Papers
Hidden Technical Debt in Machine Learning Systems
D. Sculley, Gary Holt, Daniel Golovin et al.
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
Zhaoran Wang, Quanquan Gu, Yang Ning et al.
High-dimensional neural spike train analysis with generalized count linear dynamical systems
Yuanjun Gao, Lars Busing, Krishna V. Shenoy et al.
HONOR: Hybrid Optimization for NOn-convex Regularized problems
Pinghua Gong, Jieping Ye
Human Memory Search as Initial-Visit Emitting Random Walk
Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers et al.
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems
Ruoyu Sun, Mingyi Hong
Inference for determinantal point processes without spectral knowledge
Rémi Bardenet, Michalis Titsias RC AUEB
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Armand Joulin, Tomas Mikolov
Infinite Factorial Dynamical Model
Isabel Valera, Francisco Ruiz, Lennart Svensson et al.
Information-theoretic lower bounds for convex optimization with erroneous oracles
Yaron Singer, Jan Vondrak
Interactive Control of Diverse Complex Characters with Neural Networks
Igor Mordatch, Kendall Lowrey, Galen Andrew et al.
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Qinqing Zheng, Ryota Tomioka
Inverse Reinforcement Learning with Locally Consistent Reward Functions
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Is Approval Voting Optimal Given Approval Votes?
Ariel D Procaccia, Nisarg Shah
Kullback-Leibler Proximal Variational Inference
Mohammad Emtiyaz Khan, Pierre Baque, François Fleuret et al.
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
Piyush Rai, Changwei Hu, Ricardo Henao et al.
Large-scale probabilistic predictors with and without guarantees of validity
Vladimir Vovk, Ivan Petej, Valentina Fedorova
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
CHRISTOS THRAMPOULIDIS, Ehsan Abbasi, Babak Hassibi
Latent Bayesian melding for integrating individual and population models
Mingjun Zhong, Nigel Goddard, Charles Sutton
Learnability of Influence in Networks
Harikrishna Narasimhan, David C. Parkes, Yaron Singer
Learning Bayesian Networks with Thousands of Variables
Mauro Scanagatta, Cassio P de Campos, Giorgio Corani et al.
Learning both Weights and Connections for Efficient Neural Network
Song Han, Jeff Pool, John Tran et al.
Learning Causal Graphs with Small Interventions
Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G Dimakis et al.
Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Gregory Wayne, David Silver et al.