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Found 10 papers, 5 papers with code
Querying Easily Flip-flopped Samples for Deep Active Learning
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study
Although gradient descent with momentum is widely used in modern deep learning, a concrete understanding of its effects on the training trajectory still remains elusive.
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditional on the sensitive attributes, match one another.
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.
Optimizing Layerwise Polynomial Approximation for Efficient Private Inference on Fully Homomorphic Encryption: A Dynamic Programming Approach
Unlike the previous works approximating activation functions uniformly and conservatively, this paper presents a \emph{layerwise} degree optimization of activation functions to aggressively reduce the inference time while maintaining classification accuracy by taking into account the characteristics of each layer.
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks
Then, to mitigate the issue of high communication costs incurred by flooding in complex networks, we propose a new protocol called Flooding with Absorption (FwA).
Nearly Optimal Latent State Decoding in Block MDPs
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs.
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different protected classes.
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations.
Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN
Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT).
