More Like this

1. Data Augmentation MCMC for Bayesian Inference from Privatized Data

   Ju, Nianqiao; Awan, Jordan; Gong, Ruobin; Rao, Vinayak (January 2022, Advances in neural information processing systems)

   Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; Oh, A. (Ed.)

   Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential data. Specifically, the likelihood function of the privatized data requires integrating over the large space of confidential databases and is typically intractable. For Bayesian analysis, this results in a posterior distribution that is doubly intractable, rendering traditional MCMC techniques inapplicable. We propose an MCMC framework to perform Bayesian inference from the privatized data, which is applicable to a wide range of statistical models and privacy mechanisms. Our MCMC algorithm augments the model parameters with the unobserved confidential data, and alternately updates each one conditional on the other. For the potentially challenging step of updating the confidential data, we propose a generic approach that exploits the privacy guarantee of the mechanism to ensure efficiency. We give results on the computational complexity, acceptance rate, and mixing properties of our MCMC. We illustrate the efficacy and applicability of our methods on a naive-Bayes log-linear model and on a linear regression model. 

   more » « less
2. Sensing and actuation are intricately connected in soft robotics, where contact may change actuator mechanics and robot behavior. To improve soft robotic control and performance, proprioception and contact sensors are needed to report robot state without altering actuation mechanics or introducing bulky, rigid components. For bioinspired McKibben-style fluidic actuators, prior work in sensing has focused on sensing the strain of the actuator by embedding sensors in the actuator bladder during fabrication, or by adhering sensors to the actuator surface after fabrication. However, material property mismatches between sensors and actuators can impede actuator performance, and many soft sensors available for use with fluidic actuators rely on costly or labor-intensive fabrication methods. Here, we demonstrate a low-cost and easy-to manufacture-tubular liquid metal strain sensor for use with soft actuators that can be used to detect actuator strain and contact between the actuator and external objects. The sensor is flexible, can be fabricated with commercial-off-the-shelf components, and can be easily integrated with existing soft actuators to supplement sensing, regardless of actuator shape or size. Furthermore, the soft tubular strain sensor exhibits low hysteresis and high sensitivity. The approach presented in this work provides a low-cost, soft sensing solution for broad application in soft robotics.

   Jackson, Clayton; Chardon, Matthieu; Wang, Y Curtis; Rudi, Johann; Tresch, Matthew; Heckman, Charles J; Quinn, Roger D (August 2023, Springer, Cham.)

   This work explored synaptic strengths in a computational neuroscience model of a controller for the hip joint of a rat which consists of Ia interneurons, Renshaw cells, and the associated motor neurons. This circuit has been referred to as the Canonical Motor Microcircuit (CMM). It is thought that the CMM acts to modulate motor neuron activity at the output stage. We first created a biomechanical model of a rat hindlimb consisting of a pelvis, femur, shin, foot, and flexor-extensor muscle pairs modeled with a Hill muscle model. We then modeled the CMM using non-spiking leaky-integrator neural models connected with conductance-based synapses. To tune the parameters in the network, we implemented an automated approach for parameter search using the Markov chain Monte Carlo (MCMC) method to solve a parameter estimation problem in a Bayesian inference framework. As opposed to traditional optimization techniques, the MCMC method identifies probability densities over the multidimensional space of parameters. This allows us to see a range of likely parameters that produce model outcomes consistent with animal data, determine if the distribution of likely parameters is uni- or multi-modal, as well as evaluate the significance and sensitivity of each parameter. This approach will allow for further analysis of the circuit, specifically, the function and significance of Ia feedback and Renshaw cells. 

   more » « less
3. Variational inference with NoFAS: Normalizing flow with adaptive surrogate for computationally expensive models

   https://doi.org/https://doi.org/10.48550/arXiv.2108.12657  

   Wang, Yu; Liu, Fang; Schiavazzi, Daniele E. (October 2022, Journal of computational physics)

   Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range of applications. Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space, and rely on gradient-based optimization instead of sampling, providing a more efficient approach for Bayesian inference about the model parameters. Moreover, the cost of frequently evaluating an expensive likelihood can be mitigated by replacing the true model with an offline trained surrogate model, such as neural networks. However, this approach might generate significant bias when the surrogate is insufficiently accurate around the posterior modes. To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters. We also propose an efficient sample weighting scheme for surrogate model training that preserves global accuracy while effectively capturing high posterior density regions. We demonstrate the inferential and computational superiority of NoFAS against various benchmarks, including cases where the underlying model lacks identifiability. The source code and numerical experiments used for this study are available at https://github.com/cedricwangyu/NoFAS. 

   more » « less
4. Data Augmentation MCMC for Bayesian Inference from Privatized Data

   Ju, Nianqiao; Awan, Jordan; Gong, Ruobin; Rao, Vinayak (December 2022, Advances in neural information processing systems)

   Koyejo, Sanmi; Mohamed, Shakir (Ed.)

   Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential data. Specifically, the likelihood function of the privatized data requires integrating over the large space of confidential databases and is typically intractable. For Bayesian analysis, this results in a posterior distribution that is doubly intractable, rendering traditional MCMC techniques inapplicable. We propose an MCMC framework to perform Bayesian inference from the privatized data, which is applicable to a wide range of statistical models and privacy mechanisms. Our MCMC algorithm augments the model parameters with the unobserved confidential data, and alternately updates each one conditional on the other. For the potentially challenging step of updating the confidential data, we propose a generic approach that exploits the privacy guarantee of the mechanism to ensure efficiency. In particular, we give results on the computational complexity, acceptance rate, and mixing properties of our MCMC. We illustrate the efficacy and applicability of our methods on a na\"ive-Bayes log-linear model as well as on a linear regression model. 

   more » « less
5. Computational techniques for parameter estimation of gravitational wave signals

   https://doi.org/10.1002/wics.1532  

   Meyer, Renate; Edwards, Matthew_C; Maturana‐Russel, Patricio; Christensen, Nelson (September 2020, WIREs Computational Statistics)

   Abstract Since the very first detection of gravitational waves from the coalescence of two black holes in 2015, Bayesian statistical methods have been routinely applied by LIGO and Virgo to extract the signal out of noisy interferometric measurements, obtain point estimates of the physical parameters responsible for producing the signal, and rigorously quantify their uncertainties. Different computational techniques have been devised depending on the source of the gravitational radiation and the gravitational waveform model used. Prominent sources of gravitational waves are binary black hole or neutron star mergers, the only objects that have been observed by detectors to date. But also gravitational waves from core‐collapse supernovae, rapidly rotating neutron stars, and the stochastic gravitational‐wave background are in the sensitivity band of the ground‐based interferometers and expected to be observable in future observation runs. As nonlinearities of the complex waveforms and the high‐dimensional parameter spaces preclude analytic evaluation of the posterior distribution, posterior inference for all these sources relies on computer‐intensive simulation techniques such as Markov chain Monte Carlo methods. A review of state‐of‐the‐art Bayesian statistical parameter estimation methods will be given for researchers in this cross‐disciplinary area of gravitational wave data analysis. This article is categorized under:Applications of Computational Statistics > Signal and Image Processing and CodingStatistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)Statistical Models > Time Series Models 

   more » « less