10 citations · 22 across the 13 of their papers we have counts for
14 papers
Conservative neural posterior estimation via distributionally robust training
William Laplante, Yuga Hikida, Charita Dellaporta +2
Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budgets. To address this, we propose…
Constrained Bayesian Experimental Design via Online Planning
Yujia Guo, Daolang Huang, Xinyu Zhang +3
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic const…
Amortised and provably-robust simulation-based inference
Ayush Bharti, Charita Dellaporta, Yuga Hikida +1
Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outli…
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
Daolang Huang, Xinyi Wen, Ayush Bharti +2
Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instanta…
Multilevel neural simulation-based inference
Yuga Hikida, Ayush Bharti, Niall Jeffrey +1
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used…
Robust Simulation-Based Inference under Missing Data via Neural Processes
Yogesh Verma, Ayush Bharti, Vikas Garg
Simulation-based inference (SBI) methods typically require fully observed data to infer parameters of models with intractable likelihood functions. However, datasets often contain…
Cost-aware simulation-based inference
Ayush Bharti, Daolang Huang, Samuel Kaski +1
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is t…
Bayesian Active Learning in the Presence of Nuisance Parameters
Sabina J. Sloman, Ayush Bharti, Julien Martinelli +1
In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parame…
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Daolang Huang, Ayush Bharti, Amauri Souza +2
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics t…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Ayush Bharti, Masha Naslidnyk, Oscar Key +2
Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously bee…
Approximate Bayesian Computation with Domain Expert in the Loop
Ayush Bharti, Louis Filstroff, Samuel Kaski
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summ…
A General Method for Calibrating Stochastic Radio Channel Models with Kernels
Ayush Bharti, Francois-Xavier Briol, Troels Pedersen
Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophi…
Joint Modeling of Received Power, Mean Delay, and Delay Spread for Wideband Radio Channels
Ayush Bharti, Ramoni Adeogun, Xuesong Cai +4
We propose a multivariate log-normal distribution to jointly model received power, mean delay, and root mean square (rms) delay spread of wideband radio channels, referred to as th…