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NTHRYS › PhD Assistance › Optimization Science

Optimization Science

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Optimization Science

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Convex Optimization Theory and Applications
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Stochastic Gradient Descent Convergence Analysis
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Mixed-Integer Programming Algorithms
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Distributed Optimization over Networks
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Nonconvex Optimization Landscape Analysis
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Federated Learning Optimization
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Bilevel Optimization Methods
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Conic Programming and Interior Point Methods
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Variance Reduction in Stochastic Optimization
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Online Optimization and Regret Analysis
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Derivative-Free Optimization Algorithms
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Proximal Methods and Operator Splitting
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Quantum Optimization Algorithms
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Robust Optimization under Uncertainty
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Portfolio Optimization and Financial Mathematics
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Combinatorial Optimization and Approximation
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Neural Network Training Optimization
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Semidefinite Programming Applications
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Momentum-Based Methods and Acceleration
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Second-Order Optimization Methods
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Supply Chain Optimization
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Power Systems and Smart Grid Optimization
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Machine Learning with Optimization
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Constraint Handling in Evolutionary Algorithms
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Multi-Objective Optimization Algorithms
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Inverse Optimization and Machine Teaching
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Decentralized Machine Learning at Edge
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Manifold Optimization Techniques
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Online Convex Optimization Games
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Sparse Recovery and Compressed Sensing
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Topology Optimization and Shape Design
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Frank-Wolfe and Conditional Gradient Methods
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Bandit Optimization and Sequential Selection
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Stochastic Variational Inference
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Resource Allocation in Cloud Computing
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Differentially Private Optimization
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Optimal Control and Trajectory Optimization
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Graph-Based Optimization and Message Passing
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Nonsmooth Optimization Theory
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Scheduling and Resource Optimization
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Zeroth-Order Optimization Methods
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Sum-of-Squares and Polynomial Optimization
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Dynamic Programming and Optimal Substructure
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Metaheuristics and Nature-Inspired Algorithms
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Learning Rates and Adaptive Methods
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Geometric Deep Learning Optimization
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Auction Design and Mechanism Optimization
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Time-Series Forecasting Optimization
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Hyperparameter Optimization and AutoML
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Game Theory and Equilibrium Computation
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Asynchronous Distributed Optimization Algorithms
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Variational Inequality Problems and Solutions
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Frank-Wolfe Variants and Extensions
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Primal-Dual Optimization Methods
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Zeroth-Order Black-Box Optimization
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Federated Multi-Task Learning Optimization
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Compositional Optimization and Hierarchical Problems
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Adversarial Robustness in Optimization Training
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Augmented Lagrangian and Penalty Methods
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Optimization in Reproducing Kernel Hilbert Spaces
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Byzantine-Resilient Distributed Optimization
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Stochastic Approximation and Robbins-Monro Methods
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Optimization Over Simplices and Polytopes
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Operator Splitting and Alternating Direction Methods
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Stochastic Mirror Descent and Mirror Descent Methods
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Optimization with Time-Varying Networks
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Trust Region Methods and Local Convergence
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Optimization in Hyperbolic Spaces and Riemannian Geometry
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Coordinate Descent and Block Coordinate Methods
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Optimization for Imbalanced Data Classification
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Optimization Under Partial Information and Feedback
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Submodular Optimization and Greedy Algorithms
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Optimization for Generative Adversarial Networks
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Natural Gradient Descent and Information Geometry
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Optimization for Sparse Neural Networks
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Optimization with Orthogonality Constraints
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Catalyst Acceleration and Universal Methods
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Optimization for Reinforcement Learning Control
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Majorization-Minimization and Expectation Maximization
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Optimization for Matrix Completion and Recovery
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Parallel and GPU-Accelerated Optimization Algorithms
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Optimization with Curvature Information and Hessian Methods
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Optimization for Vision Transformers and Attention
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Optimization with Inexact Gradients and Errors
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Markov Chain Monte Carlo Optimization Sampling
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Optimization for Causal Inference and Discovery
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Optimization in Presence of Computational Noise
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Optimization for Continual and Lifelong Learning
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Optimization Using Sketching and Dimensionality Reduction
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Optimization for Graph Neural Network Training
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Optimization Under Distribution Shift and Domain Adaptation
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Optimization for Kernel Methods and Kernel Learning
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Coordinate-Free and Geometric Optimization Methods
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Optimization for Tensor Networks and Decomposition
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Optimization with Chaotic Dynamics and Escape Saddles
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Optimization for Label Noise and Weak Supervision
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Optimization in Wasserstein Spaces and Optimal Transport
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Optimization with Subsampled Hessian Information
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Optimization for Graph Cuts and Image Segmentation
