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2. Inference in ERGMs and Ising Models.
3. Large Dimensional Data Analysis using Orthogonally Decomposable Tensors: Statistical Optimality and Computational Tractability
4. Martingale Schrodinger Bridges and Optimal Semistatic Portfolios
5. On the Multiway Principal Component Analysis
6. Optimal Inference with a Multidimensional Multiscale Statistic
7. Process Data Applications in Educational Assessment
8. Signal-to-noise ratio aware minimaxity and its asymptotic expansion
9. Statistical Methods for Structured Data: Analyses of Discrete Time Series and Networks
10. Advances in Machine Learning for Complex Structured Functional Data
11. Advances in Machine Learning for Compositional Data
12. Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing
13. Latent Variable Models for Events on Social Networks
14. Modeling Random Events
15. Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling
16. On Recovering the Best Rank-? Approximation from Few Entries
17. Overlapping Communities on Large-Scale Networks: Benchmark Generation and Learning via Adaptive Stochastic Optimization
18. Statistical approach to tagging stellar birth groups in the Milky Way
19. Statistical Perspectives on Modern Network Embedding Methods
20. Advances in Statistical Machine Learning Methods for Neural Data Science
21. Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles
22. Event History Analysis in Multivariate Longitudinal Data
23. High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices
24. On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods
25. Phase retrieval in the high-dimensional regime
26. Semiparametric Inference of Censored Data with Time-dependent Covariates
27. Statistical Learning for Process Data
28. Toward a scalable Bayesian workflow
29. Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates
30. Deep Probabilistic Graphical Modeling
31. High-dimensional asymptotics: new insights and methods
32. Latent Variable Models in Measurement: Theory and Application
33. Multiple Causal Inference with Bayesian Factor Models
34. New perspectives in cross-validation
35. Partition-based Model Representation Learning
36. Some Statistical Models for Prediction
37. Statistical Analysis of Complex Data in Survival and Event History Analysis
38. Time evolution of the Kardar-Parisi-Zhang equation
39. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience
40. Essays in High Dimensional Time Series Analysis
41. Limit theorems beyond sums of I.I.D observations
42. Linear Constraints in Optimal Transport
43. Point Process Models for Heterogeneous Event Time Data
44. Scalable Community Detection in Massive Networks using Aggregated Relational Data
45. Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks
46. Bayesian Modeling Strategies for Complex Data Structures, with Applications to Neuroscience and Medicine
47. Essays in Cluster Sampling and Causal Inference
48. Minimax-inspired Semiparametric Estimation and Causal Inference
49. Selected Legal Applications for Bayesian Methods
50. Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data
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