We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution. The goal of the configurator is to find a configuration that runs fast on average on most instances, and do so with the least amount of total work. It can run a chosen solver on a r…
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48 results for “algorithm configuration”
Kernel-based algorithm optimizes cellular network configuration through multi-task learning.
problem Optimizing network configuration based on field experience and minimizing exploration cost.
method Kernel-based multi-BS contextual bandit algorithm leveraging conditional kernel embedding for multi-task learning.
result The proposed algorithm reduces exploration cost and improves network performance.
New benchmarks for algorithm control using RL.
problem Optimizing algorithm performance through online hyperparameter tuning.
method Formulated as a contextual MDP, solved with reinforcement learning.
result Reinforcement learning outperforms black-box approaches for longer sequences.
Algorithm calculates stable multiplicities in cohomology of configuration spaces.
problem Computing stable multiplicities of irreducible representations in cohomology.
method Developed an algorithm to compute stable multiplicities of families of irreducible representations.
result Computed stable multiplicities for all Young diagrams with 23 boxes up to degree 50.
We prove a conjecture of Crapo and Penne which characterizes isotopy classes of skew configurations with spindle-structure. We use this result in order to define an invariant, spindle-genus, for spindle-configurations. We also slightly simplify the exposition of some known invariants for configurations of skew lines an…
Proposes learning default hyperparameters from empirical results.
problem Selecting optimal hyperparameters for machine learning algorithms.
method Learning a set of complementary default values from a database of prior empirical results.
result Learning default values improves performance and is more efficient than random search and Bayesian Optimization.
ExperienceThinking optimizes hyperparameters quickly with smart pruning and knowledge use.
problem Efficiently optimizing hyperparameters in machine learning with limited evaluations.
method Two novel methods: search space pruning and knowledge utilization.
result ExperienceThinking outperforms classical algorithms in few evaluations.
SMAC3 optimizes machine learning hyperparameters efficiently.
problem Optimizing hyperparameters for machine learning algorithms.
method Bayesian Optimization framework with facades for various use cases.
result Improves performance with minimal evaluations.
Paper proposes a new method for approximating high-dimensional matrices using Kronecker products.
problem Discovering low-dimensional structure in high-dimensional data.
method Hybrid Kronecker Product Approximation (hKoPA) and estimation procedures.
result The proposed methods provide flexible and effective dimension reduction.
Meta-learning symbolic default hyperparameters from dataset properties.
problem Empirical hyperparameter optimization is slow and requires manual configuration.
method Evolutionary algorithm to learn symbolic hyperparameter formulas from dataset properties.
result Meta-learning finds viable symbolic defaults for ML algorithms.
The goal of machine learning is to provide solutions which are trained by data or by experience coming from the environment. Many training algorithms exist and some brilliant successes were achieved. But even in structured environments for machine learning (e.g. data mining or board games), most applications beyond the…
Data-driven configuration tuning of glmnet for balancing accuracy and computational efficiencystat.CO
Optimizes glmnet configuration for better accuracy and efficiency.
problem Inappropriate glmnet configuration leads to inaccurate solutions and increased computation time.
method Data-driven framework using neural networks to predict accuracy and computation time from dataset characteristics and configuration.
result Automatic selection of optimal configuration maximizing accuracy under a time constraint.
POCA optimizes hyperparameters with adaptive allocation for faster convergence.
problem Optimizing hyperparameters for machine learning models.
method Adaptive allocation of computational budget using Bayesian sampling.
result POCA finds strong configurations faster than its competitors.
Greedy algorithm performs well in online matching despite non-i.i.d. connections.
problem Online matching in sparse random graphs with fixed degree distributions.
method Approximating stochastic processes with partial differential equations.
result GREEDY algorithm can outperform RANKING in certain configurations.
Self-configuring deep neural networks with fast training.
problem Training multi-level neural networks efficiently and automatically.
method Training neural networks layer by layer, using adaptive and self-adjusting parameters.
result Ability to self-configure and automatically build deep neural networks.
