ALLERTON 2023: 2023 ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, & COMPUTING

This page contains an index consisting of author-provided keywords.

2
2D unknown view tomography	Rotation-Invariant Correlation Features for 2D Sparse Unknown View Tomography
3
3D printing	Coding for forensic 3D fingerprinting
A
Absorbing sets	Trapping and Absorbing Set Enumerators for Multi-Edge Type LDPC Code Ensembles
Access-redundancy	Coefficient Complexity in Low-Access Quantized Linear Computations
Acheivable	Coded Caching with Linear Coded Placement: Exact Tradeoff for the Three User Case
active learning	Active Ranking without Strong Stochastic Transitivity
Actuator Faults	Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry
adaptive algorithms	Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
Adaptive Control	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Adaptive Exploration	Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
Adaptive filtering	Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach
adaptive identification	Adaptive Identification from a Support Vector Perspective
Adaptive systems	Dynamic Tolling in Arc-based Traffic Assignment Models
Adaptivity	DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
Adversarial analysis of perception/neural network-based control systems	Vulnerability Analysis of Perception-Based Control: Modeling and Fundamental Limits
Adversarial Attacks	Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
adversarial machine learning	Characterizing optimal loss for adversarial classification versus multiple adversaries
adversarial network	Multishot Adversarial Network Decoding
age of information	Age of Gossip on a Grid
aggregation
Aggregative Games	Characterization of Potential Games: Application in Aggregative Games
Aggregative Potential Games Characterization	Characterization of Potential Games: Application in Aggregative Games
AIMD algorithm	Communication-efficient Federated Multi-resource Allocation: A Non-homogeneous Markov Chain Model
alpha posteriors	Asymptotics for alpha-posteriors with applications to variational inference
altruistic player	The Effect of Altruism in the Electricity Network
Analog source-channel coding	Non-Linear Analog Coding for Image Transmission over Noisy Channels
anomaly detection	Data-Driven Sequential Change Detection in Privacy-Sensitive Networks
anomaly detectors	Vulnerability Analysis of Perception-Based Control: Modeling and Fundamental Limits
asymptotic behaviors	Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants
Asymptotic enumeration	Trapping and Absorbing Set Enumerators for Multi-Edge Type LDPC Code Ensembles
Asymptotic normality	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
Asymptotic optimality	Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Asymptotic statistics	Asymptotics for alpha-posteriors with applications to variational inference
attack resilient control	Vulnerability Analysis of Perception-Based Control: Modeling and Fundamental Limits
Augustin information	Privacy Amplification Against Quantum Side Information Via Regular Random Binning
autonomous systems	Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Average Delay	Delay Performance Optimization with Packet Drop
Average run length	Sequential Change-Point Detection for Correlation Matrices
AWGN channel	Optimal Error Exponent of Type-Based Distributed Hypothesis Testing over AWGN Channels
B
Backward Reachability Analysis	Backward Reachability Analysis for Safety Verification of Neural Feedback Loops
Bandit	Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle
bandits	Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
barrier functions	Towards Rigorous Use of Neural Networks in Nonlinear Control
Bayesian Decentralized Learning	A variational approach to federated and robust learning
Bayesian Statistics	Curse of Dimension of Thompson Sampling for Combinatorial Bandits
Bee identification problem	The Bee Identification Problem and DNA-Based Data Storage
benign overfitting	Benign overfitting, calibration, and the benefits of early stopping
best arm identification	Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
Best policy identification	Best Policy Identification in Linear MDPs
Bias reduction	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Bootstrap	Group Fairness with Uncertainty in Sensitive Attributes
Byzantine Resilience	Byzantine-resilient Federated Low Rank Column-wise Compressive Sensing
C
calibration	Benign overfitting, calibration, and the benefits of early stopping
Carleman linearization	Super-linearization of Polynomial Systems
Catastrophic forgetting	Theory on Forgetting and Generalization of Continual Learning
Causal graph recovery	A Density Evolution Framework for Recovery of Covariance and Causal Graphs from Compressed Measurements
Certifiable estimation	Practical Algorithms for Globally Optimal Geometric Machine Perception
certified guarantees	Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Certified Robustness	Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
change detection	Data-Driven Sequential Change Detection in Privacy-Sensitive Networks
Change-point detection	Non-parametric distribution-free CUSUM for online change-point detection
Channel Capacity	Achieving Capacity on Non-Binary Channels with Generalized Reed–Muller Codes
Channel Simulation	Multi-terminal Strong Coordination over Noiseless Networks with Secrecy Constraints
characteristic graph	An Achievable Low Complexity Encoding Scheme for Coloring Cyclic Graphs
Chemical Reaction Network	Verifying Chemical Reaction Networks with the Isabelle Theorem Prover
Chen--Fliess series	Linear Operator Differential Equations with Applications to Linear Time-Varying Systems
Chen-Fliess series	Backward and Inner Approximation of Output Reachable sets via Chen-Fliess Series
chromatic number	An Achievable Low Complexity Encoding Scheme for Coloring Cyclic Graphs
Client selection	Near Optimal Differentially Private Client Selection in Federated Settings
Closed-loop identifiability	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning
