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Estimating Learnability in the Sublinear Data Regime
Neural Information Processing Systems http://nips.cc/
A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer
Achieving robustness to distributional shift is a longstanding and challenging goal of computer vision.
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions
Gabriele Farina, Christian Kroer, Tuomas Sandholm
Finally we show that when the goal isminimizing regret, rather than computing a Nash equilibrium, our optimistic methods can outperformCFR+,evenindeepgametrees.
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
Importantly,we consider the general multiclass (K > 2) scenario, which introduces significant complications.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski
Neural Information Processing Systems http://nips.cc/
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems
Neural Information Processing Systems http://nips.cc/
Cookie Consent Has Disparate Impact on Estimation Accuracy
How does a user's consent decision influence
Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli
Neural Information Processing Systems http://nips.cc/