Quantum speedups of optimizing approximately convex functions with applications to logarithmic regret stochastic convex bandits
We initiate the study of quantum algorithms for optimizing approximately convex functions.
Given a convex set $\mathcal {K}\subseteq\mathbb {R}^{n} $ and a function …
Given a convex set $\mathcal {K}\subseteq\mathbb {R}^{n} $ and a function …
Learning constraints from demonstrations
We extend the learning from demonstration paradigm by providing a method for learning
unknown constraints shared across tasks, using demonstrations of the tasks, their cost …
unknown constraints shared across tasks, using demonstrations of the tasks, their cost …
Learning constraints from demonstrations with grid and parametric representations
We extend the learning from demonstration paradigm by providing a method for learning
unknown constraints shared across tasks, using demonstrations of the tasks, their cost …
unknown constraints shared across tasks, using demonstrations of the tasks, their cost …
Generalizing informed sampling for asymptotically-optimal sampling-based kinodynamic planning via markov chain monte carlo
Asymptotically-optimal motion planners such as RRT* have been shown to incrementally
approximate the shortest path between start and goal states. Once an initial solution is …
approximate the shortest path between start and goal states. Once an initial solution is …
Learning parametric constraints in high dimensions from demonstrations
We present a scalable algorithm for learning parametric constraints in high dimensions from
safe expert demonstrations. To reduce the ill-posedness of the constraint recovery problem …
safe expert demonstrations. To reduce the ill-posedness of the constraint recovery problem …
Randomized geometric tools for anomaly detection in stock markets
C Bachelard, A Chalkis… - International …, 2023 - proceedings.mlr.press
We propose novel randomized geometric tools to detect low-volatility anomalies in stock
markets; a principal problem in financial economics. Our modeling of the (detection) problem …
markets; a principal problem in financial economics. Our modeling of the (detection) problem …
Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises
We examine volume computation of general-dimensional polytopes and more general
convex bodies, defined as the intersection of a simplex by a family of parallel hyperplanes …
convex bodies, defined as the intersection of a simplex by a family of parallel hyperplanes …
Global optimization: Hit and run methods
ZB Zabinsky - Encyclopedia of Optimization, 2024 - Springer
The hit and run algorithms fall into the category of sequential random search methods (cf.
also⊳“Random Search Methods”), or stochastic methods. These methods can be applied to …
also⊳“Random Search Methods”), or stochastic methods. These methods can be applied to …
Practical volume approximation of high-dimensional convex bodies, applied to modeling portfolio dependencies and financial crises
We examine volume computation of general-dimensional polytopes and more general
convex bodies, defined by the intersection of a simplex by a family of parallel hyperplanes …
convex bodies, defined by the intersection of a simplex by a family of parallel hyperplanes …
Approximating the Geometry of Temporal Logic Formulas
C Abou-Mrad, H Abbas - Proceedings of the 27th ACM International …, 2024 - dl.acm.org
We present an algorithm for approximating the language of a temporal logic formula, that is,
the set of all signals that satisfy the formula. Most tasks involving temporal logic require …
the set of all signals that satisfy the formula. Most tasks involving temporal logic require …