156 citations · 418 across the 15 of their papers we have counts for
8 papers · 1 filter
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas +2
This paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert demonstrator without additional online environment interactions. Instead, the learner…
MobILE: Model-Based Imitation Learning From Observation Alone
Rahul Kidambi, Jonathan Chang, Wen Sun
This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (wit…
MOReL : Model-Based Offline Reinforcement Learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli +1
In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to tra…
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
Rong Ge, Sham M. Kakade, Rahul Kidambi +1
Minimax optimal convergence rates for classes of stochastic convex optimization problems are well characterized, where the majority of results utilize iterate averaged stochastic g…
On the insufficiency of existing momentum schemes for Stochastic Optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain +1
Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks a…
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
Dhruv Mahajan, Vivek Gupta, S Sathiya Keerthi +3
For many applications, an ensemble of base classifiers is an effective solution. The tuning of its parameters(number of classes, amount of data on which each classifier is to be tr…
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam +3
We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill…
A Structured Prediction Approach for Missing Value Imputation
Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam +1
Missing value imputation is an important practical problem. There is a large body of work on it, but there does not exist any work that formulates the problem in a structured outpu…