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

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

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.

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…

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…

This paper proves that no single-pass semi-streaming algorithm can achieve a better-than-half approximation to the maximum matching problem, implying the optimality of the naive greedy algorithm.

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…

This paper presents the first application of thermodynamic computing to mRNA codon optimization in pharmaceutical R&D, achieving significant energy savings compared to conventional GPUs.

This paper introduces DQAOA-GPT, a hybrid framework that integrates DQAOA and GPT-based quantum circuit generation for solving combinatorial optimization problems, reducing computational cost and main…

The paper proposes ANoCo, a training-free method that detects visual anomalies by quantifying how much a query patch deviates from the structure of a fixed normal feature manifold using graph Laplacia…

The paper proposes a novel temporal and structural credit assignment framework to efficiently optimize multi-agent LLM systems by decomposing the error signal and using targeted, discrete gradient upd…

The paper proposes a semi-relaxed Gromov-Wasserstein objective to estimate the latent connectivity structure of large-scale networks, achieving statistically consistent and efficient recovery of the u…

The paper proposes GRiD, a novel framework that uses a two-phase training strategy (supervised pre-training and RL fine-tuning) to discover complex, graph-like rules for knowledge graph reasoning, ove…

The paper introduces CHECKMATE, a novel framework that uses code evolution to automatically generate and optimize algorithms for complex combinatorial problems, outperforming state-of-the-art solvers.

This paper presents AlphaRoute, a multi-objective adaptive search framework for VLSI global routing using Large Language Models as semantic policy optimizers.

This paper introduces Neural Certificate Pricing (NCP), an unsupervised learning framework that exploits the asymmetry between certifiable discrete structures and structural feasibility in combinatori…

This paper introduces a quantum optimization framework using QAOA to perform Subgroup Discovery for network intrusion detection, demonstrating that quantum methods can find complex feature interaction…