UAI 2018

14.2 Causal Identification Under Markov Equivalence

14.2 Causal Identification Under Markov Equivalence
7.2 Learning Fast Optimizers For Contextual Stochastic Integer Programs

7.2 Learning Fast Optimizers For Contextual Stochastic Integer Programs
8.2 Constraint-Based Casual Discovery For Non-Linear Structural Causal Models

8.2 Constraint-Based Casual Discovery For Non-Linear Structural Causal Models
7.3 Abstraction Sampling In Graphical Models

7.3 Abstraction Sampling In Graphical Models
18.2 A Lagrangian Perspective On Latent Variable Generative Models

18.2 A Lagrangian Perspective On Latent Variable Generative Models
2. Bayesian Optimization

2. Bayesian Optimization
8.3 A Dual Approach To Scalable Verifiaction Of Deep Networks.mp4

8.3 A Dual Approach To Scalable Verifiaction Of Deep Networks.mp4
3.2 End To End QA

3.2 End To End QA
15. Reproducibility, Reusability, And Robustness In Deep Reinforcement Learning

15. Reproducibility, Reusability, And Robustness In Deep Reinforcement Learning
16.3 Non-Parametric Path Analysis In Structural Causal Models

16.3 Non-Parametric Path Analysis In Structural Causal Models
16. Causal Learning For Partially Observed Stochastic Dynamical Systems

16. Causal Learning For Partially Observed Stochastic Dynamical Systems
16.2 Identification Of Personalized Effects Associated With Causal Pathways

16.2 Identification Of Personalized Effects Associated With Causal Pathways
1.2 Addressing Data Security In Deep Learning.mp4

1.2 Addressing Data Security In Deep Learning.mp4
13.3 Discrete Sampling Using Semigradient-Based Product Mixtures

13.3 Discrete Sampling Using Semigradient-Based Product Mixtures
13.2 A Unified Particle-Optimization Framework For Scalable Bayesian Sampling

13.2 A Unified Particle-Optimization Framework For Scalable Bayesian Sampling
17. Towards Flatter Loss Surface Via Nonmonotonic Learning Rate Scheduling

17. Towards Flatter Loss Surface Via Nonmonotonic Learning Rate Scheduling
17.3 Revisiting Differentially Private Linear Regression

17.3 Revisiting Differentially Private Linear Regression
12.3 Variational Zero-Inflated Gaussian Process With Sparse Kernels

12.3 Variational Zero-Inflated Gaussian Process With Sparse Kernels
13. Lifted Marginal MAP Inference

13. Lifted Marginal MAP Inference
8. Adaptive Stratified Sampling For Precision-Recall Estimation

8. Adaptive Stratified Sampling For Precision-Recall Estimation
9. Bigger Data About Smaller People: Studying Children's Language Learning At Scale

9. Bigger Data About Smaller People: Studying Children's Language Learning At Scale
6.2 Sylvester Normalizing Flow For Variational Inference

6.2 Sylvester Normalizing Flow For Variational Inference
6.3 Hyperspherical Variational Auto-Encoders

6.3 Hyperspherical Variational Auto-Encoders
17.2 Averaging Weights Leads To Wider Optima And Better Generalization

17.2 Averaging Weights Leads To Wider Optima And Better Generalization
4. Recent Progress In The Theory Of Deep Learning

4. Recent Progress In The Theory Of Deep Learning
3. UAI Tutorial On Machine Reading

3. UAI Tutorial On Machine Reading
1. Tackling Data Scarcity In Deep Learning.mp4

1. Tackling Data Scarcity In Deep Learning.mp4
6. The Variational Homoencoder

6. The Variational Homoencoder
7. A Forest Mixture Bound For Block-Free Parallel Inference

7. A Forest Mixture Bound For Block-Free Parallel Inference
14. Causal Discovery With Linear Non-Gaussian Models Under Measurement Error

14. Causal Discovery With Linear Non-Gaussian Models Under Measurement Error