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MLBoost Seminars (9): Robust Yet Efficient Conformal Prediction Sets

MLBoost Seminars (8): Conformal Inverse Optimization

MLBoost Seminars (7): Sequential Conformal Prediction for Time Series

MLBoost Seminars (6): Kolmogorov–Arnold Neural Networks

Uncertainty Quantification (5): Avoid these Missteps in Validating Your Conformal Codes!

MLBoost Seminars (5): Selection by Prediction with Conformal p-values

MLBoost Seminars (4): Uncertainty Quantification over Graph with Conformalized Graph Neural Networks

MLBoost Seminars (3): Conformal Prediction for Time Series with Modern Hopfield Networks

MLBoost Seminars (2): Trustworthy Retrieval Augmented Chatbots [Utilizing Conformal Predictors]

MLBoost Seminars (1): Uncertainty Alignment for Large Language Model Planners

Uncertainty Quantification (4B): Mastering and Implementing Split Conformal Methods with NumPy Only

Applied Conformal Predictors: Why Large Language Models (LLMs) Need Conformal Predictors

Uncertainty Quantification (4A): Implementing Split Conformal - Relation for Prediction Intervals

Uncertainty Quantification (3): From Full to Split Conformal Methods

Uncertainty Quantification (2): Full Conformal Predictors

Uncertainty Quantification (1): Enter Conformal Predictors

(8) Training and Evaluating Point Forecasting Models: What Does and Doesn’t Make Sense!

(7) Under Absolute Percentage Error loss, a Non-conventional Median is Optimal!

(6) In ML Competitions, when the Error is MAE, Submit the Median of Inferred Distribution.

(5) In ML Competitions, when the Error is MSE, Submit the Expected Value of Inferred Distribution.

(4) Best Possible Model May Lose to a Naive One if Evaluation Metric is Not Consistent with ...

(3) A Forecasting Competition

(2) Model Evaluation - adjustedMAPE

(1) Model Evaluation - MAPE