Clustering	Clustering in transformers: an interacting particle systems perspective
Coded Caching	Coded Caching with Linear Coded Placement: Exact Tradeoff for the Three User Case
coded computation	Coefficient Complexity in Low-Access Quantized Linear Computations
coding	Cover Your Bases: How to Minimize the Sequencing Coverage in DNA Storage Systems
Colored flows	Push-sum, Kullback-Leibler Barycenters, and Colored Flows
Combinatorial Bandits	Curse of Dimension of Thompson Sampling for Combinatorial Bandits
Combinatorial batch codes	New results on Erasure Combinatorial Batch Codes
Community detection	Graph Matching in Correlated Stochastic Block Models for Improved Graph Clustering
Compressed sensing	A Density Evolution Framework for Recovery of Covariance and Causal Graphs from Compressed Measurements
Compression-based recovery	Theoretical Analysis of Binary Masks in Snapshot Compressive Imaging Systems
Compressive Sensing	A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing Byzantine-resilient Federated Low Rank Column-wise Compressive Sensing Non-Linear Analog Coding for Image Transmission over Noisy Channels
Computation	Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing
Computational imaging	Rotation-Invariant Correlation Features for 2D Sparse Unknown View Tomography
computational method	A Differential Dynamic Programming-based Approach for Balancing Energy and Time Optimality in Motion Planning
Computer vision	Practical Algorithms for Globally Optimal Geometric Machine Perception
Concentration	Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
cone programming	Any-dimensional convex sets
confirmation bias	A Multidimensional Opinion Evolution Model with Confirmation Bias
Conformal prediction	Conformal Prediction for STL Runtime Verification
Consecutive files	New results on Erasure Combinatorial Batch Codes
constant geometric rate	A Law of Data Separation in Deep Learning
Constant stepsize	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
constrained codes	Embracing errors is more effective than avoiding them through constrained coding for DNA data storage
Constrained control	Towards constrained grid-forming control
contrastive learning	On representation learning with the InfoNCE loss
convergence	A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
convergence analysis	Weights Matter: Linear & Superlinear Convergence Rates for IRLS in Low-Rank Optimization
Converse Bound	Coded Caching with Linear Coded Placement: Exact Tradeoff for the Three User Case
convex optimization	Any-dimensional convex sets
convexity	Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
Cooperation	Non-myopic agents can stabilize cooperation in feedback-evolving games
Cooperative MARL	An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
Correlated Disturbances	Stochastic Control under Correlated Disturbances
Correlated stochastic block models	Graph Matching in Correlated Stochastic Block Models for Improved Graph Clustering
Correlation matrices	Sequential Change-Point Detection for Correlation Matrices
corrupted linear systems	On Subsampled Quantile Randomized Kaczmarz
Counterfactual Inference	On counterfactual inference with unobserved confounding
coupon collector problem	Cover Your Bases: How to Minimize the Sequencing Coverage in DNA Storage Systems
Covariance recovery	A Density Evolution Framework for Recovery of Covariance and Causal Graphs from Compressed Measurements
covering codes	Coefficient Complexity in Low-Access Quantized Linear Computations
CUSUM	An Adaptive Approach for Online Monitoring of Large Scale Data Streams Non-parametric distribution-free CUSUM for online change-point detection
D
data separation	A Law of Data Separation in Deep Learning
Datastreams	DOLL: Distributed OnLine Learning Using Preemptible Instances
Decentralized algorithms	A Unified View of Decentralized Algorithms for Sparse Linear Regression
Decentralized control	Aggregating Power Networks for Decentralized Control: A Spectral Clustering Approach
decentralized partially observable Markov decision process (Dec-POMDP)	Distributed MARL for Scheduling in Conflict Graphs
Decentralized Reinforcement Learning	A variational approach to federated and robust learning
Decentralized Stochastic Control	Reinforcement Learning for Stochastic Control under Weak Feller Continuous Models and General Information Structures
decentralized system	Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Decision Feedback Equalization	Subspace Methods for Blind Fractionally-Spaced Equalization of Distributed Phase-Shift Beamforming Signals
decision theory	Characterizing optimal loss for adversarial classification versus multiple adversaries
decision-dependent problems	Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
deep learning	The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks A Curriculum Learning Approach to Optimization with Application to Downlink Beamforming Exploring Gradient Oscillation in Deep Neural Network Training Explorations on scalable preconditioned gradient methods for deep learning Why interpolating neural nets generalize well: recent insights from neural tangent model A Law of Data Separation in Deep Learning
Deep unfolding	Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach
Delay Performance Optimization	Delay Performance Optimization with Packet Drop
Demand-side management	An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
design	Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Detection delay	Sequential Change-Point Detection for Correlation Matrices
deterministic approximation	Phase Transitions for Diversity in Stochastic Block Model Dynamics
differential privacy	The Saddle-Point Method in Differential Privacy Data-Driven Sequential Change Detection in Privacy-Sensitive Networks Training generative models from privatized data Near Optimal Differentially Private Client Selection in Federated Settings
diffusion approximation	Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants
diffusion models	Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains
Distributed algorithms	Privacy-Preserving Distributed k-means Clustering with Quantized Communication in Finite Time
