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NTHRYS › PhD Assistance › Computational Statistics

Computational Statistics

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Computational Statistics

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Bayesian Computational Methods for High-Dimensional Data
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Gradient-Free Optimization in Complex Landscapes
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Probabilistic Graphical Models Inference
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Approximate Bayesian Computation for Intractable Likelihoods
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Markov Chain Monte Carlo Convergence Analysis
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Variational Inference with Neural Networks
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Importance Sampling Methods and Adaptive Schemes
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Distributed Statistical Computing Algorithms
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Stochastic Gradient Descent Convergence Theory
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Sequential Monte Carlo Methods and Particle Filters
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Hamiltonian Monte Carlo and Geometric Methods
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Empirical Risk Minimization and Regularization
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Kernel Methods and Gaussian Process Computation
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Expectation-Maximization Algorithm Extensions
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Computational Challenges in Causal Inference
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Bootstrap Methods for Uncertainty Quantification
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Sparse Linear Regression and Feature Selection
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Matrix Factorization and Tensor Decomposition
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Reinforcement Learning with Statistical Guarantees
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Online Learning and Streaming Data Algorithms
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Variational Autoencoder Optimization Techniques
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Generative Adversarial Network Training Stability
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Sampling from Complex Energy-Based Models
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Latent Dirichlet Allocation and Topic Modeling
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Nonparametric Bayesian Computation and Inference
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Optimal Transport and Wasserstein Distances
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Neural Network Training Dynamics and Theory
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Quantile Regression Computation and Algorithms
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Survival Analysis Computational Methods
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Spatial Statistics and Kriging Computation
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Time Series Forecasting with Machine Learning
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High-Dimensional Multiple Testing Correction
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Mixture Model Estimation and Selection
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Cross-Validation and Model Selection Methods
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Permutation Testing and Resampling Inference
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Robust Statistics and Outlier Detection
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Bayesian Model Averaging Computation
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Semi-Supervised Learning Algorithms
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Transfer Learning and Domain Adaptation
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Federated Learning and Privacy-Preserving Statistics
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Information Geometry and Statistical Manifolds
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Natural Gradient Methods in Statistics
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Influence Functions and Sensitivity Analysis
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Clustering Algorithms and Computational Complexity
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Regression Trees and Ensemble Methods
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Classification Trees and Recursive Partitioning
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Anomaly Detection in High Dimensions
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Graphical Lasso and Sparse Precision Matrices
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Principal Component Analysis and Extensions
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Differentiable Programming for Statistical Inference
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Scalable Variational Inference for Massive Datasets
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Normalizing Flows for Density Estimation
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Score-Based Generative Models and Diffusion Processes
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Contrastive Divergence and Energy-Based Learning
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Invertible Neural Networks for Bijective Mappings
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Bayesian Deep Learning with Uncertainty Quantification
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Gibbs Sampling for Discrete Latent Variable Models
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Langevin Dynamics and Gradient-Based Sampling
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Variational Graph Autoencoders and Network Inference
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Expectation Propagation and Message Passing Algorithms
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Approximate Inference via Belief Propagation
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Neural Ordinary Differential Equations and Learning Dynamics
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Sliced Wasserstein Distance Approximation
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Neural Tangent Kernel Theory and Overparameterization
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Copula-Based Statistical Dependency Modeling
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Meta-Learning and Few-Shot Statistical Inference
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Causal Forest Methods for Treatment Effect Estimation
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Debiased Machine Learning for Policy Evaluation
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Autoencoder-Based Representation Learning Theory
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Extreme Value Statistics and Tail Risk Computation
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Hidden Markov Models and State Space Inference
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Optimal Experimental Design and Active Learning
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Gradient Boosting Machines and Additive Models
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Sparsity-Inducing Priors and Computational Methods
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Posterior Sampling via Data Augmentation Schemes
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Statistical Guarantees for Deep Reinforcement Learning
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Surrogate Models and Emulation for Expensive Simulations
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Reverse Engineering via Statistical Causal Discovery
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Approximate Message Passing and State Evolution
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Functional Data Analysis and Curve Registration
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Multi-Task Learning with Parameter Sharing
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Doubly Robust Estimation and Orthogonal Learning
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Poisson Approximation and Chen-Stein Methods
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Attention Mechanisms for Statistical Modeling
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Implicit Likelihood Models and Score Matching
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Randomized Linear Algebra for Statistical Computation
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Interacting Particle Systems and Mean-Field Approximations
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Subsampling for Scalable Bayesian Inference
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Graphical Model Structure Learning and Model Selection
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Stochastic Variational Inference with Natural Gradient
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Conformal Prediction and Distribution-Free Inference
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Mixture of Experts and Computational Scaling
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ABC Shadow Filtering for Partially Observable Systems
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Annealed Importance Sampling and Tempering Methods
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Smoothed Particle Inference and Likelihood-Free Methods
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Compositional Data Analysis and Log-Ratio Transformations
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Cyclical Learning Rates and Optimizer Tuning
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Geometric Ergodicity and MCMC Diagnostics
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Information Bottleneck and Compression Bounds
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Scalable Expectation Propagation for Massive Datasets
