A game theory study on optimal hiding and searching strategies in discrete locations.
problem Optimal hiding and searching strategies in a two-person zero-sum game between a hider and a searcher.
method Proved the existence of optimal strategies, developed an algorithm to compute them, and compared with a simple strategy.
result Optimal hiding strategy involves hiding in each location with nonzero probability, and optimal searching strategy can be constructed with up to n simple sequences.
A new search-control strategy improves Dyna's efficiency.
problem Improving sample efficiency in model-based reinforcement learning.
method Proposes a novel search-control strategy by sampling high frequency regions of the value function.
result Empirically shows that high frequency regions require more samples to approximate, suggesting a better search-control strategy.
PDP framework learns CSP solvers without explicit search strategy.
problem Learning effective search strategies for CSP solvers.
method Proposes a generic neural framework based on propagation, decimation, and prediction.
result Demonstrates effectiveness in SAT solving compared to neural and state-of-the-art baselines.
Paper proposes a new evaluation method for NAS search phase.
problem NAS search phase effectiveness not well evaluated.
method Compare NAS solutions with random selection; evaluate weight sharing strategy.
result State-of-the-art NAS algorithms perform similarly to random selection.
Survey of automated neural architecture search methods.
problem Manual development of neural architectures is time-consuming and error-prone.
method Categorizes existing automated neural architecture search methods.
result Growing interest in automated methods due to manual development's limitations.
A new framework generates large hierarchical search spaces for neural architectures.
problem Discovering neural architectures from simple blocks is hard.
method Context-free grammars for a unified, scalable search space.
result Efficiently searches over complete architectures, outperforming existing methods.
Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resourc…
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
Optimizes Airbnb pricing to increase revenue and reduce booking regret.
problem Maximizing revenue in an online marketplace with competing products.
method Two-stage model to price retrieved items based on learned distributions of their values.
result Improves revenue and booking regret by at least +20% and +55% respectively.
Casting machine learning as a type of search, we demonstrate that the proportion of problems that are favorable for a fixed algorithm is strictly bounded, such that no single algorithm can perform well over a large fraction of them. Our results explain why we must either continue to develop new learning methods year af…
Introduces GOLS-I for automatically determining step sizes in neural networks without surrogates.
problem Determining step sizes in neural network training using predetermined rules or expensive global optimization strategies.
method Gradient-Only Line Searches (GOLS-I) that are Inexact.
result GOLS-I is a competitive strategy for reliably determining step sizes in stochastic loss functions.
A new algorithm, Regular Tree Search, tackles non-convex simulation optimization problems.
problem Non-convex objective functions in simulation optimization.
method Integrates adaptive sampling with recursive partitioning of the search space.
result Proves global convergence and reliably identifies the global optimum.
RandomNet uses random search to design neural architectures without much human intervention.
problem Designing neural architectures without excessive human intervention.
method Random search strategy for multimodal neural architecture design.
result RandomNet performs close to state-of-the-art on AV-MNIST with minimal human supervision.
Analyzes retail trends from sales, search, and reviews.
problem Optimizing inventory and marketing for better customer satisfaction.
method Historical sales data, search trends, and customer reviews.
result Identifies patterns and trending products for retailers.
Adaptive contrastive search improves text generation quality and diversity.
problem Decoding high-quality text from language model outputs.
method Adaptive contrastive search incorporating uncertainty-guided degeneration penalty.
result Enhanced text generation quality and diversity across different models and datasets.
Automated framework forecasts correlated time series in minutes.
problem Forecasting correlated time series with high accuracy and efficiency.
method Data-driven iterative pruning, zero-shot search, fast parameter adaptation.
result Framework achieves state-of-the-art accuracy and is faster than existing methods.
Ansor generates high-performance tensor programs for deep learning.
problem Generating high-performance tensor programs for deep learning on various hardware platforms is challenging.
method Ansor uses a hierarchical representation of the search space, sampling programs, and evolutionary search with a learned cost model to find high-performance programs.
result Ansor improves deep neural network execution performance up to 3.8x on Intel CPU, 2.6x on ARM CPU, and 1.7x on NVIDIA GPU.
