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Learning to sample from intractable distributions over discrete sets without relying on corresponding training data is a central problem in a wide range of fields, including Combinatorial Optimization.
Stochastic variational inference via upper bound
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Finding near-optimal independent sets at scale
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Forward amortized inference for likelihood-free variational marginalization
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An optimal control perspective on diffusion-based generative modeling
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Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
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Gurobi Optimizer Reference Manual, 2023
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Improved sampling via learned diffusions
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Variational annealing on graphs for combinatorial optimization
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DIFUSCO: graph-based diffusion solvers for combinatorial optimization
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Unsupervised learning for combinatorial optimization needs meta learning
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Transport meets variational inference: Controlled monte carlo diffusions
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