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Exact Bayesian Pairwise Preference Learning and Inference on the Uniform Convex Polytope
Published on Jan 24, 20123866 Views
In Bayesian approaches to utility learning from preferences, the objective is to infer a posterior belief distribution over an agent’s utility function based on previously observed agent preferences.
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Chapter list
Exact Bayesian Pairwise Preference Learning and Inference on the Uniform Convex Polytope00:00
Pic00:06
Part I: Bayesian Pairwise Preference Learning (BPPL)00:25
Learning from Preferences00:43
Bayesian Decision Theory01:37
Preference Feedback Types02:44
Preference Learning Feedback04:01
Part II: Bayesian Pairwise Preference Learning (BPPL)04:53
Bayesian Utility Model05:13
Response Likelihood Model05:40
Geometric View06:26
Uniform Prior07:32
Inference Tasks08:20
Part II: Bayesian Pairwise Preference Learning (BPPL)08:59
Polynomial Case Representation09:11
Polynomial Case Operations: - 110:06
Polynomial Case Operations: - 210:25
Polynomial Case Operations: max - 111:13
Polynomial Case Operations: max - 211:28
BPPL Inference12:11
Marginalization: ∫ - 112:45
Marginalization: ∫ - 213:34
Definite Integral Evaluation15:10
That’s It!16:00
Case ® XADD16:35
Maintaining Compact Cases17:08
Compactness of (X)ADDs17:45
Binary Operations on (X)ADDs18:02
XADD Maximization18:35
Maintaining XADD Orderings19:11
Correcting XADD Ordering19:49
XADD Pruning20:21
Empirical Results21:07
Future Work – Exact BPPL21:59
Thank you!22:58