156 citations · 418 across the 15 of their papers we have counts for
15 papers
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
Adam Block, Rahul Kidambi, Daniel N. Hill +2
Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know wh…
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…
Making Paper Reviewing Robust to Bid Manipulation Attacks
Ruihan Wu, Chuan Guo, Felix Wu +3
Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission n…
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…
Leverage Score Sampling for Faster Accelerated Regression and ERM
Naman Agarwal, Sham Kakade, Rahul Kidambi +3
Given a matrix and a vector , we show how to compute an -approximate solution to the regression problem $ \min_{x\in\m…
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 Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
Prateek Jain, Sham M. Kakade, Rahul Kidambi +3
This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares. This res…
Accelerating Stochastic Gradient Descent For Least Squares Regression
Prateek Jain, Sham M. Kakade, Rahul Kidambi +2
There is widespread sentiment that it is not possible to effectively utilize fast gradient methods (e.g. Nesterov's acceleration, conjugate gradient, heavy ball) for the purposes o…
Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification
Prateek Jain, Sham M. Kakade, Rahul Kidambi +2
This work characterizes the benefits of averaging schemes widely used in conjunction with stochastic gradient descent (SGD). In particular, this work provides a sharp analysis of:…
Submodular Hamming Metrics
Jennifer Gillenwater, Rishabh Iyer, Bethany Lusch +2
We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tract…
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…