Media Summary: A Characterization of List Learnability. Moses Charikar, Chirag Pabbaraju (Stanford University) A Unifying Theory of Distance from Calibration. Jarosław Błasiok (Columbia University); Parikshit Gopalan (Apple); Lunjia Hu ... A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. Ilias Diakonikolas ...
Stoc 2023 Session 9c A - Detailed Analysis & Overview
A Characterization of List Learnability. Moses Charikar, Chirag Pabbaraju (Stanford University) A Unifying Theory of Distance from Calibration. Jarosław Błasiok (Columbia University); Parikshit Gopalan (Apple); Lunjia Hu ... A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. Ilias Diakonikolas ... Testing distributional assumptions of learning algorithms. Ronitt Rubinfeld, Arsen Vasilyan (MIT) A Moment-Matching Approach to Testable Learning and a New Characterization of Rademacher Complexity. Aravind Gollakota ... What Makes a Good Fisherman? Linear Regression under Self-Selection Bias. Yeshwanth Cherapanamjeri (UC Berkeley); ...
Hausdorff and Gromov-Hausdorff stable subsets of the medial axis. André Lieutier (None); Mathijs Wintraecken (IST Austria and ... Lifting uniform learners via distributional decomposition. Guy Blanc (Stanford University); Jane Lange (MIT); Ali Malik, Li-Yang Tan ... Learning Polynomial Transformations via Generalized Tensor Decompositions. Sitan Chen (UC Berkeley); Jerry Li, Yuanzhi Li ... Average-Case Complexity of Tensor Decomposition for Low-Degree Polynomials. Alexander S. Wein (UC Davis) Generic Reed-Solomon codes achieve list-decoding capacity. Joshua Brakensiek (Stanford University); Sivakanth Gopi (Microsoft ... Exact Phase Transitions for Stochastic Block Models and Reconstruction on Trees. Elchanan Mossel (MIT); Allan Sly (Princeton); ...
The Power of Unentangled Quantum Proofs with Non-negative Amplitudes. Fernando Granha Jeronimo, Pei Wu (IAS)
