David Bindel
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Bean: A Language for Backward Error Analysis Open
Backward error analysis offers a method for assessing the quality of numerical programs in the presence of floating-point rounding errors. However, techniques from the numerical analysis literature for quantifying backward error require su…
High-order magnetic near-axis expansion: ill-posedness and regularisation Open
When analysing stellarator configurations, it is common to perform an asymptotic expansion about the magnetic axis. This so-called near-axis expansion is convenient for the same reason asymptotic expansions often are, namely, it reduces th…
Bean: A Language for Backward Error Analysis Open
Backward error analysis offers a method for assessing the quality of numerical programs in the presence of floating-point rounding errors. However, techniques from the numerical analysis literature for quantifying backward error require su…
The High-Order Magnetic Near-Axis Expansion: Ill-Posedness and Regularization Open
When analyzing stellarator configurations, it is common to perform an asymptotic expansion about the magnetic axis. This so-called near-axis expansion is convenient for the same reason asymptotic expansions often are, namely, it reduces th…
Differentiating Policies for Non-Myopic Bayesian Optimization Open
Bayesian optimization (BO) methods choose sample points by optimizing an acquisition function derived from a statistical model of the objective. These acquisition functions are chosen to balance sampling regions with predicted good objecti…
Walking on Spheres and Talking to Neighbors: Variance Reduction for Laplace's Equation Open
Walk on Spheres algorithms leverage properties of Brownian Motion to create Monte Carlo estimates of solutions to a class of elliptic partial differential equations. We propose a new caching strategy which leverages the continuity of paths…
Level Set Learning for Poincaré Plots of Symplectic Maps Open
Many important qualities of plasma confinement devices can be determined via the Poincaré plot of a symplectic return map. These qualities include the locations of periodic orbits, magnetic islands, and chaotic regions of phase space. Howe…
Understanding trade-offs in stellarator design with multi-objective optimization Open
In designing stellarators, any design decision ultimately comes with a trade-off. Improvements in particle confinement, for instance, may increase the burden on engineers to build more complex coils, and the tightening of financial constra…
Direct Optimization of Fast-Ion Confinement in Stellarators Open
Confining energetic ions such as alpha particles is a prime concern in the design of stellarators. However, directly measuring alpha confinement through numerical simulation of guiding-center trajectories has been considered to be too comp…
Understanding Trade-offs in Stellarator Design with Multi-objective Optimization Open
In designing stellarators, any design decision ultimately comes with a trade-off. Improvements in particle confinement, for instance, may increase the burden on engineers to build more complex coils, and the tightening of financial constra…
Direct Optimization of Fast-Ion Confinement in Stellarators Open
Data for the paper "Direct Optimization of Fast-Ion Confinement in Stellarators" by David Bindel, Matt Landreman, and Misha Padidar
Understanding Trade-offs in Stellarator Design with Multi-objective Optimization Open
Dataset for the paper "Understanding Trade-offs in Stellarator Design with Multi-objective Optimization" by David Bindel, Matt Landreman, Misha Padidar
Understanding Trade-offs in Stellarator Design with Multi-objective Optimization Open
Dataset for the paper "Understanding Trade-offs in Stellarator Design with Multi-objective Optimization" by David Bindel, Matt Landreman, Misha Padidar
Direct Optimization of Fast-Ion Confinement in Stellarators Open
Confining energetic ions such as alpha particles is a prime concern in the design of stellarators. However, directly measuring alpha confinement through numerical simulation of guiding-center trajectories has been considered to be too comp…
View article: Scalable Bayesian Transformed Gaussian Processes
Scalable Bayesian Transformed Gaussian Processes Open
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a joint prior over input warping and kernel hyperparameters. T…
Global stochastic optimization of stellarator coil configurations Open
In the construction of a stellarator, the manufacturing and assembling of the coil system is a dominant cost. These coils need to satisfy strict engineering tolerances, and if those are not met the project could be cancelled as in the case…
Early termination strategies with asynchronous parallel optimization in application to automatic calibration of groundwater PDE models Open
Automatic calibration is widely used to estimate parameters in hydrological models. The main idea is to use optimization algorithms to minimize the discrepancy between field data and simulation prediction. This process involves iterative e…
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference Open
Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative. By transforming unsupervised learning problems into decompositions of co-occurrence statistics, spectral algorithms provid…
Streaming Local Community Detection through Approximate Conductance Open
Community is a universal structure in various complex networks, and community detection is a fundamental task for network analysis. With the rapid growth of network scale, networks are massive, changing rapidly and could naturally be model…
Surveillance Evasion Through Bayesian Reinforcement Learning Open
We consider a task of surveillance-evading path-planning in a continuous setting. An Evader strives to escape from a 2D domain while minimizing the risk of detection (and immediate capture). The probability of detection is path-dependent a…
View article: Scaling Gaussian Processes with Derivative Information Using Variational Inference
Scaling Gaussian Processes with Derivative Information Using Variational Inference Open
Gaussian processes with derivative information are useful in many settings where derivative information is available, including numerous Bayesian optimization and regression tasks that arise in the natural sciences. Incorporating derivativ…
Density of States Graph Kernels Open
A fundamental problem on graph-structured data is that of quantifying similarity between graphs. Graph kernels are an established technique for such tasks; in particular, those based on random walks and return probabilities have proven to …
Density of States Graph Kernels Open
A fundamental problem on graph-structured data is that of quantifying similarity between graphs. Graph kernels are an established technique for such tasks; in particular, those based on random walks and return probabilities have proven to …
On the Distribution of Minima in Intrinsic-Metric Rotation Averaging Open
Rotation Averaging is a non-convex optimization problem that determines orientations of a collection of cameras from their images of a 3D scene. The problem has been studied using a variety of distances and robustifiers. The intrinsic (or …
View article: Efficient Rollout Strategies for Bayesian Optimization
Efficient Rollout Strategies for Bayesian Optimization Open
Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future evaluations via the optimization of an acquisition function…
Randomly Projected Additive Gaussian Processes for Regression Open
Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle with even moderate input dimensionality. Learning a low dim…
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization Open
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, d…
A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer Open
Inferring electromagnetic propagation characteristics within the marine atmospheric boundary layer (MABL) from data in real time is crucial for modern maritime navigation and communications. The propagation of electromagnetic waves is well…
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm Open
Moontae Lee, Sungjun Cho, David Bindel, David Mimno. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.