RLHO uses RL to generate better initial solutions for heuristic optimization.
problem High sample complexity in generating initial solutions for combinatorial optimization problems.
method RLHO framework that augments heuristic algorithms with RL to generate better initial solutions.
result RLHO outperforms baseline methods on bin packing problem.
Survey on minimal penalty algorithms and slope heuristics.
problem Choosing optimal multiplicative constants from data.
method Minimal penalty and slope heuristics approach.
result Slope heuristics performs almost as well as residual-based estimators.
New algorithms for private data synthesis using heuristics.
problem Private data synthesis for complex functions.
method Developed algorithms using non-private oracles and certifiable heuristics.
result Efficient private data synthesis for broad classes of functions.
New methods show less biased link prediction than traditional heuristics.
problem Systematic biases in link prediction methods.
method Comparison of heuristic and graph embedding based methods.
result Graph embedding methods show less biased results than heuristics.
Two heuristics solve dynamic multiple travelling salesmen problems.
problem Dynamic routing with unknown customers.
method Balanced dynamic closest vehicle heuristic and balanced dynamic assignment vehicle heuristic.
result Continuous approximation models for strategic dynamic routing.
A new clustering method combines Kalman filtering with K-Means for better results.
problem Improving clustering efficiency and accuracy in data mining.
method Proposes a new clustering algorithm (HKA-K) that integrates Kalman filtering and K-Means.
result HKA-K outperforms other hybrid meta-heuristic clustering approaches on UCI datasets.
Study combines A* and DNN for better pathfinding.
problem Limited applicability of A* in domains without good heuristics.
method Trains a DNN to represent heuristic for A* and integrates it.
result Significantly better performance in driving simulation.
This paper investigates Shampoo's heuristics and decouples preconditioner updates.
problem Improving Shampoo's heuristics for training neural networks.
method Decomposing preconditioner updates, correcting eigenvalues, and adapting eigenbasis computation frequency.
result Principled techniques to remove Shampoo's heuristics and improve training algorithms.
This paper optimizes portfolio rebalancing under uncertain security returns using meta-heuristic algorithms.
problem Optimizing portfolio rebalancing under uncertain security returns with transaction costs.
method Meta-heuristic algorithms (genetic algorithm) for solving the portfolio rebalancing problem.
result Meta-heuristic algorithms provide better results than global optimization solvers for portfolio rebalancing under uncertainty.
The report evaluates heuristics for learning timescale graphical event models.
problem Lack of heuristics for determining hyper-parameters in timescale graphical event models.
method Proposed and evaluated different heuristics for hyper-parameter determination and refined an existing distance measure.
result Conclusions about the applicability of different heuristics on synthetic data.
A new algorithm improves efficiency and robustness of heuristic optimization in simulation-based problems.
problem Optimizing input parameters for stochastic simulation-based optimization.
method Reactive sample size algorithm based on parametric tests and indifference-zone selection.
result The reactive method improves efficiency and robustness of heuristic optimization techniques.
RL improves combinatorial optimization by automating heuristic search.
problem Hard combinatorial optimization problems with suboptimal solutions.
method Training RL agents to solve these problems.
result RL can outperform traditional algorithms in solving complex problems.
The paper develops algorithms for Boolean matrix factorization using IP and heuristics.
problem Approximating binary input matrices as products of smaller binary factors.
method Alternating optimization with integer programming and greedy/local-search heuristics.
result Proposed methods improve scalability and performance compared to existing techniques.
Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents in…
This paper evaluates heuristics and hyperparameters in weight-sharing NAS methods.
problem Improving the performance of weight-sharing NAS methods.
method Systematic evaluation of heuristics and hyperparameters in weight-sharing NAS algorithms.
result Some heuristics negatively impact super-net and stand-alone performance correlation.
A new method learns link prediction heuristics from local subgraphs using GNN.
problem Link prediction in network-structured data.
method Developed a novel γ-decaying heuristic theory and a GNN-based algorithm to learn heuristics from local subgraphs.
result Unprecedented performance in link prediction across various problems.
The paper introduces a new language for semi-supervised learning tasks.
problem Handling semi-supervised learning with declarative constraints.
method Developed a declarative language for modeling both supervised and semi-supervised learning tasks, including heuristics and combining multiple heuristics.
result Improved performance on relation-extraction tasks for real-world domains.
Optimal prototypes found for challenging pathological geometries.
problem Finding optimal prototypes for pathological geometries is challenging.
method Analytical and heuristic algorithms for finding nearly-optimal prototypes.
result Optimal prototypes can be found analytically for challenging geometries.
The paper uses Column Generation for faster construction of binary decision trees.
problem Constructing efficient univariate binary decision trees for classification tasks.
method Proposes an Integer Linear Programming (ILP) formulation and solves it via Column Generation based heuristic.
result The approach is competitive with state-of-the-art ILP-based algorithms and can handle large datasets.
A new heuristic strategy improves sparse BSS performance.
problem Efficiently solving non-convex penalized matrix factorization problems.
method Combining heuristic approach with PALM algorithm.
result Significantly improved separation results on astrophysical data.
A new algorithm BE converts dendrograms to 2D scatter plots.
problem Preserving hierarchical structures in high-dimensional data.
method Converts a dendrogram to a 2D scatter plot.
result Moderately preserves the original hierarchical structures.
This work explores how to balance rule consistency and coverage for multi-label classification.
problem Balancing rule consistency and coverage for effective multi-label classification.
method Empirical study of rule learning heuristics in multi-label classification.
result The choice of heuristic for multi-label classification is crucial and depends on the performance measure.
A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.
problem Optimizing SVR parameters for reliable regression performance on small sample sizes.
method Golden Sine Algorithm (GSA) for parameter tuning of SVR.
result The GSA-based SVR outperforms eleven other meta-heuristics in terms of accuracy and computing time.
