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2. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience
3. Advances in Machine Learning for Complex Structured Functional Data
4. Advances in Machine Learning for Compositional Data
5. Advances in Model Selection Techniques with Applications to Statistical Network Analysis and Recommender Systems
6. Advances in Statistical Machine Learning Methods for Neural Data Science
7. A Graphon-based Framework for Modeling Large Networks
8. An Assortment of Unsupervised and Supervised Applications to Large Data
9. A Point Process Model for the Dynamics of Limit Order Books
10. Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks
11. Asymptotic Theory and Applications of Random Functions
12. A unified view of high-dimensional bridge regression
13. Bayesian Modeling Strategies for Complex Data Structures, with Applications to Neuroscience and Medicine
14. Bayesian Model Selection in terms of Kullback-Leibler discrepancy
15. Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing
16. Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles
17. Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates
18. Contagion and Systemic Risk in Financial Networks
19. Contributions to Semiparametric Inference to Biased-Sampled and Financial Data
20. Credit Risk Modeling and Analysis Using Copula Method and Changepoint Approach to Survival Data
21. Deep Probabilistic Graphical Modeling
22. Detecting Dependence Change Points in Multivariate Time Series with Applications in Neuroscience and Finance
23. Distributionally Robust Optimization and its Applications in Machine Learning
24. Distributionally Robust Performance Analysis with Applications to Mine Valuation and Risk
25. Efficiency in Lung Transplant Allocation Strategies
26. Efficient Estimation of the Expectation of a Latent Variable in the Presence of Subject-Specific Ancillaries
27. Empirical Bayes, Bayes factors and deoxyribonucleic acid fingerprinting
28. Essays in Cluster Sampling and Causal Inference
29. Essays in High Dimensional Time Series Analysis
30. Essays on Matching and Weighting for Causal Inference in Observational Studies
31. Estimation and Testing Methods for Monotone Transformation Models
32. Event History Analysis in Multivariate Longitudinal Data
33. Expansion of a filtration with a stochastic process: a high frequency trading perspective
34. Flexible Sparse Learning of Feature Subspaces
35. Generalized Volatility-Stabilized Processes
36. High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices
37. High-dimensional asymptotics: new insights and methods
38. High dimensional information processing
39. Inference in ERGMs and Ising Models.
40. Interaction-Based Learning for High-Dimensional Data with Continuous Predictors
41. Kernel-based association measures
42. Large Dimensional Data Analysis using Orthogonally Decomposable Tensors: Statistical Optimality and Computational Tractability
43. Latent Variable Modeling and Statistical Learning
44. Latent Variable Models for Events on Social Networks
45. Latent Variable Models in Measurement: Theory and Application
46. Limit theorems beyond sums of I.I.D observations
47. Limit Theory for Spatial Processes, Bootstrap Quantile Variance Estimators, and Efficiency Measures for Markov Chain Monte Carlo
48. Linear Constraints in Optimal Transport
49. Martingale Schrodinger Bridges and Optimal Semistatic Portfolios
50. Mathematical Modeling of Insider Trading
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