COMBO optimizes Bayesian Optimization for combinatorial search spaces.
problem Optimizing objectives on combinatorial search spaces with high-order interactions.
method COMBO uses a combinatorial graph and ARD diffusion kernel with Horseshoe prior for efficient modeling and variable selection.
result COMBO outperforms state-of-the-art methods consistently across various benchmarks.
New method learns search policies by inspecting and improving past roll-outs.
problem Learning good search policies for complex combinatorial spaces.
method Retrospective imitation learning, improving policy through past roll-outs.
result Policy can iteratively scale up to larger problems.
Unified framework for combinatorial and rounding algorithms in experimental design.
problem Designing and analyzing combinatorial and rounding algorithms for experimental design problems.
method Local search framework for combinatorial algorithms and regret minimization framework for rounding algorithms.
result Unified approach to match and improve all known results in D/A/E-design and obtain new results in unknown settings.
Paper uses CMAB to improve NAS efficiency and accuracy.
problem Improving efficiency and accuracy of NAS for DNNs.
method Formulated NAS as CMAB, used Nested Monte-Carlo Search.
result Discovered cell structure achieves comparable accuracy to state-of-the-art, 20x faster.
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.
We investigate the probability of detecting combinatorial Morse flows on a simplicial complex via a random search. We prove that it is really small, in a quantifiable way.
A new method uses heat diffusion to efficiently solve combinatorial optimization problems.
problem Challenges in combinatorial optimization due to discrete nature and limited search scope.
method Transforming the target function through heat diffusion to enable information flow and more efficient navigation.
result Superior performance across various combinatorial optimization problems.
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.
CrossBeam learns to search more efficiently in program synthesis.
problem Efficiently searching through vast program spaces.
method Trains a neural model to guide program synthesis, combining previously explored programs.
result CrossBeam explores much smaller portions of the program space compared to state-of-the-art methods.
Bayesian optimization method for permutations accelerates combinatorial search.
problem Optimizing expensive-to-evaluate objectives on permutation problems.
method LAW2ORDER, a batch Bayesian optimization method based on the acquisition weighted kernel.
result LAW2ORDER achieves sublinear batch cumulative regret, demonstrating accelerated search.
VCSMC improves efficiency in Bayesian phylogenetic inference.
problem Inefficient exploration of phylogenetic state space.
method Variational Combinatorial Sequential Monte Carlo (VCSMC) and nested CSMC.
result VCSMC and VNCSMC explore higher probability spaces efficiently.
Improves neural network search in combinatorial spaces of mathematical symbols.
problem Early commitment and initialization bias limit exploration in neural network search.
method Entropy regularization and distribution initialization methods.
result Improves performance, increases sample efficiency, lowers solution complexity.
Neural model with parameterized algorithms improves graph CO problem solving.
problem Solving NP-hard graph combinatorial optimization problems efficiently and accurately.
method Combining neural models and parameterized algorithms to identify and handle hard and easy parts of CO instances.
result Framework produces superior solution quality and out-of-distribution generalization.
New algorithm optimizes complex combinatorial structures efficiently.
problem Optimizing expensive functions over combinatorial structures.
method Adaptive, scalable model using semidefinite programming.
result Consistently outperforms other methods in evaluations.
ECO-DQN uses RL to improve combinatorial optimization solutions by continuous exploration.
problem Finding optimal subsets or orderings in NP-hard combinatorial optimization problems.
method ECO-DQN learns to explore continuously at test time, improving solutions incrementally.
result ECO-DQN achieves state-of-the-art performance on the Maximum Cut problem.
The paper solves fractional combinatorial flows for prescribed hyperbolic bordered surfaces.
problem Finding hyperbolic bordered surfaces with prescribed boundary lengths.
method Fractional combinatorial Calabi flow and generalized combinatorial Yamabe flow.
result The flows converge to a hyperbolic surface with prescribed boundary lengths.
New A* algorithm improves hierarchical clustering quality.
problem Improving hierarchical clustering quality in large search spaces.
method Combining A* search with a trellis data structure.
result Achieves higher quality results than baselines in particle physics and other benchmarks.
Automates supervised learning pipeline design with matrix and tensor factorization.
problem Designing effective supervised learning pipelines with many choices.
method Uses matrix and tensor factorization to model pipeline search space and develops greedy experiment design protocols.
result Demonstrates the effectiveness of the approach on real-world classification problems.
This paper proposes a new method to learn combinatorial patterns for airline crew pairing optimization.
problem Enhancing airline crew pairing optimization for large-scale, complex flight networks.
method Variational Graph Auto-Encoder for learning combinatorial patterns among flight-connection graphs.
result The proposed method generates new pairings for the optimizer, improving the efficacy of airline crew pairing optimization.
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
Uses RL to discover causal DAGs from data.
problem Discovering causal structure among variables.
method Reinforcement Learning to search for DAGs.
result Improved search ability and flexible scoring.
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and p…
Quantum algorithm finds extrema in discrete optimisation problems.
problem Finding extrema in discrete optimisation functions.
method Quantum unstructured search algorithm (QSERA) to map and find extrema.
result Quadratic speed-up over classical algorithms for discrete optimisation.
NeuRewriter learns to choose and rewrite heuristics in combinatorial problems.
problem Time-consuming tuning of heuristics in combinatorial optimization.
method NeuRewriter uses reinforcement learning to learn a policy for picking heuristics and rewriting solutions.
result NeuRewriter outperforms existing methods in various combinatorial tasks.
Via a computer search, Altshuler and Steinberg found that there are 1296 +1 combinatorial 3-manifolds on nine vertices, of which only one is non-sphere. This exceptional 3-manifold K93 triangulates the twisted S2-bundle over S1. It was first constructed by Walkup. In this paper, we present a computer-…
New clustering methods for binary data using combinatorial optimization.
problem Clustering binary data efficiently and effectively.
method Five new combinatorial optimization heuristics (SA, TA, TS, GA, ACO) applied to binary data.
result Simulated annealing performs exceptionally well compared to classical methods.
