4 citations · 13 across the 6 of their papers we have counts for
6 papers
Towards xApp Conflict Evaluation with Explainable Machine Learning and Causal Inference in O-RAN
Pragya Sharma, Shihua Sun, Shachi Deshpande +2
The Open Radio Access Network (O-RAN) architecture enables a flexible, vendor-neutral deployment of 5G networks by disaggregating base station components and supporting third-party…
Calibrated and Conformal Propensity Scores for Causal Effect Estimation
Shachi Deshpande, Volodymyr Kuleshov
Propensity scores are commonly used to estimate treatment effects from observational data. We argue that the probabilistic output of a learned propensity score model should be cali…
Calibrated Regression Against An Adversary Without Regret
Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
We are interested in probabilistic prediction in online settings in which data does not follow a probability distribution. Our work seeks to achieve two goals: (1) producing valid…
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas +2
Estimating the effect of intervention from observational data while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserv…
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Volodymyr Kuleshov, Shachi Deshpande
Accurate probabilistic predictions can be characterized by two properties -- calibration and sharpness. However, standard maximum likelihood training yields models that are poorly…
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
Accurate uncertainty estimates are important in sequential model-based decision-making tasks such as Bayesian optimization. However, these estimates can be imperfect if the data vi…