1 citations · 1 across the 4 of their papers we have counts for
5 papers
Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
Kaushal Paneri, Michael Munje, Kailash Singh Maurya +2
Growing scale of recommender systems require extensive tuning to respond to market dynamics and system changes. We address the challenge of tuning a large-scale ads recommendation…
Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning
Yimeng Jia, Kaushal Paneri, Rong Huang +3
This paper introduces Adaptive Mixture Importance Sampling (AMIS) as a novel approach for optimizing key performance indicators (KPIs) in large-scale recommender systems, such as o…
Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
Jeremy Zucker, Kaushal Paneri, Sara Mohammad-Taheri +8
Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, c…
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
Robert Osazuwa Ness, Kaushal Paneri, Olga Vitek
This manuscript contributes a general and practical framework for casting a Markov process model of a system at equilibrium as a structural causal model, and carrying out counterfa…
Crop Planning using Stochastic Visual Optimization
Gunjan Sehgal, Bindu Gupta, Kaushal Paneri +3
As the world population increases and arable land decreases, it becomes vital to improve the productivity of the agricultural land available. Given the weather and soil properties,…