Automated Scientific Discovery Using Insights from Problem Structure

Published on ● Video Link: https://www.youtube.com/watch?v=25SGdOJoTIw



Duration: 43:59
294 views
6


Bart Selman, Cornell University
https://simons.berkeley.edu/talks/bart-selman-2016-11-18
Learning, Algorithm Design and Beyond Worst-Case Analysis




Other Videos By Simons Institute for the Theory of Computing


2016-12-05Data Structures for Quasistrict Higher Categories
2016-12-05An Operadic Approach to Compositionality
2016-12-05Compositionality, Adequacy, and Full Abstraction
2016-12-05Semantics for Physicists
2016-12-01Logic and Automata and Beyond
2016-11-18The Power of Predictions in Online Optimization
2016-11-18Above Average-Case Analysis?
2016-11-18When Existing Techniques in Linear Regression Preserve Differential Privacy
2016-11-18Monotone Estimation Framework and its Applications for Scalable Analytics of Large Data Sets
2016-11-18How Hard Is Inference for Structured Prediction?
2016-11-18Automated Scientific Discovery Using Insights from Problem Structure
2016-11-17Sketching and Randomization for Distributed Submodular and Coverage Optimization
2016-11-17When Does Clustering Become Easy, and Should We Care About Other Cases?
2016-11-17Follow the Leader with Dropout Perturbations
2016-11-17Beyond Worst Case: When Complex Feedback Can Improve the Label Complexity of Active Learning
2016-11-17Characterizing the Typical Case Complexity of Formal Verification and Synthesis
2016-11-17Recovery Guarantee of Non-Negative Matrix Factorization via Alternating Updates
2016-11-17The Logic of Counting Query Answers
2016-11-16The Computational Benefit of Correlated Instances
2016-11-16Learning as a Tool for Algorithm Design and Beyond-Worst-Case Analysis
2016-11-16Learning the Best Agorithm for Max-Cut, Clustering, and Other​ ​Partitioning Problems



Tags:
Bart Selman
Learning Algorithm Design and Beyond Worst-Case Analysis
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley