Search Results for author: Harsh Gupta

Found 8 papers, 3 papers with code

The Mean-Squared Error of Double Q-Learning

1 code implementation NeurIPS 2020 Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

In this paper, we establish a theoretical comparison between the asymptotic mean-squared error of Double Q-learning and Q-learning.


Mixed Logit Models and Network Formation

no code implementations30 Jun 2020 Harsh Gupta, Mason A. Porter

We also illustrate how to use the RC model to accurately study network formation using both synthetic and real-world networks.


Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning

1 code implementation NeurIPS 2019 Harsh Gupta, R. Srikant, Lei Ying

We study two time-scale linear stochastic approximation algorithms, which can be used to model well-known reinforcement learning algorithms such as GTD, GTD2, and TDC.

reinforcement-learning reinforcement Learning

Almost Boltzmann Exploration

no code implementations25 Jan 2019 Harsh Gupta, Seo Taek Kong, R. Srikant, Weina Wang

In this paper, we show that a simple modification to Boltzmann exploration, motivated by a variation of the standard doubling trick, achieves $O(K\log^{1+\alpha} T)$ regret for a stochastic MAB problem with $K$ arms, where $\alpha>0$ is a parameter of the algorithm.

Multi-Armed Bandits

Multimodal Content Analysis for Effective Advertisements on YouTube

no code implementations12 Sep 2017 Nikhita Vedula, Wei Sun, Hyunhwan Lee, Harsh Gupta, Mitsunori Ogihara, Joseph Johnson, Gang Ren, Srinivasan Parthasarathy

The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users.

Recommendation Systems

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