170 citations · 506 across the 51 of their papers we have counts for
10 papers · 1 filter
Sparse Representations of Positive Functions via First and Second-Order Pseudo-Mirror Descent
Abhishek Chakraborty, Ketan Rajawat, Alec Koppel
We consider expected risk minimization problems when the range of the estimator is required to be nonnegative, motivated by the settings of maximum likelihood estimation (MLE) and…
A Markov Decision Process Approach to Active Meta Learning
Bingjia Wang, Alec Koppel, Vikram Krishnamurthy
In supervised learning, we fit a single statistical model to a given data set, assuming that the data is associated with a singular task, which yields well-tuned models for specifi…
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems
Zhan Gao, Alec Koppel, Alejandro Ribeiro
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we s…
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
Junyu Zhang, Alec Koppel, Amrit Singh Bedi +2
In recent years, reinforcement learning (RL) systems with general goals beyond a cumulative sum of rewards have gained traction, such as in constrained problems, exploration, and a…
Policy Gradient using Weak Derivatives for Reinforcement Learning
Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy
This paper considers policy search in continuous state-action reinforcement learning problems. Typically, one computes search directions using a classic expression for the policy g…
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck
Alec Koppel, Hrusikesha Pradhan, Ketan Rajawat
Gaussian processes provide a framework for nonlinear nonparametric Bayesian inference widely applicable across science and engineering. Unfortunately, their computational burden sc…