Computational Depth
Lecture Notes by Emma Cohen
Computational depth: Concept and applications by Antunes, Fortnow, van Melkebeek and Vinodchandran
Low-Depth Witnesses are Easy to Find by Antunes, Fortnow, Pinto and Souto
Worst-Case Running Times for Average-Case Algorithms by Antunes and Fortnow
Monday, March 30, 2015
Friday, March 27, 2015
Wednesday, March 25, 2015
Lecture 30
Extractors
Lecture Notes by David Durfee
Relativizable pseudorandom generators and extractors by Peter Bro Miltersen
Extractors: optimal up to constant factors by Lu, Reingold, Vadhan and Wigderson
Lecture Notes by David Durfee
Relativizable pseudorandom generators and extractors by Peter Bro Miltersen
Extractors: optimal up to constant factors by Lu, Reingold, Vadhan and Wigderson
Monday, March 23, 2015
Lecture 29
Time-bounded sizes of sets using Kolmogorov complexity
Lecture Notes by John Stewart
Resource-bounded Kolmogorov complexity revisited by Buhrman, Fortnow and Laplante
Lecture Notes by John Stewart
Resource-bounded Kolmogorov complexity revisited by Buhrman, Fortnow and Laplante
Friday, March 13, 2015
Wednesday, March 11, 2015
Lecture 27
Sophistication and data with no simple models
Lecture Notes by Abhishek Banerjee
Sophistication Revisited by Antunes and Fortnow
Lecture Notes by Abhishek Banerjee
Sophistication Revisited by Antunes and Fortnow
Monday, March 9, 2015
Lecture 26
Model Selection via Kolmogorov Complexity
Lecture Notes by Abhishek Banerjee
Kolmogorov's structure functions and model selection by Vereschagin and Vitanyi
Lecture Notes by Abhishek Banerjee
Kolmogorov's structure functions and model selection by Vereschagin and Vitanyi
Friday, March 6, 2015
Lecture 25
NEXP in P/poly iff NEXP = EXP = MA
In search of an easy witness: exponential time vs. probabilistic polynomial time by Impagliazzo, Kabanets and Wigderson
In search of an easy witness: exponential time vs. probabilistic polynomial time by Impagliazzo, Kabanets and Wigderson
Wednesday, March 4, 2015
Lecture 24
Sign Up for Student Presentations
Implications of full derandomization including Graph Iso in NP ∩ co-NP under reasonable assumptions
Lecture Notes by Peter Ralli
Private coins versus public coins in interactive proof systems by Goldwasser and Sipser
Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses by Klivans and van Melkebeek
Implications of full derandomization including Graph Iso in NP ∩ co-NP under reasonable assumptions
Lecture Notes by Peter Ralli
Private coins versus public coins in interactive proof systems by Goldwasser and Sipser
Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses by Klivans and van Melkebeek
Monday, March 2, 2015
Lecture 23
Removing the approximation assumption for derandomization.
Lecture Notes by Emma Cohen
Yao XOR Lemma course notes from Ronitt Rubinfeld
BPP has subexponential time simulations unless EXPTIME has publishable proofs by Babai, Fortnow, Nisan and Wigderson
P = BPP if E requires exponential circuits: derandomizing the XOR lemma by Impagliazzo and Wigderson
Lecture Notes by Emma Cohen
Yao XOR Lemma course notes from Ronitt Rubinfeld
BPP has subexponential time simulations unless EXPTIME has publishable proofs by Babai, Fortnow, Nisan and Wigderson
P = BPP if E requires exponential circuits: derandomizing the XOR lemma by Impagliazzo and Wigderson
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