Papers
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics
Yee Whye Teh, Alexandre H. Thiery, Sebastian J. Vollmer
Consistency of Cheeger and Ratio Graph Cuts
Nicolás García Trillos, Dejan Slepčev, James von Brecht et al.
Consistent Algorithms for Clustering Time Series
Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary et al.
Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables
Ruth Heller, Yair Heller, Shachar Kaufman et al.
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models
Zijian Guo, Dylan S. Small
Convergence of an Alternating Maximization Procedure
Andreas Andresen, Vladimir Spokoiny
Convex Calibration Dimension for Multiclass Loss Matrices
Harish G. Ramaswamy, Shivani Agarwal
Convex Regression with Interpretable Sharp Partitions
Ashley Petersen, Noah Simon, Daniela Witten
Covariance-based Clustering in Multivariate and Functional Data Analysis
Francesca Ieva, Anna Maria Paganoni, Nicholas Tarabelloni
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
Vikash Mansinghka, Patrick Shafto, Eric Jonas et al.
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders
Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman et al.
CVXPY: A Python-Embedded Modeling Language for Convex Optimization
Steven Diamond, Stephen Boyd
Data-driven Rank Breaking for Efficient Rank Aggregation
Ashish Khetan, Sewoong Oh
Decrypting “Cryptogenic” Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients
Bilal Ahmed, Thomas Thesen, Karen E. Blackmon et al.
Differentially Private Data Releasing for Smooth Queries
Ziteng Wang, Chi Jin, Kai Fan et al.
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
François Denis, Mattias Gybels, Amaury Habrard
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing et al.
Distributed Coordinate Descent Method for Learning with Big Data
Peter Richtárik, Martin Takáč
Distributed Submodular Maximization
Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar et al.
Distribution-Matching Embedding for Visual Domain Adaptation
Mahsa Baktashmotlagh, Mehrtash Harandi, Mathieu Salzmann
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan et al.
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
Nihar B. Shah, Dengyong Zhou
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm
Aryan Mokhtari, Alejandro Ribeiro
Dual Control for Approximate Bayesian Reinforcement Learning
Edgar D. Klenske, Philipp Hennig