Ranked Reward algorithm improves bin packing performance.
problem Improving reinforcement learning for combinatorial optimization.
method Ranking rewards from self-play to create a relative performance metric.
result Ranked Reward algorithm outperforms other methods on bin packing problems.
Adapting neural networks to guide program optimization for better classifiers.
problem Learning differentiable programs with complex architectures.
method Formulating program optimization as a graph search problem, using neural networks as heuristic relaxations.
result Trained neural networks can guide combinatorial search for programmatic classifiers, improving accuracy and interpretability.
Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback. In a CMAB problem an agent pulls a combination of arms from a set {1,...,k} in each round, g…
DO uses DNNs to solve combinatorial optimisation problems.
problem Solving complex combinatorial optimisation problems.
method Combines evolutionary search with DNNs to learn and exploit problem structure.
result DO can solve problems like HTOP and MCparity that other algorithms cannot.
Bayesian optimization method tackles combinatorial spaces, scalable for large data.
problem Optimization over combinatorial categorical spaces in natural sciences.
method Combines variational optimization and continuous relaxations for gradient-based optimization.
result Method performs comparably to state-of-the-art methods while scaling well.
GFlowNets improve combinatorial optimization by efficiently sampling from solution spaces.
problem NP-hard combinatorial optimization problems with structured constraints.
method Design Markov decision processes and train conditional GFlowNets to sample solutions.
result GFlowNet policies find high-quality solutions efficiently on various CO tasks.
New model guarantees integer optimal solutions for combinatorial problems.
problem Finding optimal solutions for combinatorial problems with costly or noisy evaluations.
method Developed a surrogate model with integer-valued minima for combinatorial optimization.
result Outperforms other optimization algorithms on specific combinatorial problems.
This is a report on our ongoing research on a combinatorial approach to knot recognition, using coloring of knots by certain algebraic objects called quandles. The aim of the paper is to summarize the mathematical theory of knot coloring in a compact, accessible manner, and to show how to use it for computational purpo…
New method uses hyperbolic space for faster phylogenetic tree inference.
problem Inefficient Euclidean-based phylogenetic inference in high dimensions.
method Developed novel hyperbolic extensions of sequential search algorithms and variational inference methods.
result Improved speed, scalability and performance in phylogenetic inference.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
problem Expanding and reducing feature space in tabular learning becomes computationally expensive with increased dimensionality.
method SCOPE-FE controls the search space by regulating operator and feature-pair spaces, using OperatorProbing and FeatureClustering.
result SCOPE-FE reduces feature engineering time while maintaining competitive predictive performance.
Paper speeds up structured output prediction without sacrificing accuracy.
problem Computational inefficiency in predicting structured outputs.
method Learning to search approach to train a speedup classifier.
result Speedup classifier outperforms greedy search in terms of speed.
AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.
problem Designing optimal antigen-specific CDRH3 regions in antibody design due to combinatorial sequence space.
method Combinatorial Bayesian optimization framework with trust region for developability.
result AntBO designs CDRH3 regions with diverse biophysical properties and outperforms existing methods.
Learning-based BnB improves maximum common subgraph search efficiency.
problem Finding maximum common subgraphs efficiently.
method Inspired by reinforcement learning, a heuristic to reach tree leaves early.
result Significantly reduces search tree size and outperforms existing BnB algorithms.
GFN-SR uses deep learning to generate diverse mathematical expressions.
problem Symbolic regression to find best mathematical expressions.
method Traversing a DAG to generate expression trees sequentially with GFlowNet.
result GFN-SR outperforms other SR algorithms in noisy data.
VSD efficiently learns conditional distributions for combinatorial designs.
problem Learning conditional distributions for rare combinatorial designs.
method Variational Search Distributions (VSD) using variational inference.
result VSD outperforms existing methods on real sequence-design problems.
New algorithm for adaptive experimental design in scientific settings.
problem Identifying true positives while controlling false discoveries in adaptive experimental design.
method Provably sample efficient adaptive algorithm for FDR control.
result First provably sample efficient adaptive algorithm for adaptive experimental design.
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.
Model-free RL agents can learn effective planning without explicit model.
problem Challenging domains with combinatorial complexity.
method Model-free RL with inductive bias in neural networks.
result Model-free RL can exhibit planning characteristics without explicit model.
Ultra-fast search algorithm for trillion-scale corpora with semantic flexibility.
problem Efficiently searching over large natural language corpora with semantic variations.
method String matching based on suffix arrays, vector representation of words, dynamic corpus-aware pruning, fast exact lookup.
result Substantially lower search latency compared to existing methods on FineWeb-Edu corpus.
PDNAS optimizes GNN architectures for diverse datasets.
problem Inadequate adaptability and combinatorial search space in GNNs.
method Dual architecture search (micro- and macro-architectures) with gradient-based optimization.
result PDNAS finds deeper GNNs with better performance on diverse datasets.
Adaptive sampling improves finding good local optima in combinatorial optimization.
problem Finding good local optima in NP-hard combinatorial optimization problems.
method Derive a robust learning algorithm to adapt sampling distributions towards good local optima.
result Adaptive sampling outperforms related methods in recovering locally maximal cliques and k-medoid clustering.
Develops a new method for learning non-parametric DAGs using RKHS.
problem Challenges of learning non-parametric causal models with large combinatorial search space.
method Uses reproducing kernel Hilbert spaces (RKHS) and sparsity-inducing regularization terms based on partial derivatives to enforce acyclicity.
result Shows improved performance through simulations and data analyses.