Distributed Beamforming	Subspace Methods for Blind Fractionally-Spaced Equalization of Distributed Phase-Shift Beamforming Signals
Distributed computing	Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Distributed control	An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
Distributed Data-Parallel Shampoo	Explorations on scalable preconditioned gradient methods for deep learning
distributed detection	Fairness in Distributed Sensor Systems to Detect Emitters
Distributed energy resources	Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities
distributed learning	Distributed Optimization with Imperfect Information Sharing
Distributed optimization	Push-sum, Kullback-Leibler Barycenters, and Colored Flows Distributed Optimization with Imperfect Information Sharing Communication-efficient Federated Multi-resource Allocation: A Non-homogeneous Markov Chain Model Near Optimal Differentially Private Client Selection in Federated Settings
Distributed Storage	Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths
Distributed system	Optimal Error Exponent of Type-Based Distributed Hypothesis Testing over AWGN Channels
distributed systems	Straggler Exploitation in Distributed Computing Systems with Task Grouping
distribution-free	Non-parametric distribution-free CUSUM for online change-point detection
distributionally robust control	Wasserstein Distributionally Robust Regret-Optimal Control under Partial Observability
diversity	Phase Transitions for Diversity in Stochastic Block Model Dynamics
DNA based data storage	The Bee Identification Problem and DNA-Based Data Storage
DNA data storage	Embracing errors is more effective than avoiding them through constrained coding for DNA data storage
DNA storage	Cover Your Bases: How to Minimize the Sequencing Coverage in DNA Storage Systems
downlink beamforming	A Curriculum Learning Approach to Optimization with Application to Downlink Beamforming
dynamic population	Learning in Network Games with Dynamic Populations
Dynamic Programming	Stochastic Control under Correlated Disturbances
Dynamic Tolling	Dynamic Tolling in Arc-based Traffic Assignment Models
dynamic traffic	Distributed MARL for Scheduling in Conflict Graphs
dynamical systems	Phase Transitions for Diversity in Stochastic Block Model Dynamics
E
early stopping	Benign overfitting, calibration, and the benefits of early stopping
eigenvalues	An Achievable Low Complexity Encoding Scheme for Coloring Cyclic Graphs
electrical vehicles	Dynamic Load Balancing of Selfish Drivers between Spatially Distributed Electrical Vehicle Charging Stations
Electricity market	The Effect of Altruism in the Electricity Network
electricity markets
electricity rates	Quantifying Confidence in Electricity Rate Plan Recommendations Considering Variability in Future Electricity Usage
Energy communities	Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities
Energy-delay tradeoff	On Energy-Delay Tradeoff in Uncoordinated MAC
Entropic regularization	Training generative models from privatized data
epidemics	Adaptive Identification from a Support Vector Perspective
equilibrium analysis	The Effect of Altruism in the Electricity Network
Erasures combinatorial batch codes	New results on Erasure Combinatorial Batch Codes
Error correcting codes	Coding for forensic 3D fingerprinting
error exponent	Privacy Amplification Against Quantum Side Information Via Regular Random Binning
estimation	Adaptive Identification from a Support Vector Perspective
EV charging network control	An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
Evolutionary game theory	Non-myopic agents can stabilize cooperation in feedback-evolving games
Excess distortion probability	Single-Shot Lossy Compression for Joint Inference and Reconstruction
explainability	Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Exploration vs. Exploitation	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning
Exponential Family	On counterfactual inference with unobserved confounding
extremum seeking	Online Parameter Estimation in Partially Observed Markov Decision Processes
F
Failure Detection of Learning Systems	Discovering and Learning from Closed-Loop Failures of Vision-based Controllers.
Fair Machine Learning	Learning Fair Classifiers via Min-Max F-divergence Regularization
fairness	Fairness in Distributed Sensor Systems to Detect Emitters
Fairness Accuracy Tradeoffs	Learning Fair Classifiers via Min-Max F-divergence Regularization
False discovery rate	An Adaptive Approach for Online Monitoring of Large Scale Data Streams
false positive control	Human-in-the-Loop Out-of-Distribution Detection with False Positive Rate Control
Fault Detection	Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry
feature attributions	Stability Guarantees for Feature Attributions with Multiplicative Smoothing
feature extraction	Feature Geometry of Multivariate Dependence and Applications
feature learning	Sequential Dependence Decomposition and Feature Learning
FedAvg and FedProx	A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
federated learning	Fedsoft: Soft clustered federated learning with proximal local updating Incentive Mechanism Design for Federated Learning and Unlearning High Probability Guarantees For Federated Learning. A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
Federated optimization	Communication-efficient Federated Multi-resource Allocation: A Non-homogeneous Markov Chain Model Near Optimal Differentially Private Client Selection in Federated Settings
federated recommendation system
federated unlearning	Incentive Mechanism Design for Federated Learning and Unlearning
Federation	AltGDMin for Fast Federated Low Rank Matrix Completion
feedback	Coded Kalman Filtering Over Gaussian Channels with Feedback
Finite blocklength coding	On Energy-Delay Tradeoff in Uncoordinated MAC
Fisher divergence	Score-Based Bayesian Quickest Change Detection
Fixed Point	Stochastic Game with Interactive Information Acquisition: A Fixed-Point Alignment Principle
Formal Methods	Discovering and Learning from Closed-Loop Failures of Vision-based Controllers.
