Все публикации

14.2 Causal Identification

14.2 Causal Identification Under Markov Equivalence

18.2 A Lagrangian

18.2 A Lagrangian Perspective On Latent Variable Generative Models

7.3 Abstraction Sampling

7.3 Abstraction Sampling In Graphical Models

8.2 Constraint-Based Casual

8.2 Constraint-Based Casual Discovery For Non-Linear Structural Causal Models

7.2 Learning Fast

7.2 Learning Fast Optimizers For Contextual Stochastic Integer Programs

2. Bayesian Optimization

2. Bayesian Optimization

8.3 A Dual

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

3.2 End To

3.2 End To End QA

15. Reproducibility, Reusability,

15. Reproducibility, Reusability, And Robustness In Deep Reinforcement Learning

16.3 Non-Parametric Path

16.3 Non-Parametric Path Analysis In Structural Causal Models

16. Causal Learning

16. Causal Learning For Partially Observed Stochastic Dynamical Systems

16.2 Identification Of

16.2 Identification Of Personalized Effects Associated With Causal Pathways

1.2 Addressing Data

1.2 Addressing Data Security In Deep Learning.mp4

17.3 Revisiting Differentially

17.3 Revisiting Differentially Private Linear Regression

13.2 A Unified

13.2 A Unified Particle-Optimization Framework For Scalable Bayesian Sampling

12.3 Variational Zero-Inflated

12.3 Variational Zero-Inflated Gaussian Process With Sparse Kernels

13.3 Discrete Sampling

13.3 Discrete Sampling Using Semigradient-Based Product Mixtures

17. Towards Flatter

17. Towards Flatter Loss Surface Via Nonmonotonic Learning Rate Scheduling

13. Lifted Marginal

13. Lifted Marginal MAP Inference

8. Adaptive Stratified

8. Adaptive Stratified Sampling For Precision-Recall Estimation

9. Bigger Data

9. Bigger Data About Smaller People: Studying Children's Language Learning At Scale

6.3 Hyperspherical Variational

6.3 Hyperspherical Variational Auto-Encoders

4. Recent Progress

4. Recent Progress In The Theory Of Deep Learning

3. UAI Tutorial

3. UAI Tutorial On Machine Reading