Uncertainty in Computation

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source: Simons Institute     2016年10月4日
Uncertainty in Computation
The true logic of the World is the calculus of probabilities — James Clerk Maxwell
The theory of probability is at bottom nothing but common sense reduced to calculus — Pierre Simon Laplace
Uncertainty pervades our every interaction with the physical world and with all our attempts to organize our data and understanding. Probability is the best tool we have for managing uncertainty. This workshop will bring together researchers for whom probabilistic reasoning is an essential aspect of their research.
We will address three major themes: (i) logic and probability, (ii) probabilistic databases and statistical relational models, and (iii) machine learning. The first theme is both foundational, as the quote from Laplace suggests, and practical. There is substantial interest in automated tools for verifying probabilistic systems using probabilistic modal logics. The second theme is about the management of uncertainty in relational data and probabilistic inference for queries expressed in first-order logic. The use of logic for representing and reasoning about probabilities has generated a lot interest and has led to new approaches for probabilistic inference. The third theme is represented by a vast community interested in extracting information from data. There is also burgeoning interest in programming languages for building probabilistic models which impinges on all three themes.
For more information, please visit https://simons.berkeley.edu/workshops/logic2016-1.
These presentations were supported in part by an award from the Simons Foundation.

Unifying Logic and Probability: The BLOG Language 1:00:46 Stuart Russell, UC Berkeley https://simons.berkeley.edu/talks/stu...
Model Checking and Strategy Synthesis for Stochastic Games: From Theory to Practice 42:40
Computing Probabilistic Bisimilarity Distances via Policy Iteration 37:28
Quantitative Algebraic Reasoning 43:18
Controlling Probabilistic Systems Under Partial Observation 42:46
Three Problems in Computable Probability Theory 54:53
Brief Tutorial on Probabilistic Databases 1:01:11
A Dichotomy for Queries with Negation in Probabilistic Databases 42:30
Fourier Representations in Probabilistic Inference 30:53
Approximate Lifted Inference with Probabilistic Databases 46:48
Scalable Collective Inference from Richly Structured Data using Probabilistic Soft Logic (PSL) 41:12
Stochastic Control via Entropy Compression 41:45
Probabilistic Reasoning by First-Order Model Counting 48:09
A Personal Viewpoint on Probabilistic Programming 1:03:22
Probabilistic Programming for Augmented Intelligence 40:16
Differential Privacy 42:05
Pufferfish Privacy Mechanisms for Correlated Data 42:47
Axioms for Information Leakage 41:26
Constrained Sampling and Counting 46:06
Learning about Auctions 43:13
Random Projections for Probabilistic Inference 51:02
Approximation Algorithms for Optimization under Uncertainty 40:01
Outposts Between Worst- and Average Case Analysis: A Case Study in Auction Design 47:49
Semantic Foundations for Probabilistic Programming 48:58
Black-Box Variational Inference for Probabilistic Programs 37:58