formal power series	Linear Operator Differential Equations with Applications to Linear Time-Varying Systems
Forward model	Best Policy Identification in Linear MDPs
Fractionally Spaced Equalization	Subspace Methods for Blind Fractionally-Spaced Equalization of Distributed Phase-Shift Beamforming Signals
Free-Space Communication	Quantum Key Distribution with State Replacement
frequency control	Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Function compression	An Achievable Low Complexity Encoding Scheme for Coloring Cyclic Graphs
functional differential equations	Linear Operator Differential Equations with Applications to Linear Time-Varying Systems
G
game theory	Fundamental Limits of Strategic Coordination Communication Enabled Collaborations in Multi-Agent Systems Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing Strategic Quantization of a Noisy Source Dynamic Tolling in Arc-based Traffic Assignment Models Incentive Mechanism Design for Federated Learning and Unlearning
GAN	Training generative models from privatized data
Gaussian barycenters	Push-sum, Kullback-Leibler Barycenters, and Colored Flows
Gaussian point source model	Rotation-Invariant Correlation Features for 2D Sparse Unknown View Tomography
Gaussian Process Regression	Inverse Reinforcement Learning with Learning and Leveraging Demonstrators' Varying Expertise Levels
Gaussian sequence model	Likelihood-free hypothesis testing
Generalization	Theory on Forgetting and Generalization of Continual Learning Why interpolating neural nets generalize well: recent insights from neural tangent model
generative model	Generative Plug and Play: Posterior Sampling for Inverse Problems
Generative modeling	Topology-aware Piecewise Linearization of the AC Power Flow through Generative Modeling
Global optimization	Practical Algorithms for Globally Optimal Geometric Machine Perception
gossip networks	Age of Gossip on a Grid
Gradient Descent	A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing Strategic Quantization of a Noisy Source DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
gradient dynamics	The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks
gradient oscillation	Exploring Gradient Oscillation in Deep Neural Network Training
Graph clustering	Graph Matching in Correlated Stochastic Block Models for Improved Graph Clustering
graph coloring	An Achievable Low Complexity Encoding Scheme for Coloring Cyclic Graphs
graph entropy	An Achievable Low Complexity Encoding Scheme for Coloring Cyclic Graphs Characterizing optimal loss for adversarial classification versus multiple adversaries
Graph matching	Graph Matching in Correlated Stochastic Block Models for Improved Graph Clustering
graphons	Any-dimensional convex sets
Greedy Cover Algorithm	An Improved Greedy Cover Algorithm for Pliable Index Coding
grid topology optimization	Topology-aware Piecewise Linearization of the AC Power Flow through Generative Modeling
Grid-aware optimization	Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities
Grid-forming control	Towards constrained grid-forming control
Group Fairness	Group Fairness with Uncertainty in Sensitive Attributes
H
Herding	Bayesian Observational Learning in Mean-Field Games with Imperfect Observations
heterogenous systems	Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games
high-dimensional dynamic systems	Manifold Optimization for Data Driven Reduced-Order Modeling
High-dimensional systems	Pulling Back from Infinity: Asymptotically Optimal Sampling of Infinite-Dimensional Measurements
hockey-stick divergence	The Saddle-Point Method in Differential Privacy
Holder class	Likelihood-free hypothesis testing
human-in-the-loop	Human-in-the-Loop Out-of-Distribution Detection with False Positive Rate Control
Hypothesis testing	Optimal Error Exponent of Type-Based Distributed Hypothesis Testing over AWGN Channels
Hyvärinen score	Score-Based Bayesian Quickest Change Detection
I
ill-conditioning	The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
incentive design	Communication Enabled Collaborations in Multi-Agent Systems
incentive mechanism	Incentive Mechanism Design for Federated Learning and Unlearning
Independence	Group Fairness with Uncertainty in Sensitive Attributes
Information Acquisition	Stochastic Game with Interactive Information Acquisition: A Fixed-Point Alignment Principle
Information cascades	Bayesian Observational Learning in Mean-Field Games with Imperfect Observations
information decomposition	Sequential Dependence Decomposition and Feature Learning
Information design	Information Design for Spatial Resource Allocation
information geometry	Feature Geometry of Multivariate Dependence and Applications
Information Theory	Fundamental Limits of Strategic Coordination
Instantaneous Coding	An Improved Greedy Cover Algorithm for Pliable Index Coding
Integral Quadratic Constraints	Analysis of Neural Network Controllers Using Quadratic Constraints
Interacting Particle Systems	Clustering in transformers: an interacting particle systems perspective
interactive learning	A Unified Analysis of Dynamic Interactive Learning
Interconnected Systems	Interaction-aware interval reachability of neural network controlled systems
intermediate layers	A Law of Data Separation in Deep Learning
Interpolation	Why interpolating neural nets generalize well: recent insights from neural tangent model
inverse problems	Generative Plug and Play: Posterior Sampling for Inverse Problems
Inverse Reinforcement Learning	Inverse Reinforcement Learning with Learning and Leveraging Demonstrators' Varying Expertise Levels
IoT	Data-Driven Sequential Change Detection in Privacy-Sensitive Networks
Isabelle	Verifying Chemical Reaction Networks with the Isabelle Theorem Prover
iteratively reweighted least squares	Weights Matter: Linear & Superlinear Convergence Rates for IRLS in Low-Rank Optimization
J
Joint decoding	The Bee Identification Problem and DNA-Based Data Storage
Joint inference and reconstruction	Single-Shot Lossy Compression for Joint Inference and Reconstruction
joint source channel coding	Coded Kalman Filtering Over Gaussian Channels with Feedback
K
k-means clustering	Privacy-Preserving Distributed k-means Clustering with Quantized Communication in Finite Time
k-mer based algorithms	On $k$-Mer-Based and Maximum Likelihood Estimation Algorithms for Trace Reconstruction
Kalman filter	Coded Kalman Filtering Over Gaussian Channels with Feedback
Kalman filtering	Pulling Back from Infinity: Asymptotically Optimal Sampling of Infinite-Dimensional Measurements
Koopman linearization	Super-linearization of Polynomial Systems
L
Label noise	Understanding Self Distillation
large alphabet	Likelihood-free hypothesis testing
large language models	Fine-Tuning Language Models with Advantage-Induced Policy Alignment
LASSO estimator	A Unified View of Decentralized Algorithms for Sparse Linear Regression
law of iterated logarithm	Human-in-the-Loop Out-of-Distribution Detection with False Positive Rate Control
learning	Learning in Network Games with Dynamic Populations
