Querying Easily Flip-flopped Samples for Deep Active Learning
Proposes a new active learning approach by proposing a new uncertainty measure called the least disagree metric, as well as its efficient estimator, which is proven to be …
seong-jin-cho
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1 min read
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Logistic bandit is a ubiquitous framework of modeling users' choices, e.g., click vs. no click for advertisement recommender system. We observe that the prior works overlook or …
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Proposes a framework for performing fair PCA in memory limited, streaming setting. Sample complexity results and empirical discussions show the superiority of our approach compared …
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks
A novel problem setting where heterogeneous multi-agent bandits collaborate over a network to minimize their group regret. To deal with the high communication complexity of the …
Nearly Optimal Latent State Decoding in Block MDPs
First theoretical analysis of model estimation and reward-free RL of block MDP, without resorting to function approximation frameworks. Lower bounds and algorithms with …
yassir-jedra
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
Proposes a new MMD-based definition of fairness for PCA, then formulate fair PCA as an optimization over the Stiefel manifold. Various theoretical and empirical discussions show …
Preliminary Evaluation of SWAY in Permutation Decision Space via a Novel Euclidean Embedding
Extend SWAY (Chen et al., 2016) to the space of permutations by proposing a new Euclidean embedding of permutations.