A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a criteria which includes a χ2 test and a regularization. After a relatively s…
This paper proposes a new Nystrom-based clustering algorithm for large-scale data.
problem Spectral clustering's high computational complexity for large-scale data.
method Centroid Minimum Sum of Squared Similarities (CMS3) sampling procedure with eigen spectrum shape heuristic.
result Competitive low-rank approximations in test datasets compared to state-of-the-art methods.
This paper optimizes UAV and FeICIC locations in a three-tier LTE-Advanced network.
problem Optimizing UAV and FeICIC locations in a three-tier LTE-Advanced network.
method Integrates UAVs as both UE and BS in LTE-Advanced HetNet, uses CRE, ICIC, 3D beamforming, and genetic algorithms for optimization.
result Heuristic algorithms outperform brute-force techniques in achieving better 5pSE and coverage probability.
A new model tracks indices without rebalancing, solving NP-hard problems.
problem Tracking indices without rebalancing and minimizing deviations.
method Metaheuristic algorithms and local branching for solving mixed integer linear programming.
result The heuristic generates portfolios that outperform commercial solvers in both in-sample and out-of-sample data.
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-HARD, and for most practical problems there are no efficient algorithms that…
Paper predicts player retention in mobile games quickly.
problem Predicting player retention in free-to-play mobile games.
method Heuristic modeling to build simple retention prediction rules.
result Heuristic approach achieves comparable performance to common algorithms.
Heuristic tool estimates lactate threshold for easier training decisions.
problem Improving lactate threshold estimation for recreational runners.
method Formalized lactate threshold principles, iterative methodology, heuristic approach.
result Heuristic %60 of 'endurance running speed reserve' is reliable and accessible.
PAC-Bayes analysis explains sentence vector learning from unlabeled data.
problem Understanding and improving sentence vector learning from unlabeled data.
method PAC-Bayes bound analysis for transfer learning.
result Simple heuristics and new algorithms derived from PAC-Bayes analysis.
GCOMB learns heuristics for large graphs efficiently.
problem Scalability and practical constraints in graph problem solving.
method GCOMB uses a Graph Convolutional Network (GCN) with Q-learning for efficient heuristic discovery.
result GCOMB is 100 times faster and marginally better than state-of-the-art algorithms.
Regina is a software package for studying 3-manifold triangulations and normal surfaces. It includes a graphical user interface and Python bindings, and also supports angle structures, census enumeration, combinatorial recognition of triangulations, and high-level functions such as 3-sphere recognition, unknot recognit…
Partial reinitialization improves heuristic optimizers by retaining previous information.
problem Local optima in heuristic optimizers.
method Partial reinitialization of sub-sets of variables.
result Significant improvements in solution quality.
A new heuristic LM algorithm improves k-segmentation accuracy with less computation.
problem Efficiently segmenting large video streams into meaningful piecewise-linear segments.
method Inspired by Lloyd's and Lloyd-Max algorithms, LM algorithm iteratively minimizes a cost function.
result LM algorithm achieves competitive accuracy with exact methods at a fraction of the computational cost.
Deep neural networks approximate solutions to NP-hard problems.
problem Approximating solutions to NP-hard combinatorial optimization problems.
method Homotopic recurrent neural networks combined with reinforcement learning.
result Homotopic RNNs improve the quality of solutions compared to vanilla RNNs.
Developed Forex trading heuristics with high profit potential.
problem Reduced trade opportunities due to technical indicator values.
method Machine learning simulation of 10 years of Forex data.
result Optimized trade parameters for 118 pips daily profit.
Deep RL learns 2-opt heuristics to improve TSP solutions.
problem Improving TSP solutions beyond initial heuristics.
method Deep reinforcement learning to learn 2-opt operations.
result Learned policies improve solutions faster than previous methods.
Graphical heuristic reduces and partitions large datasets for faster supervised training.
problem Training large datasets for classification tasks.
method Clustering and information graph construction for dataset reduction and partitioning.
result Significant speed-up in training run-time without compromising prediction accuracy.
New heuristics for parallel Bayesian optimization.
problem Challenges in parallel and scalable Bayesian optimization.
method Review and propose practical heuristic algorithms.
result Simple, heuristic algorithms for parallel Bayesian optimization.
We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, w…
GE finds failures in autonomous systems without domain heuristics.
problem Finding failures in autonomous systems without domain-specific heuristics.
method Adaptive stress testing using go-explore (GE) algorithm.
result GE finds failures in scenarios other RL techniques cannot solve.
New neural network design improves performance.
problem Improving neural network performance.
method Guided by traditional algorithm design, two heuristics are presented to incorporate additional weights.
result Performance of the new networks can match or exceed traditional neural networks.
Model-based Bayesian Reinforcement Learning (BRL) allows a found formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL suffer from such a combinatorial explosion that a large body of work r…
Dynamic classifier chains improve multi-label classification efficiency.
problem Building efficient multi-label classification models.
method Dynamic ensemble of chain classifiers using Naive Bayes and nearest neighbor approaches, with heuristic for label order optimization.
result The proposed dynamic chain model based on Naive Bayes classifier and heuristic is efficient for multi-label classification.
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring…
Improves heuristics for routing problems using attention models.
problem Improving heuristics for combinatorial optimization problems, especially for routing problems.
method Proposed a model based on attention layers and trained it using REINFORCE with a simple greedy rollout.
result Significantly improved results for TSP and other routing problems, close to optimal or specialized algorithms.
Heuristic algorithm for portfolio optimization reduces solve times to milliseconds.
problem Mean-variance portfolio optimization with various constraints.
method Alternating Direction Method of Multipliers (ADMM).
result Achieves performance bounds and solves problems in milliseconds.