Learning and adaptive systems	Inverse-Free Zap Stochastic Approximation
learning theory	A Unified Analysis of Dynamic Interactive Learning
likelihood-free inference	Likelihood-free hypothesis testing
Linear Coding Placement	Coded Caching with Linear Coded Placement: Exact Tradeoff for the Three User Case
linear convergence	A Unified View of Decentralized Algorithms for Sparse Linear Regression
Linear MDP	Best Policy Identification in Linear MDPs
Linear Optimization	Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices
Linear Quadratic Gaussian (LQG)	Stochastic Control under Correlated Disturbances
Linear Quadratic Gaussian systems	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning
Linear Quadratic sub-Gaussian systems	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning
Linear Systems	Block-missing data in linear systems: An unbiased stochastic gradient descent approach
linear time-varying systems	Linear Operator Differential Equations with Applications to Linear Time-Varying Systems
Linearly constrained optimization	Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants
Lipschitz Constant Estimation	Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
load balancing	Dynamic Load Balancing of Selfish Drivers between Spatially Distributed Electrical Vehicle Charging Stations
local minimax optimality	Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
Logarithmic loss	Single-Shot Lossy Compression for Joint Inference and Reconstruction
logistic loss	Benign overfitting, calibration, and the benefits of early stopping
Low Rank Matrix Completion	AltGDMin for Fast Federated Low Rank Matrix Completion
Low rank plus Sparse	A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing
low-dimensional models	The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks
Low-Rank	Byzantine-resilient Federated Low Rank Column-wise Compressive Sensing
Low-rank matrix recovery	Blessing of Nonconvexity in Factorized Models
low-rank matrix sensing	The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
low-rank optimization	Weights Matter: Linear & Superlinear Convergence Rates for IRLS in Low-Rank Optimization
Lowest eigenvalue	United We Fall: On the Nash Equilibria of Multiplex Network Games
Lyapunov	Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
Lyapunov methods	Towards Rigorous Use of Neural Networks in Nonlinear Control
Lyapunov Stability Analysis	Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices
M
M-estimation	On counterfactual inference with unobserved confounding
M/M/1 Queues	Delay Performance Optimization with Packet Drop
machine learning	Natural Actor-Critic for Robust Reinforcement Learning DOLL: Distributed OnLine Learning Using Preemptible Instances Learning Source Coding for General Alphabets and Finite State Machines A Density Evolution Framework for Recovery of Covariance and Causal Graphs from Compressed Measurements
machine learning for optimization	A Curriculum Learning Approach to Optimization with Application to Downlink Beamforming
MapReduce	Straggler Exploitation in Distributed Computing Systems with Task Grouping
Markov chain	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Markov Decision Processes	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning
Markov model	A Unified Analysis of Dynamic Interactive Learning
Markov process	Sequential Dependence Decomposition and Feature Learning
Mask optimization	Theoretical Analysis of Binary Masks in Snapshot Compressive Imaging Systems
matrix games	Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
Max-affine regression	Fast max-affine regression via stochastic gradient descent
maximum likelihood	On $k$-Mer-Based and Maximum Likelihood Estimation Algorithms for Trace Reconstruction
maximum likelihood estimate	Online Parameter Estimation in Partially Observed Markov Decision Processes
Maximum Likelihood Estimation	On counterfactual inference with unobserved confounding
MDPs	Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
MDS Codes	Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Mean-field games	Bayesian Observational Learning in Mean-Field Games with Imperfect Observations
mesh networks	A Unified View of Decentralized Algorithms for Sparse Linear Regression
method of steepest descent	The Saddle-Point Method in Differential Privacy
minimax	Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
minimax testing	The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives
Minimization	A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing
Missing Data	Block-missing data in linear systems: An unbiased stochastic gradient descent approach
Mixed-expert demonstrations	Inverse Reinforcement Learning with Learning and Leveraging Demonstrators' Varying Expertise Levels
Molecular Programming	Verifying Chemical Reaction Networks with the Isabelle Theorem Prover
Monotone partitional information mechanisms	Information Design for Spatial Resource Allocation
motion planning	A Differential Dynamic Programming-based Approach for Balancing Energy and Time Optimality in Motion Planning
multi-agent learning	Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games
Multi-agent reinforcement learning (MARL)	Distributed MARL for Scheduling in Conflict Graphs
multi-agent systems	Fundamental Limits of Strategic Coordination Communication Enabled Collaborations in Multi-Agent Systems
Multi-Armed Bandits	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning Curse of Dimension of Thompson Sampling for Combinatorial Bandits Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems Quantifying the Cost of Learning in Queueing Systems
multi-armed bandits (MAB)	Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
Multi-edge type low-density parity-check codes	Trapping and Absorbing Set Enumerators for Multi-Edge Type LDPC Code Ensembles
Multi-resource allocation	Communication-efficient Federated Multi-resource Allocation: A Non-homogeneous Markov Chain Model
multi-task learning	Feature Geometry of Multivariate Dependence and Applications
multi-terminal information theory	Feature Geometry of Multivariate Dependence and Applications
multidimensional opinion dynamics	A Multidimensional Opinion Evolution Model with Confirmation Bias
Multiplex network	United We Fall: On the Nash Equilibria of Multiplex Network Games
Multiset	New results on Erasure Combinatorial Batch Codes
multishot capacity	Multishot Adversarial Network Decoding
N
Nash equilibrium	United We Fall: On the Nash Equilibria of Multiplex Network Games
nesting technique	Sequential Dependence Decomposition and Feature Learning
Net energy metering	Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities
network decoding	Multishot Adversarial Network Decoding
network games	Learning in Network Games with Dynamic Populations
Neural Feedback Loops	Backward Reachability Analysis for Safety Verification of Neural Feedback Loops
Neural Network	Analysis of Neural Network Controllers Using Quadratic Constraints Interaction-aware interval reachability of neural network controlled systems Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Neural network theory	Understanding Self Distillation
