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The 2021 schedule is still incomplete
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Oral
Thu Jul 22 11:00 AM -- 11:20 AM (KST)
Analysis of stochastic Lanczos quadrature for spectrum approximation
In Learning Theory 13
Tyler Chen · Thomas Trogdon · Shashanka Ubaru
[ Paper ]
Spotlight
Thu Jul 22 11:20 AM -- 11:25 AM (KST)
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise
In Learning Theory 13
Jie Shen
[ Paper ]
Spotlight
Thu Jul 22 11:25 AM -- 11:30 AM (KST)
On Robust Mean Estimation under Coordinate-level Corruption
In Learning Theory 13
Zifan Liu · Jong Ho Park · Theo Rekatsinas · Christos Tzamos
[ Paper ]
Spotlight
Thu Jul 22 11:30 AM -- 11:35 AM (KST)
Multidimensional Scaling: Approximation and Complexity
In Learning Theory 13
Erik Demaine · Adam C Hesterberg · Frederic Koehler · Jayson Lynch · John C Urschel
[ Paper ]
Spotlight
Thu Jul 22 11:35 AM -- 11:40 AM (KST)
Toward Better Generalization Bounds with Locally Elastic Stability
In Learning Theory 13
Zhun Deng · Hangfeng He · Weijie Su
[ Paper ]
Spotlight
Thu Jul 22 11:40 AM -- 11:45 AM (KST)
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
In Learning Theory 13
Jonathan Lacotte · Yifei Wang · Mert Pilanci
[ Paper ]
Spotlight
Thu Jul 22 11:45 AM -- 11:50 AM (KST)
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
In Learning Theory 13
Jie Ren · Mingjie Li · Zexu Liu · Quanshi Zhang
[ Paper ]
Q&A
Thu Jul 22 11:50 AM -- 11:55 AM (KST)
Q&A
In Learning Theory 13