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Found 15 papers, 4 papers with code
Competitions in AI -- Robustly Ranking Solvers Using Statistical Resampling
To address this problem, we introduce a novel approach to statistically meaningful analysis of competition results based on resampling performance data.
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet.
Frugal Machine Learning
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.
Automated Configuration of Negotiation Strategies
By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation scenarios.
Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization
The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications.
Automated Algorithm Selection: Survey and Perspectives
The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning.
LSQ++: Lower running time and higher recall in multi-codebook quantization
Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in multiple bases.
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers
Modern software systems in many application areas offer to the user a multitude of parameters, switches and other customisation hooks.
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures.
Stacked Quantizers for Compositional Vector Compression
Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a generalization of Product Quantization (PQ) where a non-independent set of codebooks is used to compress vectors into small binary codes.
ParamILS: An Automatic Algorithm Configuration Framework
The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms.
Algorithm Runtime Prediction: Methods & Evaluation
We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems.
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.
Sequential Model-Based Optimization for General Algorithm Configuration
State-of-the-art algorithms for hard computational problems often expose many parameters that can be modified to improve empirical performance.
