Statistical Learning
Sparse Embedded $k$-Means Clustering
Weiwei Liu, Xiaobo Shen, Ivor Tsang
However, its high computational cost has hindered its applications in broad domains. Researchers have successfully addressed these obstacles with dimensionality reduction methods.
Alternating minimization for dictionary learning with random initialization
Dictionary learning and specifically alternating minimization algorithms for dictionary learning are well studied both theoretically and empirically.
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
Self-paced learning and hard example mining re-weight training instances to improve learning accuracy.
Affinity Clustering: Hierarchical Clustering at Scale
Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni
The main focus of this paper is on minimum spanning tree (MST) based clusterings.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
Yunzhu Li, Jiaming Song, Stefano Ermon
Neural Information Processing Systems http://nips.cc/
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
We pay special attention to the setting where the sample size n is small. This type of tests carries concrete significance in scientific studies.
Population Matching Discrepancy and Applications in Deep Learning
Jianfei Chen, Chongxuan LI, Yizhong Ru, Jun Zhu
One such estimation is maximum mean discrepancy (MMD).
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
Lalit Jain, Kevin G. Jamieson, Rob Nowak
Euclidean space given a set of constraints like "item
Group Additive Structure Identification for Kernel Nonparametric Regression
However, its main drawback is that it neglects possible interactions between predictor variables.
Reliable Decision Support using Counterfactual Models
One use of such an estimate is to evaluate risk; e.g. is this patient likely to die if I do not