Algorithms

The paper introduces a diffusion-based uncertainty model for robust optimization on graphs, showing that the resulting computational complexity depends critically on the interaction between the uncert…

This paper demonstrates that treewidth dynamic programming (TDP) can be used to enhance the performance of various graph combinatorial optimization algorithms.

The paper compares four methods for scalable nonlinear causal discovery and proposes a new method, SPADE, that improves scalability and accuracy.

This paper experimentally evaluates various planar graph drawing algorithms and compares their performance in terms of visual properties.

This paper proposes a Genetic Algorithm with constraint repair mechanism for optimally allocating educational resources to high school mathematics students under study time constraints.

The paper proposes a SINR-aware greedy algorithm for base station deployment in IoT sensor networks to achieve near-optimal coverage while reducing computational effort.

The paper analyzes the sample complexity of contextual bandits in the $s$-sparse setting, achieving optimal sample bounds for identifying an $\epsilon$-optimal policy.

This paper solves the tournament isomorphism problem for tournaments of bounded VC dimension in time n^(O(dlog d)), enabling polynomial-time computation of their automorphism groups.

The paper proposes the LLM-GNN Soft Prompt Framework (LGSPF) to enhance fraud detection by directly integrating graph structure and semantic information into LLMs, achieving state-of-the-art performan…

This paper addresses the problem of inferring a directed network from nodal measurements using graph convolutional filters and identifies the diffusion filter and network topology.

This paper proposes a Bayesian optimization method for wavelength selection in near-infrared spectroscopy to improve prediction accuracy and yield consistent wavelength regions.

This paper improves sparse variational Gaussian processes by introducing a scalable algorithm to select blocks that tighten the variational objective.

The paper introduces and explores Truly Linear FPT (TLFPT), a complexity class defined by $O(n) + f(k)$, demonstrating that it is a strict subset of standard Linear FPT and providing new algorithms fo…

The paper addresses limitations in the Linear Ordering Problem (LOP) by introducing a novel benchmark suite derived from current economic data and an algorithmic scheme to generate diverse, high-quali…

A data-driven framework generates robot hands from human demonstrations using a simple control policy and reinforcement learning.

The paper provides a quantitative characterization of size-oblivious one-sided testability in dense graphs using hypergraph containers, resolving a question in modern combinatorics.

This paper learns a mapping from problem instances to high-quality QAOA angles, replacing the need for per-instance optimization.

This paper proposes a new iterated greedy algorithm with a multi-operator approach and a large language model-assisted framework to enhance exploration and improve performance on the permutation flow…

This paper presents a randomized algorithm that runs in $ ilde{O}(n^2)$ time and, with high probability, guarantees a 2-approximation for all pairs at distance at least $c$ in an undirected, unweighte…

This paper shows that finding the minimum geodetic set in chordal graphs is fixed parameter tractable, implying a polynomial-time algorithm for k-trees. It also proves that the problem is NP-hard on i…