1 citations · 1 across the 3 of their papers we have counts for
4 papers
Parallel Tempering for Diffusion-Based Combinatorial Optimization
Arman Mielke, Uwe Bauknecht, Thilo Strauss +1
Discrete diffusion models have emerged as a powerful paradigm for solving combinatorial optimization (CO) problems on graphs by learning to sample high-quality solutions. A common…
GraIP: A Benchmarking Framework For Neural Graph Inverse Problems
Semih Cantürk, Andrei Manolache, Arman Mielke +5
A wide range of graph learning tasks, such as structure discovery, temporal graph analysis, and combinatorial optimization, focus on inferring graph structures from data, rather th…
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization
Arman Mielke, Uwe Bauknecht, Thilo Strauss +1
Combinatorial optimization (CO) problems arise across a broad spectrum of domains, including medicine, logistics, and manufacturing. While exact solutions are often computationally…