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The Implicit Bias of AdaGrad on Separable Data
We study the implicit bias of AdaGrad on separable linear classification problems.
A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization
The differential equation (DE) approach for convex optimization, which relates optimization methods to specific continuous DEs with rate-revealing Lyapunov functionals, has gained increasing interest since the seminal paper by Su-Boyd-Candès (2014).
Optimal Stochastic and Online Learning with Individual Iterates
Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou
Neural Information Processing Systems http://nips.cc/
DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
We also introduce a novel benchmark for evaluating NNPs in molecular property prediction, Hamiltonian prediction, and conformational optimization tasks.
Unlabeled Data Improves Adversarial Robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy S. Liang
Neural Information Processing Systems http://nips.cc/
