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Found 6 papers, 2 papers with code
Dynamic Global Sensitivity for Differentially Private Contextual Bandits
We provide a rigorous theoretical analysis over the amount of noise added via dynamic global sensitivity and the corresponding upper regret bound of our proposed algorithm.
Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting
The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$.
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
In this work, we propose a unified and modular inference framework that bridges classical statistics and modern machine learning providing (i) a practical approach to the Neyman construction of confidence sets with frequentist finite-sample coverage for any value of the unknown parameters; and (ii) interpretable diagnostics that estimate the empirical coverage across the entire parameter space.
MD-split+: Practical Local Conformal Inference in High Dimensions
Quantifying uncertainty in model predictions is a common goal for practitioners seeking more than just point predictions.
Reinforcement learning for bandwidth estimation and congestion control in real-time communications
Bandwidth estimation and congestion control for real-time communications (i. e., audio and video conferencing) remains a difficult problem, despite many years of research.
Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines
Few assets in financial history have been as notoriously volatile as cryptocurrencies.