Optimizes neural architecture search to generate novel lightweight models.
problem Over-reliance on expert knowledge limits NAS to local optima, preventing architectural breakthroughs.
method Casts NAS as an optimization problem, introduces a hierarchical graph-based search space, and uses Bayesian optimization.
result Generates extremely lightweight yet competitive models on six benchmark datasets.
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute t…
BS-NAS broadens and shrinks search space for optimal neural architectures.
problem Suboptimal channel numbers and model averaging effects in One-Shot NAS methods.
method Broadening with spring block for channel search, shrinking with underperforming operations removal, evolutionary algorithm for optimal architecture search.
result BS-NAS achieves state-of-the-art performance on ImageNet.
Bayesian optimization tackles unknown search spaces with automatic expansion.
problem Bayesian optimization in unknown search spaces is challenging.
method Proposes a systematic volume expansion strategy to find points close to the objective function maximum without specifying parameters.
result Derives analytic expressions for expansion triggers and sizes, achieving epsilon-accuracy after a finite number of iterations.
Investment strategy optimized in markets with transaction costs and search delays.
problem Maximizing wealth in an illiquid market with transaction costs and search frictions.
method Characterized no-trade region and provided asymptotic expansions of value function for small transaction costs.
result The effects of transaction costs are more pronounced in illiquid markets.
Improved causal discovery methods for large graphs without strict assumptions.
problem Sub-optimal solutions due to faithfulness assumption violations.
method Super-structure estimation and local search strategies.
result The proposed method scales to hundreds of nodes with high accuracy.
New method finds best neural architecture during learning.
problem Active learning of deep neural networks with known architectures.
method Neural architecture search during active learning.
result Outperforms fixed architecture active learning.
Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with…
A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.
problem Optimizing functions with mixed variable types (continuous, integer, categorical).
method Merges MCTS for categorical and GP for continuous variables, integrates UCTS search strategy, and dynamically selects kernels.
result Hybrid models outperform traditional methods in Bayesian optimization.
AI methods broaden signal discovery in scientific data.
problem Limited coverage of possible signals in model-dependent searches.
method Model-agnostic AI strategies for broad exploration.
result Enhanced discovery potential in experimental science.
New method optimizes BO by learning search spaces from past evaluations.
problem Optimizing expensive black-box functions efficiently.
method Automatically designs BO search space from historical data.
result Significant boost in BO performance by reducing search space size.
NAS for financial time series forecasts using chain-structured architectures.
problem Optimizing neural architectures for financial time series forecasting.
method Comparison of three NAS strategies (Bayesian optimization, hyperband, reinforcement learning) on chain-structured search spaces for simple and complex architectures.
result Bayesian optimization and hyperband outperform other strategies, and RNN and 1D CNN perform best among architectures.
Survey on algorithms for quick robot learning.
problem Efficiently learn robot controllers with limited data.
method Leverage prior knowledge and data-driven models.
result Combining prior knowledge and surrogate models improves learning.
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.
This paper improves keyword recommendation for sponsored search using deep reinforcement learning.
problem Selecting keywords from given candidates considering internal and external competitions.
method Solves the combinatorial optimization problem of keyword recommendations with a modified pointer network structure trained in a deep reinforcement learning framework.
result Remarkable improvements in performance observed both offline and online.
NPENAS improves neural architecture search efficiency and accuracy.
problem Efficient and accurate neural architecture search (NAS) for minimizing search costs.
method Proposes NPENAS, a neural predictor guided evolutionary algorithm that enhances exploration ability of evolutionary algorithms.
result NPENAS-BO and NPENAS-NP outperform existing NAS algorithms on NASBench-201, NASBench-101, and DARTS.