Heuristic optimisers which search for an optimal configuration of variables relative to an objective function often get stuck in local optima where the algorithm is unable to find further improvement. The standard approach to circumvent this problem involves periodically restarting the algorithm from random initial con…
SplitNN-driven Vertical Partitioning enables distributed learning from diverse data sources.
problem Learning from vertically distributed features across institutions.
method A configuration of SplitNN that does not share raw data or model details.
result Flexibility in merging split model outputs and resource efficiency.
We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to f…
ABoB optimizes online configuration tuning by clustering parameters and accelerating learning.
problem Online optimization in large, dynamic parameter spaces.
method Hierarchical adversarial bandit framework.
result Significant performance gains in adversarial metric scenarios.
SORSCNs improve nonstationary data modeling by self-organizing and adjusting network parameters.
problem Nonstationary data challenges traditional models in continuous learning.
method SORSCNs autonomously adjust network parameters and structure in real-time using adaptive algorithms.
result SORSCNs outperform other models in generalizing to nonstationary data.
Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulationcond-mat.stat-mech
Machine learning classifies phases of spin models using improved correlation configurations.
problem Classifying phases of spin models using machine learning.
method Improved correlation configuration estimator applied to machine learning.
result Classifies Berezinskii-Kosterlitz-Thouless transition in quantum XY model.
Deep learning enhances Hamiltonian Monte Carlo for sampling gauge field configurations.
problem Sampling from complex gauge field topologies efficiently.
method Stacked neural networks to generalize Hamiltonian Monte Carlo.
result Significantly reduces computational cost for generating gauge field configurations.
A new method uses modified Boltzmann weights to infer system configurations from observed data.
problem Inference of system configurations from limited observed data.
method Data-driven approach based on re-weighting observed configurations to achieve a flat distribution probability.
result Accurate inference of system configurations with high-temperature re-weighting of observations.
Using results on the topology of moduli space of polygons [Jaggi, 92; Kapovich and Millson, 94], it can be shown that for a planar robot arm with segments there are some values of the base-length, , at which the configuration space of the constrained arm (arm with its end effector fixed) has two disconnected com…
Algorithm learns optimal parameters from infinite space for computational resource optimization.
problem Finding nearly-optimal parameters from an infinite space of tunable parameters.
method Learn a finite set of promising parameters from an infinite set using a data-independent discretization approach.
result Algorithm can help compile a configuration portfolio or select input to a configuration algorithm for finite parameter spaces.
Researchers analyze a new neural network training method.
problem Training robust configurations in discrete weight neural networks.
method Replicated simulated annealing combining physics and classical simulated annealing.
result Explicit criteria for algorithm convergence and successful sampling.
We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. The spectral algorithm consists of multiple algorithmic steps, named as "{edges}", to which noise cou…
The topological complexity TC(X) is a numerical homotopy invariant of a topological space X which is motivated by robotics and is similar in spirit to the classical Lusternik-Schnirelmann category of X. Given a mechanical system with configuration space X, the invariant TC(X) measures the complexity of all possible mot…
DreamerV3 learns diverse tasks with a single configuration.
problem Generalizing reinforcement learning across multiple domains.
method World model and farsighted strategies.
result Outperforms specialized methods across 150 diverse tasks.
Automated ML simplifies machine learning configurations.
problem Difficulty in configuring and selecting ML methods.
method Bi-level learning objective, learning strategy, and evaluation strategy.
result Satisfactory ML configurations generated automatically.
New algorithms improve binary neural network configurations.
problem Training binary neural networks efficiently.
method Stochastic message passing algorithms (BP and SP) for discrete inference.
result Stochastic BP and SP find better BNN configurations.
Paper proposes a method to recover point configurations from noisy distance data.
problem Recovering point configurations from noisy distance data.
method Robust Euclidean Distance Geometry via Dual Basis (RoDEoDB) algorithm.
result Exact recovery guarantees for point configuration and Gram matrix under mild conditions.
