Papers
4,122 papers found
Multiple-Splitting Projection Test for High-Dimensional Mean Vectors
Wanjun Liu, Xiufan Yu, Runze Li
Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach
Kweku Abraham, Ismaël Castillo, Elisabeth Gassiat
Multi-Task Dynamical Systems
Alex Bird, Christopher K. I. Williams, Christopher Hawthorne
Multivariate Boosted Trees and Applications to Forecasting and Control
Lorenzo Nespoli, Vasco Medici
MurTree: Optimal Decision Trees via Dynamic Programming and Search
Emir Demirović, Anna Lukina, Emmanuel Hebrard et al.
Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling
Gábor Melis, András György, Phil Blunsom
Network Regression with Graph Laplacians
Yidong Zhou, Hans-Georg Müller
Neural Estimation of Statistical Divergences
Sreejith Sreekumar, Ziv Goldfeld
New Insights for the Multivariate Square-Root Lasso
Aaron J. Molstad
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
Yahya Sattar, Samet Oymak
Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials
Daiqi Gao, Yufeng Liu, Donglin Zeng
Nonconvex Matrix Completion with Linearly Parameterized Factors
Ji Chen, Xiaodong Li, Zongming Ma
Nonparametric adaptive control and prediction: theory and randomized algorithms
Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine
Nonparametric Neighborhood Selection in Graphical Models
Hao Dong, Yuedong Wang
Nonparametric Principal Subspace Regression
Yang Zhou, Mark Koudstaal, Dengdeng Yu et al.
Nonstochastic Bandits with Composite Anonymous Feedback
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni et al.
Novel Min-Max Reformulations of Linear Inverse Problems
Mohammed Rayyan Sheriff, Debasish Chatterjee
No Weighted-Regret Learning in Adversarial Bandits with Delays
Ilai Bistritz, Zhengyuan Zhou, Xi Chen et al.
Nystrom Regularization for Time Series Forecasting
Zirui Sun, Mingwei Dai, Yao Wang et al.
OMLT: Optimization & Machine Learning Toolkit
Francesco Ceccon, Jordan Jalving, Joshua Haddad et al.
On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping
Qiang Zhou, Sinno Jialin Pan
On Biased Stochastic Gradient Estimation
Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems
Michael Muehlebach, Michael I. Jordan
On Generalizations of Some Distance Based Classifiers for HDLSS Data
Sarbojit Roy, Soham Sarkar, Subhajit Dutta et al.