75 citations · 897 across the 91 of their papers we have counts for
95 papers
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
Liang Zhang, Junchi Yang, Amin Karbasi +1
Algorithmic reproducibility measures the deviation in outputs of machine learning algorithms upon minor changes in the training process. Previous work suggests that first-order met…
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
Idan Attias, Steve Hanneke, Alkis Kalavasis +2
In this work, we aim to characterize the statistical complexity of realizable regression both in the PAC learning setting and the online learning setting. Previous work had establi…
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma +1
Thompson sampling (TS) is widely used in sequential decision making due to its ease of use and appealing empirical performance. However, many existing analytical and empirical resu…
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos +5
Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and data distillation, have been shown to be effect…
Submodular Minimax Optimization: Finding Effective Sets
Loay Mualem, Ethan R. Elenberg, Moran Feldman +1
Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings. In thi…
Statistical Indistinguishability of Learning Algorithms
Alkis Kalavasis, Amin Karbasi, Shay Moran +1
When two different parties use the same learning rule on their own data, how can we test whether the distributions of the two outcomes are similar? In this paper, we study the simi…