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Theory

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Papers

Is your function low dimensional? COLT 2019
Lower Bounds for Parallel and Randomized Convex Optimization COLT 2019
Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard COLT 2019
The implicit bias of gradient descent on nonseparable data COLT 2019
On Communication Complexity of Classification Problems COLT 2019
Discrepancy, Coresets, and Sketches in Machine Learning COLT 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit COLT 2019
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon COLT 2019
Depth Separations in Neural Networks: What is Actually Being Separated? COLT 2019
The Relative Complexity of Maximum Likelihood Estimation, MAP Estimation, and Sampling COLT 2019
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds COLT 2019
Open Problem: Monotonicity of Learning COLT 2019
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory COLT 2019
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions CVPR 2019
Single Image Deraining: A Comprehensive Benchmark Analysis CVPR 2019
RAVEN: A Dataset for Relational and Analogical Visual REasoNing CVPR 2019
Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure From Motion CVPR 2019
Polynomial Representation for Persistence Diagram CVPR 2019
Interpreting CNNs via Decision Trees CVPR 2019
Disentangling Adversarial Robustness and Generalization CVPR 2019
Tightness-Aware Evaluation Protocol for Scene Text Detection CVPR 2019
Ray-Space Projection Model for Light Field Camera CVPR 2019
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning CVPR 2019
Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications CVPR 2019
Convolutional Neural Networks Can Be Deceived by Visual Illusions CVPR 2019