112 citations · 507 across the 28 of their papers we have counts for
5 papers · 1 filter
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Shengjia Zhao, Michael P. Kim, Roshni Sahoo +2
When facing uncertainty, decision-makers want predictions they can trust. A machine learning provider can convey confidence to decision-makers by guaranteeing their predictions are…
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Xiang Wang, Chenwei Wu, Jason D. Lee +2
Over-parametrization is an important technique in training neural networks. In both theory and practice, training a larger network allows the optimization algorithm to avoid bad lo…
Individual Calibration with Randomized Forecasting
Shengjia Zhao, Tengyu Ma, Stefano Ermon
Machine learning applications often require calibrated predictions, e.g. a 90\% credible interval should contain the true outcome 90\% of the times. However, typical definitions of…
On the Performance of Thompson Sampling on Logistic Bandits
Shi Dong, Tengyu Ma, Benjamin Van Roy
We study the logistic bandit, in which rewards are binary with success probability and actions and coefficients are within the …
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei, Jason D. Lee, Qiang Liu +1
Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the…