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Adaptive Metropolis-Hastings with Machine Learning Proposals
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Computational Methods for Copula-Based Inference
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Sparse Precision Matrix Estimation and Graph Learning
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Variational Bounds for Intractable Likelihood Models
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Parallel Tempering and Replica Exchange Methods
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Stochastic Optimization for Non-Convex Statistical Problems
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Approximate Inference in Continuous Markov Random Fields
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Spectral Methods for Statistical Learning and Estimation
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Variance Reduction Techniques in Monte Carlo Methods
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Computational Topology and Persistent Homology in Statistics
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Multivariate Functional Data Analysis Algorithms
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Accelerated Proximal Methods for Regularized Estimation
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Computational Inference for Point Process Models
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Sequential Anomaly Detection and Change Point Analysis
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Laplace Approximation and Higher-Order Methods
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Neural Operator Learning for Statistical Emulation
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Computational Methods for Latent Variable Models
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GPU-Accelerated Statistical Algorithms and Libraries
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Normalizing Flows for Flexible Posterior Approximation
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Composite Likelihood and Pairwise Likelihood Methods
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Manifold Learning and Dimensionality Reduction Algorithms
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Convex Relaxations for Combinatorial Statistical Problems
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Bayesian Optimization for Expensive Computer Experiments
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Computational Methods for Networked Data Analysis
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Score Matching and Contrastive Learning Methods
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Tensor Network and Belief Propagation Algorithms
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Data Augmentation Strategies for Statistical Computation
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Gradient-Based Sampling with Score Functions
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Coupling Methods for Probability Distance Estimation
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Multi-Task Learning and Meta-Learning Algorithms
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Bayesian Nonparametrics with Scalable Inference
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Computational Statistics for Differential Equations
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Slice Sampling and Adaptive Slice Schemes
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Causal Discovery through Constraint-Based Methods
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Minibatch Statistics and Subsampling Methods
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Gaussian Approximations and Mean Field Methods
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Computational Methods for Survival and Event History Data
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Neural Density Ratio Estimation and Classification
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Computational Methods for Mixed Effects Models
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Risk Minimization and Empirical Process Theory
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Computational Statistics for Image and Signal Analysis
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Quasi-Monte Carlo Methods and Randomization
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Diffusion-Based Generative Models and Sampling
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Computational Statistics for Genomic Data Analysis
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Amortized Inference with Conditional Neural Networks
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Coordinate Descent and Block Optimization Methods
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Computational Methods for Count and Categorical Data
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Probabilistic Program Inference and Synthesis
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Adaptive Metropolis-Hastings and Convergence Diagnostics
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Computational Methods for Causal Graphs Discovery
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Differentiable Monte Carlo Estimators
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Approximate Inference in Continuous-Time Models
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Score-Based Generative Modeling Algorithms
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Debiasing Stochastic Gradient Estimates
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Continuous Normalizing Flows and ODE Solvers
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Stein Variational Descent Methods
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Accelerated Proximal Algorithms for Statistics
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Computationally Efficient Model Checking Methods
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Selective Inference and Conditioning Algorithms
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Randomized Matrix Algorithms for Linear Regression
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Consensus and Distributed MCMC Methods
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Scalable Expectation Propagation Algorithms
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Gradient Flow Dynamics and Implicit Bias
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Computational Topology and Persistent Homology
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Variational Approximations for Mixed Effects Models
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Reparameterization Tricks for Discrete Latent Variables
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Spectral Methods for Nonparametric Statistics
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Computational Inference in Epidemic Models
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GPU-Accelerated Statistical Computing
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Splitting and Alternating Direction Methods
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Empirical Likelihood Computation and Optimization
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Neural Ordinary Differential Equations for Inference
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Minimax Optimal Algorithms and Lower Bounds
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Bayesian Optimization and Active Learning
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Computational Methods for Functional Data Analysis
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Scalable Gaussian Process Approximations
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Computation Under Privacy Constraints
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Path Sampling and Thermodynamic Integration
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Quantile-Based and Robust Optimization Methods
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Iteratively Reweighted Algorithms for Sparsity
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Sampling from Intractable Densities via Coupling
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Computational Approaches to Time-Varying Networks
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High-Dimensional Covariance Estimation and Shrinkage
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Doubly Robust Computation and Semiparametric Inference
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Simulation-Based Calibration and Diagnostics
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Data Augmentation and Latent Variable Algorithms
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Convex Relaxations and Semidefinite Programming
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Online Convex Optimization for Statistics
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Computational Inference in Matching and Causality
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Quasi-Monte Carlo Methods in Bayesian Inference
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Structure-Exploiting Optimization for Graphical Models
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Scalable Inference for Massive Graph Networks
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Computational Methods for Categorical Data Analysis
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Adaptive Monte Carlo for Multimodal Posteriors
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Second-Order Methods and Newton Algorithms
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Computational Aspects of Multiple Hypothesis Testing
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Debiased Machine Learning and Causal Effect Estimation
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Laplace Approximations and Saddle Point Methods
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Differentiable Probabilistic Programming Languages
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Computational Optimal Experimental Design
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