neural networks	Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks Benign overfitting, calibration, and the benefits of early stopping
Neural tangent kernel	Why interpolating neural nets generalize well: recent insights from neural tangent model
noisy communication	Distributed Optimization with Imperfect Information Sharing
Non-adaptive Group testing	New results on Erasure Combinatorial Batch Codes
Non-binary Codes	Achieving Capacity on Non-Binary Channels with Generalized Reed–Muller Codes
non-catalytic	One-Shot Non-Catalytic Distributed Purity Distillation
non-convex optimization	Fast max-affine regression via stochastic gradient descent
Non-homogeneous Markov chain	Communication-efficient Federated Multi-resource Allocation: A Non-homogeneous Markov Chain Model
Non-linear analog coding	Non-Linear Analog Coding for Image Transmission over Noisy Channels
non-parametric	Non-parametric distribution-free CUSUM for online change-point detection The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives
non-parametric regression	A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
nonconvex optimization	Manifold Optimization for Data Driven Reduced-Order Modeling Exploring Gradient Oscillation in Deep Neural Network Training Blessing of Nonconvexity in Factorized Models
nonlinear AC power flow	Topology-aware Piecewise Linearization of the AC Power Flow through Generative Modeling
nonlinear control	Towards Rigorous Use of Neural Networks in Nonlinear Control
Nonlinear Dynamics	Participation Factor Analysis of Nonlinear Systems: Beyond Equilibria
Nonlinear systems	Backward and Inner Approximation of Output Reachable sets via Chen-Fliess Series Super-linearization of Polynomial Systems
O
one-shot	One-Shot Non-Catalytic Distributed Purity Distillation
online allocation	Priorities and Quotas in Online Matching
Online learning	Quantifying the Cost of Learning in Queueing Systems
Operating envelopes	Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities
Optimal control	Inverse-Free Zap Stochastic Approximation A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning A Differential Dynamic Programming-based Approach for Balancing Energy and Time Optimality in Motion Planning
optimal power flow
Optimal Tradeoff	Coded Caching with Linear Coded Placement: Exact Tradeoff for the Three User Case
optimality theory	Globally-optimal Greedy Experiment Selection For Active Sequential Estimation
optimization	Fundamental Limits of Strategic Coordination Understanding Self Distillation Relative Entropy Regularization for Robust Submodular Multi-Task Subset Selection A Curriculum Learning Approach to Optimization with Application to Downlink Beamforming Adaptive optimization for stochastic renewal systems
Optimization and control	Communication-efficient Federated Multi-resource Allocation: A Non-homogeneous Markov Chain Model Near Optimal Differentially Private Client Selection in Federated Settings
optimization geometry	Exploring Gradient Oscillation in Deep Neural Network Training
Out-of-distribution detection	Human-in-the-Loop Out-of-Distribution Detection with False Positive Rate Control
overparameterization	Theory on Forgetting and Generalization of Continual Learning The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Overparametrized model	Why interpolating neural nets generalize well: recent insights from neural tangent model
P
P-matrix	United We Fall: On the Nash Equilibria of Multiplex Network Games
PAC	Learning Source Coding for General Alphabets and Finite State Machines
Packet Deadline	Delay Performance Optimization with Packet Drop
Packet Drop	Delay Performance Optimization with Packet Drop
Parameter Free	DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
Pareto efficiency	Priorities and Quotas in Online Matching
partial observability	Wasserstein Distributionally Robust Regret-Optimal Control under Partial Observability
Partially Observable Markov Decision Processes	Reinforcement Learning for Stochastic Control under Weak Feller Continuous Models and General Information Structures
Partially observed Markov decision processes	Online Parameter Estimation in Partially Observed Markov Decision Processes
Participation Factors	Participation Factor Analysis of Nonlinear Systems: Beyond Equilibria
pattern recognition	The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives
performative prediction	Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
Personalization	Fedsoft: Soft clustered federated learning with proximal local updating
phase transitions	"All-Something-Nothing" Phase Transitions in Planted Subgraph Recovery Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants
physical layer security	Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
Piecewise affine Systems	Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices
piecewise linear approximation	Topology-aware Piecewise Linearization of the AC Power Flow through Generative Modeling
planar wind farm model	Manifold Optimization for Data Driven Reduced-Order Modeling
planted subgraph recovery	"All-Something-Nothing" Phase Transitions in Planted Subgraph Recovery
Pliable Index Coding	An Improved Greedy Cover Algorithm for Pliable Index Coding
Plug-and-Play (PnP)	Generative Plug and Play: Posterior Sampling for Inverse Problems
point density function	Pulling Back from Infinity: Asymptotically Optimal Sampling of Infinite-Dimensional Measurements
Population dynamics	Non-myopic agents can stabilize cooperation in feedback-evolving games
posterior sampling	Generative Plug and Play: Posterior Sampling for Inverse Problems
Potential Games	Characterization of Potential Games: Application in Aggregative Games
Power flow	Aggregating Power Networks for Decentralized Control: A Spectral Clustering Approach
Power system dynamics	Towards constrained grid-forming control
power systems	Quantifying Confidence in Electricity Rate Plan Recommendations Considering Variability in Future Electricity Usage Participation Factor Analysis of Nonlinear Systems: Beyond Equilibria
preconditioned gradient descent method	The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Preconditioned Gradient Optimization	Explorations on scalable preconditioned gradient methods for deep learning
Preemptible instances	DOLL: Distributed OnLine Learning Using Preemptible Instances
preferential attachment	Phase Transitions for Diversity in Stochastic Block Model Dynamics
priorities	Priorities and Quotas in Online Matching
Privacy	Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations AltGDMin for Fast Federated Low Rank Matrix Completion Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths
privacy amplification	Privacy Amplification Against Quantum Side Information Via Regular Random Binning
privacy preservation	Privacy-Preserving Distributed k-means Clustering with Quantized Communication in Finite Time
probability guarantees	High Probability Guarantees For Federated Learning.
