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Found 5 papers, 0 papers with code
Leveraging Large Language Models for Multimodal Search
Multimodal search has become increasingly important in providing users with a natural and effective way to ex-press their search intentions.
Learning Best-in-Class Policies for the Predict-then-Optimize Framework
This is the first such result in misspecified settings and we provide numerical evidence confirming our PG losses substantively outperform existing proposals when the underlying model is misspecified and the noise is not centrally symmetric.
Efficient LDPC Decoding using Physical Computation
Due to 5G deployment, there is significant interest in LDPC decoding.
Augmented Electronic Ising Machine as an Effective SAT Solver
With experimental analyses, we show that such an Augmented Ising Machine for SAT (AIMS), outperforms state-of-the-art software-based, GPU-based and conventional hardware SAT solvers by orders of magnitude.
Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization
Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization. Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance.
