Papers
4,025 papers found
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
Atsushi Nitanda, Taiji Suzuki
Graphical Models for Non-Negative Data Using Generalized Score Matching
Shiqing Yu, Mathias Drton, Ali Shojaie
Group Invariance Principles for Causal Generative Models
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf et al.
Growth-Optimal Portfolio Selection under CVaR Constraints
Guy Uziel, Ran El-Yaniv
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou et al.
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Paul Rolland, Jonathan Scarlett, Ilija Bogunovic et al.
HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step
Yuting Ye, Lihua Lei, Cheng Ju
Human Interaction with Recommendation Systems
Sven Schmit, Carlos Riquelme
IHT dies hard: Provable accelerated Iterative Hard Thresholding
Rajiv Khanna, Anastasios Kyrillidis
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
Inference in Sparse Graphs with Pairwise Measurements and Side Information
Dylan Foster, Karthik Sridharan, Daniel Reichman
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis
Luca Ambrogioni, Eric Maris
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
Jussi Viinikka, Ralf Eggeling, Mikko Koivisto
Iterative Spectral Method for Alternative Clustering
Chieh Wu, Stratis Ioannidis, Mario Sznaier et al.
Iterative Supervised Principal Components
Juho Piironen, Aki Vehtari
Kernel Conditional Exponential Family
Michael Arbel, Arthur Gretton
Labeled Graph Clustering via Projected Gradient Descent
Shiau Hong Lim, Gregory Calvez
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Heng Guo, Kaan Kara, Ce Zhang
Learning Determinantal Point Processes in Sublinear Time
Christophe Dupuy, Francis Bach
Learning Generative Models with Sinkhorn Divergences
Aude Genevay, Gabriel Peyre, Marco Cuturi
Learning Hidden Quantum Markov Models
Siddarth Srinivasan, Geoff Gordon, Byron Boots
Learning linear structural equation models in polynomial time and sample complexity
Asish Ghoshal, Jean Honorio
Learning Priors for Invariance
Eric Nalisnick, Padhraic Smyth
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Asish Ghoshal, Jean Honorio