Python Forecasting Masterclass: From Foundations to State-of-the-Art (Registration Link Below)
MLBoost
Python Forecasting Masterclass: From Foundations to State-of-the-Art (Registration Link Below)
5:36
The Future of AI: From Mathematics’ Revenge to the Rise of Prompt Markets | Peter Cotton Interview
MLBoost
The Future of AI: From Mathematics’ Revenge to the Rise of Prompt Markets | Peter Cotton Interview
53:26
Uncertainty Quantification in Medicine and Digital Health
MLBoost
Uncertainty Quantification in Medicine and Digital Health
50:52
Time Series Foundation Models: A Tutorial and Survey
MLBoost
Time Series Foundation Models: A Tutorial and Survey
1:30:10
MLBoost Seminars (10): Graph Neural Networks for Time Series
MLBoost
MLBoost Seminars (10): Graph Neural Networks for Time Series
1:19:15
MLBoost Seminars (9): Robust Yet Efficient Conformal Prediction Sets
MLBoost
MLBoost Seminars (9): Robust Yet Efficient Conformal Prediction Sets
53:55
MLBoost Seminars (8): Conformal Inverse Optimization
MLBoost
MLBoost Seminars (8): Conformal Inverse Optimization
59:54
MLBoost Seminars (7): Sequential Conformal Prediction for Time Series
MLBoost
MLBoost Seminars (7): Sequential Conformal Prediction for Time Series
1:02:07
MLBoost Seminars (6): Kolmogorov–Arnold Neural Networks
MLBoost
MLBoost Seminars (6): Kolmogorov–Arnold Neural Networks
1:24:23
MLBoost Seminars (5): Selection by Prediction with Conformal p-values
MLBoost
MLBoost Seminars (5): Selection by Prediction with Conformal p-values
1:02:10
MLBoost Seminars (4): Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
MLBoost
MLBoost Seminars (4): Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
1:20:55
MLBoost Seminars (3): Conformal Prediction for Time Series with Modern Hopfield Networks
MLBoost
MLBoost Seminars (3): Conformal Prediction for Time Series with Modern Hopfield Networks
50:38
MLBoost Seminars (2): Trustworthy Retrieval Augmented Chatbots [Utilizing Conformal Predictors]
MLBoost
MLBoost Seminars (2): Trustworthy Retrieval Augmented Chatbots [Utilizing Conformal Predictors]
58:30
MLBoost Seminars (1): Uncertainty Alignment for Large Language Model Planners
MLBoost
MLBoost Seminars (1): Uncertainty Alignment for Large Language Model Planners
1:04:12
MLBoost Live Stream
MLBoost
MLBoost Live Stream
Applied Conformal Predictors: Why Large Language Models (LLMs) Need Conformal Predictors
MLBoost
Applied Conformal Predictors: Why Large Language Models (LLMs) Need Conformal Predictors
12:57
(8) Training and Evaluating Point Forecasting Models: What Does and Doesn’t Make Sense!
MLBoost
(8) Training and Evaluating Point Forecasting Models: What Does and Doesn’t Make Sense!
4:59
(7) Under Absolute Percentage Error loss, a Non-conventional Median is Optimal!
MLBoost
(7) Under Absolute Percentage Error loss, a Non-conventional Median is Optimal!
3:55
(6) In ML Competitions, when the Error is MAE, Submit the Median of Inferred Distribution.
MLBoost
(6) In ML Competitions, when the Error is MAE, Submit the Median of Inferred Distribution.
4:12
(5) In ML Competitions, when the Error is MSE, Submit the Expected Value of Inferred Distribution.
MLBoost
(5) In ML Competitions, when the Error is MSE, Submit the Expected Value of Inferred Distribution.
8:50
(4) Best Possible Model May Lose to a Naive One if Evaluation Metric is Not Consistent with ...
MLBoost
(4) Best Possible Model May Lose to a Naive One if Evaluation Metric is Not Consistent with ...
6:15
(3) A Forecasting Competition
MLBoost
(3) A Forecasting Competition
4:05
(2) Model Evaluation - adjustedMAPE
MLBoost
(2) Model Evaluation - adjustedMAPE
9:28
(1) Model Evaluation - MAPE
MLBoost
(1) Model Evaluation - MAPE
11:30