No free lunch theorems for optimization
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which…
No Free Lunch Theorems for Search
It is shown that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions, which allows for mathematical benchmarks for assessing a particular search algorithm's performance.
A practical heuristic for finding graph minors
We present a heuristic algorithm for finding a graph $H$ as a minor of a graph $G$ that is practical for sparse $G$ and $H$ with hundreds of vertices. We also explain the practical importance of…
Optimal search on a technology landscape
Coevolutionary free lunches
This paper presents a general framework covering most optimization scenarios and shows that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems.
A Robust Learning Approach to Domain Adaptive Object Detection
- Mehran Khodabandeh, Arash Vahdat, Mani Ranjbar, W. Macready
- Computer ScienceIEEE International Conference on Computer Vision
- 4 April 2019
A robust object detection framework that is resilient to noise in bounding box class labels, locations and size annotations is proposed that significantly improves the state-of-the-art on multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI datasets.
Discrete optimization using quantum annealing on sparse Ising models
- Zhengbing Bian, Fabián A. Chudak, R. Israel, Brad Lackey, W. Macready, Aidan Roy
- Computer ScienceFrontiers of Physics
- 18 September 2014
A way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible Ising models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware are proposed.
The Ising model : teaching an old problem new tricks
In this paper we investigate the use of hardware which physically realizes quantum annealing for machine learning applications. We show how to take advantage of the hardware in both zeroand…
Parameter space exploration with Gaussian process trees
- R. Gramacy, Herbert K. H. Lee, W. Macready
- Computer ScienceInternational Conference on Machine Learning
- 4 July 2004
A general methodology for addressing the need for computationally inexpensive surrogate models and an accompanying method for selecting small designs that uses non-stationary Gaussian processes is explored.