Towards a Theory of Learning Inductive Invariants

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=g1NSjTEXzLc


Duration: 23:36
710 views
13

Yotam Feldman (Tel Aviv University)
https://simons.berkeley.edu/talks/towards-theory-learning-inductive-invariants
Synthesis of Models and Systems



Other Videos By Simons Institute for the Theory of Computing


2021-04-05Visualization By Example
2021-04-05Counterexample-guided Repair in Boolean Functional Synthesis
2021-04-05Guarantees in Syntax-Guided Synthesis
2021-04-01SAT for Optimization
2021-04-01Short Proofs in Strong Proof Systems
2021-04-01Back to the Future: A Fresh Look at Linear Temporal Logic
2021-03-31Panel Discussion on Potential for Quantum Advantage in Machine Learning | Quantum Colloquium
2021-03-31On Quantum Linear Algebra for Machine Learning | Quantum Colloquium
2021-03-31Exact Mixed-Integer Programming over the Rational Numbers
2021-03-29On Optimal Collusion-Free Teaching
2021-03-29Towards a Theory of Learning Inductive Invariants
2021-03-29Formal Synthesis: Oracle-guided Learning of Compositional Concepts
2021-03-29Inferring Specifications From Demonstrations; A Maximum (Causal) Entropy Approach
2021-03-26Subgame Perfect Equilibria
2021-03-25Information Efficiency of Proof Systems
2021-03-25Proofs and Circuits: Connections via Interpolation and Lifting
2021-03-24Knowledge Compilation for Boolean Functional Synthesis
2021-03-22Dreamcoder: Bootstrapping Inductive Program Synthesis With Wake-Sleep Library Learning
2021-03-22Program Synthesis via Deep Learning over Graph Structured Data
2021-03-22Liquid Time Constant Networks
2021-03-22Towards Human-Like Program Synthesis


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Yotam Feldman
Synthesis of Models and Systems