Search Results for author:
Found 10 papers, 4 papers with code
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
In this work, we investigate the properties of permutation equivariance and invariance in GNNs, particularly in relation to the inherent symmetry of ILP formulations.
An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
Unlike the standard learning-to-optimize framework that requires optimization solutions generated by solvers, our unsupervised method adjusts the network weights directly from the evaluation of the primal-dual gap.
NeuroLifting: Neural Inference on Markov Random Fields at Scale
Inference in large-scale Markov Random Fields (MRFs) is a critical yet challenging task, traditionally approached through approximate methods like belief propagation and mean field, or exact methods such as the Toulbar2 solver.
Stackelberg reinsurance and premium decisions with MV criterion and irreversibility
We study a reinsurance Stackelberg game in which both the insurer and the reinsurer adopt the mean-variance (abbr.
Consumption-investment decisions with endogenous reference point and drawdown constraint
We propose a consumption-investment decision model where past consumption peak $h$ plays a crucial role.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
Bayesian Methods for the Analysis of Early-Phase Oncology Basket Trials with Information Borrowing across Cancer Types
We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach and Liu's two-stage approach.
Applications
A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards
The integration and application of maxcombo tests in interim analyses often require extensive simulation studies.
Methodology
FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation
With a novel fully-trained generative network, FTGAN can synthesize higher-quality images and urge the outputs of the FTGAN are more relevant to the input sentences.
Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators
For demonstration, we apply the ensemble-based learning framework and the integrated, ensemble-based data assimilation framework to a supervised learning problem and a data assimilation problem with an imperfect forward simulator, respectively.
