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Found 6 papers, 3 papers with code
MORBDD: Multiobjective Restricted Binary Decision Diagrams by Learning to Sparsify
We focus on binary decision diagrams (BDDs) which first construct a graph that represents all feasible solutions to the problem and then traverse the graph to extract the Pareto frontier.
LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams
We show how the configuration space can be efficiently explored using black-box optimization, circumventing the curse of dimensionality (in the number of variables and objectives), and finding good orderings that reduce the PF enumeration time.
Neur2SP: Neural Two-Stage Stochastic Programming
Stochastic Programming is a powerful modeling framework for decision-making under uncertainty.
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages.
Correlated discrete data generation using adversarial training
Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection.
KELT-19Ab: A P~4.6 Day Hot Jupiter Transiting a Likely Am Star with a Distant Stellar Companion
We confirm the planetary nature of the companion via a combination of radial velocities, which limit the mass to $< 4. 1\,\mathrm{M_J}$ $(3\sigma)$, and a clear Doppler tomography signal, which indicates a retrograde projected spin-orbit misalignment of $\lambda = -179. 7^{+3. 7}_{-3. 8}$ degrees.
Earth and Planetary Astrophysics