EGO optimizes neural network architectures without manual tuning.
problem Designing optimal neural network architectures is difficult and time-consuming.
method Adapted EGO algorithm for efficient optimization of neural network architectures.
result Automatically optimized neural networks achieve competitive performance compared to hand-crafted ones.
Adaptive tuning of portfolio selection parameters improves performance in volatile markets.
problem Improving online portfolio selection in volatile financial markets.
method Modeling parameter space with Gaussian process prior and using adaptive Bayesian optimization for automatic configuration.
result Oracle-based adaptive configuration enhances performance of online portfolio selection algorithms.
Automated HPO design using Bayesian optimization and benchmarking.
problem Designing effective hyperparameter optimization algorithms is manual and lacks systematic understanding.
method Formalized space of HPO candidates, Bayesian optimization for search, ablation analysis.
result Simple configurations can perform well in HPO, especially with right parameters.
Computes fundamental groups of restricted configuration spaces.
problem Understanding the structure of configuration spaces after removing hypersurfaces.
method Fibration over unordered configuration spaces of points, computation of fundamental groups.
result Fundamental groups of restricted configuration spaces computed in small dimensions.
DeepRSCN models nonlinear systems using stochastic configurations.
problem Modeling nonlinear dynamic systems efficiently.
method Incrementally constructed deep reservoir computing framework with random parameters and online weight updates.
result DeepRSCN outperforms single-layer networks in efficiency, learning, and generalization.
Entropy from Machine Learningcond-mat.stat-mech
Machine learning for entropy calculation from binary signals.
problem Calculating entropy from binary configurations/signals.
method Transformed entropy calculation into supervised classification tasks using machine learning.
result Reproduced entropy and free energy of the 2D Ising model.
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While a myriad of classification methods have been proposed, there is no consensus on which methods are more suitable for a given …
New approach classifies EEG signals for SAD detection with improved accuracy.
problem Detecting SAD using EEG for classification with limited study.
method Exploits EEG sensor spatial configuration with different interpolation methods.
result Model 2 significantly outperforms model 1, providing -- higher accuracy.
We study the configuration space of equilateral and equiangular spatial hexagons for any bond angle by giving explicit expressions of all the possible shapes. We show that the chair configuration is isolated, whereas the boat configuration allows one-dimensional deformations which form a circle in the configuration spa…
A hybrid RL-Bayesian search configures machine learning pipelines efficiently.
problem Optimizing hyper-parameters in a hierarchical conditional space.
method Combines Reinforcement Learning and Bayesian Optimization.
result Outperforms state-of-the-art methods in pipeline optimization.
Bayesian optimization (BO) aims to minimize a given blackbox function using a model that is updated whenever new evidence about the function becomes available. Here, we address the problem of BO under partially right-censored response data, where in some evaluations we only obtain a lower bound on the function value. T…
Role mining tackles the problem of finding a role-based access control (RBAC) configuration, given an access-control matrix assigning users to access permissions as input. Most role mining approaches work by constructing a large set of candidate roles and use a greedy selection strategy to iteratively pick a small subs…
Study shows configuration spaces' homological dimension increases monotonically.
problem Understanding the homological properties of configuration spaces of manifolds.
method Analyzing the homological monotonicity of unordered configuration spaces of manifolds.
result Homological dimension of configuration spaces increases monotonically in each degree.
Budgeted hyper-parameter tuning algorithm improves performance.
problem Optimizing hyper-parameters with resource constraints.
method Sequential decision making, Bayesian model, action-value function.
result Superior performance across various budgets.
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
problem Optimizing black-box functions with noisy, expensive evaluations and hyperparameter tuning.
method Exploring heuristics to automatically tune acquisition functions in Bayesian Optimization.
result Demonstrates effectiveness of heuristics in automatic Bayesian Optimization.
Study improves communication efficiency in RIS-assisted downlink communication.
problem Improving performance of RIS-aided downlink communication over heterogeneous designs.
method Distributed learning with distributionally robust optimization.
result Our algorithm achieves 50% fewer communication rounds for similar worst-case performance.