235 citations · 901 across the 97 of their papers we have counts for
21 papers · 1 filter
Semi-supervised Batch Active Learning via Bilevel Optimization
Zalán Borsos, Marco Tagliasacchi, Andreas Krause
Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learni…
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
Max B. Paulus, Chris J. Maddison, Andreas Krause
Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, mode…
Logistic Q-Learning
Joan Bas-Serrano, Sebastian Curi, Andreas Krause +1
We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The method is closely related to the classic…
Online Active Model Selection for Pre-trained Classifiers
Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš +3
Given pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest…
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Chris Wendler, Andisheh Amrollahi, Bastian Seifert +2
Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function tha…
Learning to Play Sequential Games versus Unknown Opponents
Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour +1
We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We seek to design strategies for the learner to succes…