13 citations · 19 across the 14 of their papers we have counts for
17 papers
Online Adaptive Kernel Mixing for Gaussian Process Decision Making
Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy +1
Gaussian Processes (GPs) are widely used as surrogates for black-box functions in sequential decision-making problems such as Bayesian optimization (BO), level set estimation (LSE)…
Bayesian Optimization for Dynamic Pricing and Learning
Anush Anand, Pranav Agrawal, Tejas Bodas
Dynamic pricing is the practice of adjusting the selling price of a product to maximize a firm's revenue by responding to market demand. The literature typically distinguishes betw…
CGD: Modifying the Loss Landscape by Gradient Regularization
Shikhar Saxena, Tejas Bodas, Arti Yardi
Line-search methods are commonly used to solve optimization problems. The simplest line search method is steepest descent where one always moves in the direction of the negative gr…
Tabular and Deep Reinforcement Learning for Gittins Index
Harshit Dhankhar, Kshitij Mishra, Tejas Bodas
In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian ar…
Mixture Density Networks for Classification with an Application to Product Bundling
Narendhar Gugulothu, Sanjay P. Bhat, Tejas Bodas
While mixture density networks (MDNs) have been extensively used for regression tasks, they have not been used much for classification tasks. One reason for this is that the usabil…
Practical First-Order Bayesian Optimization Algorithms
Utkarsh Prakash, Aryan Chollera, Kushagra Khatwani +2
First Order Bayesian Optimization (FOBO) is a sample efficient sequential approach to find the global maxima of an expensive-to-evaluate black-box objective function by suitably qu…