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.
Dropout: a simple way to prevent neural networks from overfitting.The Journal of Machine Learning Research, 15(1):1929–1958, 2014
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Augmenting self-attention with persistent memory vectors allows removal of feed-forward layers from Transformers without degrading performance on character and word level language modeling benchmarks.
RL agents fail dangerously on unseen environments; ensembles reduce catastrophes in gridworld but not CoinRun, with uncertainty enabling intervention prediction.
Structured dropout improves confidence calibration in CNNs by promoting ensemble diversity, with empirical support on SVHN, CIFAR-10, CIFAR-100 and in Bayesian active learning.
citing papers explorer
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MIPaaL: Mixed Integer Program as a Layer
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.
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Augmenting Self-attention with Persistent Memory
Augmenting self-attention with persistent memory vectors allows removal of feed-forward layers from Transformers without degrading performance on character and word level language modeling benchmarks.
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Generalizing from a few environments in safety-critical reinforcement learning
RL agents fail dangerously on unseen environments; ensembles reduce catastrophes in gridworld but not CoinRun, with uncertainty enabling intervention prediction.
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Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
Structured dropout improves confidence calibration in CNNs by promoting ensemble diversity, with empirical support on SVHN, CIFAR-10, CIFAR-100 and in Bayesian active learning.