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Optimization Under Memory and Communication Constraints
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Asynchronous Distributed Optimization Convergence
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Composite Optimization and Proximal Splitting
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Saddle Point Optimization Dynamics
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Stochastic Variance-Reduced Mirror Descent
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Distributed First-Order Methods with Delays
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Nonconvex Federated Optimization Privacy
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Coordinate Descent for Large-Scale Learning
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Variance-Aware Adaptive Learning Rates
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Submodular Optimization and Greedy Algorithms
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Variational Inequality Methods and Applications
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Projection-Free Conditional Gradient Methods
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Zeroth-Order Bandit Feedback Optimization
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Catalytic Gradient Methods Acceleration
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Distributed Nonconvex Optimization Landscape
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Primal-Dual Algorithm Design and Analysis
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Structured Sparsity and Group Regularization
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Optimization Under Limited Feedback Information
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Decentralized Consensus Optimization Networks
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Smoothing Techniques for Nonsmooth Problems
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Variance Reduction with Importance Sampling
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Extragradient Methods Monotone Operators
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Sparse Optimization and Compressed Sensing
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Optimization on Riemannian Manifolds
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Gradient Compression Quantization Communication
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Dual Decomposition and Subgradient Methods
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Mirror Descent and Bregman Divergences
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Inexact Optimization Error Analysis
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Stochastic Heavy Ball and Polyak Momentum
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Gossip Algorithms and Consensus
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Sketching Methods Dimensionality Reduction
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Proximal Policy Optimization Reinforcement
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Dual Averaging and Online Prediction
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Optimization with Coupled Constraints
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Fast Saddle Point Escape Methods
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Stochastic Proximal Gradient Variants
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Optimization with Switching Constraints
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Nesterov Acceleration Beyond Convexity
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Optimization for Tensor Decomposition
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Forward-Backward Splitting Algorithms
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Optimization Under Concept Drift
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Cubic Regularization Newton Methods
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Optimization for Matrix Factorization
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Trust Region Methods Nonconvex
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Optimization with Imperfect Oracles
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Quasi-Newton Methods and Secant Updates
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Optimization for Operator Equations
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Distributed Gradient Tracking Methods
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Optimization with Random Projections
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Stochastic Optimization Generalization Bounds
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Optimization for Spectral Methods
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Composite Optimization with Structured Sparsity
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Stochastic Variational Optimization Inequalities
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Byzantine-Robust Federated Optimization Methods
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Operator Splitting for Large-Scale Problems
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Saddle Point Escape and Local Geometry
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Federated Optimization with Personalization
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Parametric Uncertainty in Robust Control
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Tensor Decomposition and Low-Rank Optimization
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Conditional Value-at-Risk Optimization
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Sketching-Based Optimization for Massive Data
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Primal-Dual Methods with Adaptive Metrics
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Lifted Representations for Combinatorial Problems
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Gradient Compression and Quantization Effects
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Matrix Completion with Side Information
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Dual Decomposition for Network Problems
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Stochastic Coordinate Descent Variance
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Nonconvex-Concave Minimax Optimization
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Constrained Optimization over Riemannian Manifolds
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Accelerated Methods for Ill-Conditioned Problems
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Multi-Time-Scale Stochastic Approximation
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Optimization with Sample-Dependent Constraints
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Cooperative Multi-Agent Optimization Games
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Variance-Reduced Policy Gradient Methods
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Cutting Plane Methods for Structured Problems
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Time-Varying Network Optimization Algorithms
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Variational Inference with Implicit Models
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Gradient Tracking and Distributed Control
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Hyperparameter-Free Adaptive Algorithms
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Lifted Convex Relaxations Combinatorics
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Optimization Under Long-Range Dependencies
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Gaussian Process Bandit Optimization
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Nonsmooth Nonconvex Optimization Landscapes
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Optimization with Exogenous Information Structure
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Partial Relaxation and Implicit Gradients
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Convex Geometry and Optimization Complexity
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Optimization with Latency and Communication Costs
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Hypergradient Optimization and Meta-Learning
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Optimal Transport and Distributionally Robust
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Coordinate-Wise Variance Reduction Acceleration
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Evolutionary Optimization with Recombination
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Optimization-Based Physics-Informed Neural Networks
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Personalized Federated Risk Minimization
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Incremental Aggregated Gradient Methods
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Asynchronous Distributed Optimization with Delays
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Constrained Markov Decision Process Optimization
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Zeroth-Order Federated Learning in Heterogeneous Settings
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Warm-Starting and Transfer in Optimization
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Operator Splitting for Large-Scale Machine Vision
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Stochastic Optimization Under Distribution Shift
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Submodular Maximization and Approximations
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