projection complexity	Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants
proximal policy optimization	Fine-Tuning Language Models with Advantage-Induced Policy Alignment
Proximal updates	Fedsoft: Soft clustered federated learning with proximal local updating
Public Channel	Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths
public goods	Priorities and Quotas in Online Matching
purity-distillation	One-Shot Non-Catalytic Distributed Purity Distillation
Push-sum algorithm	Push-sum, Kullback-Leibler Barycenters, and Colored Flows
Q
Q-learning	Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games
Quadratic equation	Rotation-Invariant Correlation Features for 2D Sparse Unknown View Tomography
quantization	Strategic Quantization of a Noisy Source
Quantum Cryptography	Quantum Key Distribution with State Replacement
Quantum Key Distribution	Quantum Key Distribution with State Replacement
quantum side information	Privacy Amplification Against Quantum Side Information Via Regular Random Binning
Queueing Systems	Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems Quantifying the Cost of Learning in Queueing Systems
Quickest change detection	Sequential Change-Point Detection for Correlation Matrices
quickest detection	An Adaptive Approach for Online Monitoring of Large Scale Data Streams
quotas	Priorities and Quotas in Online Matching
R
random binning	Privacy Amplification Against Quantum Side Information Via Regular Random Binning
random graphs	"All-Something-Nothing" Phase Transitions in Planted Subgraph Recovery
random initialization	The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Random processes	Stochastic localization and algorithmic variants
Randomized Kaczmarz method	On Subsampled Quantile Randomized Kaczmarz
randomized smoothing	Stability Guarantees for Feature Attributions with Multiplicative Smoothing
ranking	Active Ranking without Strong Stochastic Transitivity
Reachability	Backward and Inner Approximation of Output Reachable sets via Chen-Fliess Series
Reachability Analysis	Interaction-aware interval reachability of neural network controlled systems
Recommender Systems	Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle
recurrent neural networks	Distributed MARL for Scheduling in Conflict Graphs
reduced-order modeling	Manifold Optimization for Data Driven Reduced-Order Modeling
Reed-Muller codes	Achieving Capacity on Non-Binary Channels with Generalized Reed–Muller Codes
Regret	The Reward Biased Method: An Optimism based Approach for Reinforcement Learning Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
regret-optimal control	Wasserstein Distributionally Robust Regret-Optimal Control under Partial Observability
Regularization	Learning Fair Classifiers via Min-Max F-divergence Regularization
regularizers	Any-dimensional convex sets
Reinforcement Learning	Reinforcement Learning for Stochastic Control under Weak Feller Continuous Models and General Information Structures The Reward Biased Method: An Optimism based Approach for Reinforcement Learning Curse of Dimension of Thompson Sampling for Combinatorial Bandits Natural Actor-Critic for Robust Reinforcement Learning Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
reinforcement learning with human feedback	Fine-Tuning Language Models with Advantage-Induced Policy Alignment
reliability	High Probability Guarantees For Federated Learning.
ReLU Neural Networks	Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices
remote source coding	Strategic Quantization of a Noisy Source
representation learning	On representation learning with the InfoNCE loss
representation theory	Any-dimensional convex sets
Representative-Based Decoding	An Improved Greedy Cover Algorithm for Pliable Index Coding
reproducibility	Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
Restless bandits	Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Riemannian manifold optimization	Manifold Optimization for Data Driven Reduced-Order Modeling
Robot Safety	Discovering and Learning from Closed-Loop Failures of Vision-based Controllers.
robotics	A Differential Dynamic Programming-based Approach for Balancing Energy and Time Optimality in Motion Planning Practical Algorithms for Globally Optimal Geometric Machine Perception
Robust Bellman equation	Robust Average-Reward Markov Decision Processes
Robust Blackwell optimality	Robust Average-Reward Markov Decision Processes
robust learning	Natural Actor-Critic for Robust Reinforcement Learning Fine-Tuning Language Models with Advantage-Induced Policy Alignment
Robust Learning with Poisoned Data/Model	A variational approach to federated and robust learning
Robust Machine Learning	Byzantine-resilient Federated Low Rank Column-wise Compressive Sensing
Robust RL	Robust Average-Reward Markov Decision Processes
Robustness	Relative Entropy Regularization for Robust Submodular Multi-Task Subset Selection Robust Transfer Learning Based on Minimax Principle Analysis of Neural Network Controllers Using Quadratic Constraints Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Rotation-invariant features	Rotation-Invariant Correlation Features for 2D Sparse Unknown View Tomography
Runtime verification	Conformal Prediction for STL Runtime Verification
S
saddle-point method	The Saddle-Point Method in Differential Privacy
safety	Quantifying Confidence in Electricity Rate Plan Recommendations Considering Variability in Future Electricity Usage Towards Rigorous Use of Neural Networks in Nonlinear Control Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Safety Certification	Backward Reachability Analysis for Safety Verification of Neural Feedback Loops
sample complexity	Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains Active Ranking without Strong Stochastic Transitivity
Sampling from high-dimensional distributions	Stochastic localization and algorithmic variants
scalability	High Probability Guarantees For Federated Learning.
