate a counter-intuitive phenomenon for such problems: generalization error of the task can be a non-monotonic function of the number of OOD samples; a small number of OOD samples can improve generalization but if the number of OOD samples is beyond a threshold, then the generalization error can deteriorate.

Data Augmentation Hyperparameter Optimization

Deep Reference Priors: What is the best way to pretrain a model?

2 code implementations pproximateinference AABI Symposium 2022 Yansong Gao, Rahul Ramesh, Pratik Chaudhari

Such priors enable the task to maximally affect the Bayesian posterior, e. g., reference priors depend upon the number of samples available for learning the task and for very small sample sizes, the prior puts more probability mass on low-complexity models in the hypothesis space.

Semi-Supervised Image Classification Transfer Learning

Model Zoo: A Growing Brain That Learns Continually

no code implementations ICLR 2022 Rahul Ramesh, Pratik Chaudhari

This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models.

Continual Learning Learning Theory

Model Zoo: A Growing "Brain" That Learns Continually

2 code implementations6 Jun 2021 Rahul Ramesh, Pratik Chaudhari

We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them.

Continual Learning Learning Theory

Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning

no code implementations9 Sep 2019 Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran

Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent.

reinforcement-learning

Successor Options: An Option Discovery Framework for Reinforcement Learning

1 code implementation14 May 2019 Rahul Ramesh, Manan Tomar, Balaraman Ravindran

This work adopts a complementary approach, where we attempt to discover options that navigate to landmark states.

Navigate reinforcement-learning

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