AgABC improves ABC algorithm by balancing exploration and exploitation.
problem Balancing global and local search abilities in ABC algorithm.
method Divide population into groups and assign different search strategies to members.
result Proposed AgABC algorithm outperforms other algorithms in accuracy and stability.
GLSearch uses GNN to learn efficient search strategies for finding large common subgraphs.
problem Finding the Maximum Common Subgraph (MCS) between two graphs is NP-hard and hard to solve efficiently.
method GLSearch combines GNN and DQN to learn optimal node pairs for expansion in a branch and bound algorithm.
result GLSearch finds significantly larger common subgraphs than heuristic search methods given the same computation budget.
Ortho-MADS optimizes SVM hyperparameters for better accuracy.
problem Optimizing hyperparameters for SVM with Gaussian kernel.
method Deterministic Mesh Adaptive Direct Search (MADS) with orthogonal directions (Ortho-MADS).
result Ortho-MADS consistently finds comparable or better solutions than other methods.
Novel method for high-dimensional BO using CMA to define local regions.
problem Challenges in applying BO to high-dimensional optimization problems.
method CMA strategy to learn search distribution and define local regions.
result Our method outperforms existing techniques on various benchmarks.
AI uses language models to find instrumental variables quickly.
problem Finding valid instrumental variables is a challenging and heuristic process.
method Uses large language models to search for new instrumental variables through narratives and counterfactual reasoning.
result Demonstrates the effectiveness of multi-step and role-playing prompting strategies for LLMs.
This paper explores LETOR for E-Com search, addressing practical challenges and reporting key findings.
problem Applying LETOR to E-Com search presents unique challenges.
method Investigates practical challenges in LETOR for E-Com search, including feature representation, relevance judgments, and feedback signal exploitation.
result LETOR methods can effectively optimize combinations of popularity-based and relevance-based features, and order rate is the most robust training objective.
Parallelizes MCTS for continuous domains using leaf and root parallelization.
problem Solving challenging tasks in continuous domains using MCTS.
method Extends existing parallelization strategies to continuous domains, focusing on leaf and root parallelization.
result Proposes two final selection strategies for continuous states in root parallelization.
We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based on given data. Most common learning algorithms feature a set of hyperparameters t…
Analyzes SVM classifier behavior with different parameters and data types.
problem Tuning SVM parameters for balanced and imbalanced data.
method Behavioral analysis of SVM with different parameters and data types, proposing a novel search algorithm.
result Proposed search algorithm reduces computational time and provides expected kernel function range.
Gradients help find global optima in complex functions.
problem Finding global optima in functions with many local minima.
method A principle for generating search directions from non-local quadratic approximants based on gradients.
result The proposed algorithm and CMA-ES perform better than random reinitialized BFGS.
Optimistic search speeds up change point detection in large datasets.
problem Efficiently detecting change points in large-scale data with high computational demands.
method Adaptive logarithmic queries to reduce evaluation complexity.
result Asymptotic minimax optimality and fast localization rates for change point detection.
SSNAS finds neural architectures without labeled data.
problem Limited labeled data for NAS.
method Self-supervised learning for NAS.
result Comparable results to supervised NAS with labeled data.
Improved genetic programming by optimizing mutation operators for continuous program search.
problem Small syntactic mutations in genetic programming can lead to unpredictable behavioral shifts.
method Learned a compact trading-strategy DSL, created a block-factorized embedding, and designed geometry-compiled mutation operators.
result Geometry-compiled mutation operators discover strong strategies using fewer evaluations and achieve higher Sharpe ratios.
A new framework for selecting base classes in multi-class classification boosts accuracy.
problem Selecting the base class in multi-class classification to improve accuracy.
method Introduces a unified framework with parameters (s,g,w) to search for the base class at each boosting iteration, improving computational efficiency. result Our framework can achieve better test accuracy than the exhaustive search strategy, providing a robust and reliable scheme.
Fast AutoAugment speeds up data augmentation search.
problem Efficiently searching for effective data augmentation policies.
method Density matching-based search strategy.
result Comparable performance with faster search time.