Papers
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
Maxime Cauchois, Suyash Gupta, John C. Duchi
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Vikram Krishnamurthy, George Yin
Langevin Monte Carlo: random coordinate descent and variance reduction
Zhiyan Ding, Qin Li
LassoNet: A Neural Network with Feature Sparsity
Ismael Lemhadri, Feng Ruan, Louis Abraham et al.
LDLE: Low Distortion Local Eigenmaps
Dhruv Kohli, Alexander Cloninger, Gal Mishne
Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression
Gunwoong Park, Sang Jun Moon, Sion Park et al.
Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
Melkior Ornik, Ufuk Topcu
Learning Bayesian Networks from Ordinal Data
Xiang Ge Luo, Giusi Moffa, Jack Kuipers
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Fei Lu, Mauro Maggioni, Sui Tang
Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation
Pierre Humbert, Batiste Le Bars, Laurent Oudre et al.
Learning partial correlation graphs and graphical models by covariance queries
Gábor Lugosi, Jakub Truszkowski, Vasiliki Velona et al.
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
Xiaowu Dai, Michael I. Jordan
Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature
Stéphane Chrétien, Mihai Cucuringu, Guillaume Lecué et al.
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Keith D. Levin, Fred Roosta, Minh Tang et al.
Linear Bandits on Uniformly Convex Sets
Thomas Kerdreux, Christophe Roux, Alexandre d'Aspremont et al.
LocalGAN: Modeling Local Distributions for Adversarial Response Generation
Baoxun Wang, Zhen Xu, Huan Zhang et al.
Locally Differentially-Private Randomized Response for Discrete Distribution Learning
Adriano Pastore, Michael Gastpar
Locally Private k-Means Clustering
Uri Stemmer
L-SVRG and L-Katyusha with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik
Matrix Product States for Inference in Discrete Probabilistic Models
Rasmus Bonnevie, Mikkel N. Schmidt
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
Fei Wang, Ling Zhou, Lu Tang et al.
Mixing Time of Metropolis-Hastings for Bayesian Community Detection
Bumeng Zhuo, Chao Gao