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Matrix Compression via Randomized Low Rank and Low Precision Factorization
Neural Information Processing Systems
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.
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Matrix Compression via Randomized Low Rank and Low Precision Factorization
Neural Information Processing Systems
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.
Country:
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Washington > King County > Seattle (0.04)
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Predict-then-Calibrate: A New Perspective of Robust Contextual LP
Neural Information Processing Systems
The idea is to first develop a prediction model without concern for the downstream risk profile or robustness guarantee, and then utilize calibration (or recalibration) methods to quantify the uncertainty of the prediction.
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