MIPaaL differentiates through mixed integer programs via cutting planes to enable decision-focused learning for general MIPs, outperforming two-stage prediction-plus-optimization and LP-relaxation baselines on real-world domains.
Automatic differentiation in PyTorch
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RL agents fail dangerously on unseen environments; ensembles reduce catastrophes in gridworld but not CoinRun, with uncertainty enabling intervention prediction.
RL policies decompose into information-regularized primitives that compete by requesting state information amounts, with the greediest one acting, yielding better generalization than flat or hierarchical baselines.
Virtual KITTI 2 supplies synthetic clones of real KITTI driving sequences with added weather and camera variants and multi-modal ground-truth annotations for autonomous driving vision research.
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Virtual KITTI 2
Virtual KITTI 2 supplies synthetic clones of real KITTI driving sequences with added weather and camera variants and multi-modal ground-truth annotations for autonomous driving vision research.