22:12
14.2 Causal Identification Under Markov Equivalence
20:23
7.2 Learning Fast Optimizers For Contextual Stochastic Integer Programs
16:31
8.2 Constraint-Based Casual Discovery For Non-Linear Structural Causal Models
19:33
7.3 Abstraction Sampling In Graphical Models
17:42
18.2 A Lagrangian Perspective On Latent Variable Generative Models
01:34:54
2. Bayesian Optimization
20:42
8.3 A Dual Approach To Scalable Verifiaction Of Deep Networks.mp4
49:32
3.2 End To End QA
47:12
15. Reproducibility, Reusability, And Robustness In Deep Reinforcement Learning
18:17
16.3 Non-Parametric Path Analysis In Structural Causal Models
16:43
16. Causal Learning For Partially Observed Stochastic Dynamical Systems
17:15
16.2 Identification Of Personalized Effects Associated With Causal Pathways
37:46
1.2 Addressing Data Security In Deep Learning.mp4
15:54
13.3 Discrete Sampling Using Semigradient-Based Product Mixtures
20:38
13.2 A Unified Particle-Optimization Framework For Scalable Bayesian Sampling
15:35
17. Towards Flatter Loss Surface Via Nonmonotonic Learning Rate Scheduling
21:21
17.3 Revisiting Differentially Private Linear Regression
16:57
12.3 Variational Zero-Inflated Gaussian Process With Sparse Kernels
19:52
13. Lifted Marginal MAP Inference
16:19
8. Adaptive Stratified Sampling For Precision-Recall Estimation
01:06:54
9. Bigger Data About Smaller People: Studying Children's Language Learning At Scale
15:57
6.2 Sylvester Normalizing Flow For Variational Inference
15:23
6.3 Hyperspherical Variational Auto-Encoders
14:06
17.2 Averaging Weights Leads To Wider Optima And Better Generalization
01:19:14
4. Recent Progress In The Theory Of Deep Learning
39:21
3. UAI Tutorial On Machine Reading
01:02:46
1. Tackling Data Scarcity In Deep Learning.mp4
15:35
6. The Variational Homoencoder
17:27
7. A Forest Mixture Bound For Block-Free Parallel Inference
18:23
14. Causal Discovery With Linear Non-Gaussian Models Under Measurement Error
17:29
18.3 Constant Step Size Stochastic Gradient Descent For Probabilistic Modeling
18:13
11.3 Finite-State Controllers Of POMDP's Using Parameter Synthesis
18:35
11. Fast Policy Learning Through Imitation
21:13
11.2 Comparing Direct And Indirect Temp.-Diff. Methods For Estimating The Variance Of The Return
16:50
12.2 Sampling And Inference For Beta Neutral-To-The-Left Models Of Sparse Networks
01:03:28
10. Uncertainty In Objectives
14:24
12. Efficient Bayesian Inference For A Gaussian Process Density Model
16:13
18. Unsupervised Learning Of Latent Physical Properties Using Perception-Prediction Networks
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