Papers
Probabilistic Learning on Graphs via Contextual Architectures
Davide Bacciu, Federico Errica, Alessio Micheli
Probabilistic Symmetries and Invariant Neural Networks
Benjamin Bloem-Reddy, Yee Whye Teh
ProtoAttend: Attention-Based Prototypical Learning
Sercan O. Arik, Tomas Pfister
Provable Convex Co-clustering of Tensors
Eric C. Chi, Brian J. Gaines, Will Wei Sun et al.
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Mihai Cucuringu, Hemant Tyagi
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan et al.
pyDML: A Python Library for Distance Metric Learning
Juan Luis Suárez, Salvador García, Francisco Herrera
pyts: A Python Package for Time Series Classification
Johann Faouzi, Hicham Janati
Quadratic Decomposable Submodular Function Minimization: Theory and Practice
Pan Li, Niao He, Olgica Milenkovic
Quantile Graphical Models: a Bayesian Approach
Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Lucas Mentch, Siyu Zhou
Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images
Avrim Blum, Travis Dick, Naren Manoj et al.
Rank-based Lasso - efficient methods for high-dimensional robust model selection
Wojciech Rejchel, Małgorzata Bogdan
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior
William Hoiles, Vikram Krishnamurthy, Kunal Pattanayak
Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering
Tao Jiang, Stephen Vavasis, Chen Wen Zhai
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh et al.
Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models
Jiahe Lin, George Michailidis
Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size
Francois Kamper, Sarel J. Steel, Johan A. du Preez
Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou
Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain et al.
Risk Bounds for Reservoir Computing
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis
Robust high dimensional learning for Lipschitz and convex losses
Chinot Geoffrey, Lecué Guillaume, Lerasle Matthieu
Robust Reinforcement Learning with Bayesian Optimisation and Quadrature
Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek et al.
Scalable Approximate MCMC Algorithms for the Horseshoe Prior
James Johndrow, Paulo Orenstein, Anirban Bhattacharya