Scheme	Coded Caching with Linear Coded Placement: Exact Tradeoff for the Three User Case
score matching	Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains
score-based models	Score-Based Bayesian Quickest Change Detection
secrecy capacity	Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
secure codes	Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
Security	Coding for forensic 3D fingerprinting
self-interested player	The Effect of Altruism in the Electricity Network
selfish routing	Dynamic Load Balancing of Selfish Drivers between Spatially Distributed Electrical Vehicle Charging Stations
Semidefinite programming	Practical Algorithms for Globally Optimal Geometric Machine Perception
sensor attacks	Vulnerability Analysis of Perception-Based Control: Modeling and Fundamental Limits
sensor systems	Fairness in Distributed Sensor Systems to Detect Emitters
Separation	Group Fairness with Uncertainty in Sensitive Attributes
Sequential analysis	Non-parametric distribution-free CUSUM for online change-point detection
sequential dependence	Sequential Dependence Decomposition and Feature Learning
sequential estimation	Globally-optimal Greedy Experiment Selection For Active Sequential Estimation
sequential methods	Globally-optimal Greedy Experiment Selection For Active Sequential Estimation
sgd	Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
Shannon capacity	Coded Kalman Filtering Over Gaussian Channels with Feedback
Shared Key Lengths	Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths
Signal temporal logic	Conformal Prediction for STL Runtime Verification
Similarity digraph	Aggregating Power Networks for Decentralized Control: A Spectral Clustering Approach
Similarity measurement	Robust Transfer Learning Based on Minimax Principle
simulated tempering	Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains
Single-shot compression	Single-Shot Lossy Compression for Joint Inference and Reconstruction
Snapshot compressive imaging	Theoretical Analysis of Binary Masks in Snapshot Compressive Imaging Systems
social networks	A Multidimensional Opinion Evolution Model with Confirmation Bias
social planner	The Effect of Altruism in the Electricity Network
Source coding	Learning Source Coding for General Alphabets and Finite State Machines Single-Shot Lossy Compression for Joint Inference and Reconstruction
Sparsity	Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Spatial resource allocation	Information Design for Spatial Resource Allocation
Spectral clustering	Aggregating Power Networks for Decentralized Control: A Spectral Clustering Approach
Stability	Analysis of Neural Network Controllers Using Quadratic Constraints
statistical efficiency	A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
statistical error	A Unified View of Decentralized Algorithms for Sparse Linear Regression
Statistical Fairness	Learning Fair Classifiers via Min-Max F-divergence Regularization
statistical signal processing	A Density Evolution Framework for Recovery of Covariance and Causal Graphs from Compressed Measurements
statistical theory	Globally-optimal Greedy Experiment Selection For Active Sequential Estimation
Stein's unbiased risk estimator	Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach
Stochastic Approximation	Inverse-Free Zap Stochastic Approximation Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
stochastic block model	Phase Transitions for Diversity in Stochastic Block Model Dynamics
stochastic control	Quantifying the Cost of Learning in Queueing Systems Stochastic Control under Correlated Disturbances Adaptive optimization for stochastic renewal systems
Stochastic Extragradient	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
stochastic games	Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games Stochastic Game with Interactive Information Acquisition: A Fixed-Point Alignment Principle
Stochastic Gradient Descent	Fast max-affine regression via stochastic gradient descent Block-missing data in linear systems: An unbiased stochastic gradient descent approach
Stochastic Gradient Descent Ascent	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
stochastic iterative methods	On Subsampled Quantile Randomized Kaczmarz
Stochastic localization	Stochastic localization and algorithmic variants
stochastic optimization	Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
stochastic processes	Adaptive optimization for stochastic renewal systems
Stochastic Systems	Fundamental Limits of Strategic Coordination
stragglers	Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations Straggler Exploitation in Distributed Computing Systems with Task Grouping
Strong Coordination	Multi-terminal Strong Coordination over Noiseless Networks with Secrecy Constraints
Strong Secrecy	Multi-terminal Strong Coordination over Noiseless Networks with Secrecy Constraints
strong stochastic transitivity	Active Ranking without Strong Stochastic Transitivity
Sub-gradient method	Blessing of Nonconvexity in Factorized Models
submodularity	Relative Entropy Regularization for Robust Submodular Multi-Task Subset Selection
Subspace Channelization	Subspace Methods for Blind Fractionally-Spaced Equalization of Distributed Phase-Shift Beamforming Signals
Super-linearization	Super-linearization of Polynomial Systems
Symbol detection	Feature Geometry of Multivariate Dependence and Applications
Synthetic Controls	Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle
T
T equivalent circuit	Three-phase Transformer Modeling
Tail Bounds	Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
testing multinomials	The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives
three-phase transformer	Three-phase Transformer Modeling
trace reconstruction	On $k$-Mer-Based and Maximum Likelihood Estimation Algorithms for Trace Reconstruction
Trade-off	Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths
Transfer learning	Robust Transfer Learning Based on Minimax Principle
Transformers	Clustering in transformers: an interacting particle systems perspective
Transportation networks	Dynamic Tolling in Arc-based Traffic Assignment Models
Trapping sets	Trapping and Absorbing Set Enumerators for Multi-Edge Type LDPC Code Ensembles
Treating interference as noise	On Energy-Delay Tradeoff in Uncoordinated MAC
trustworthiness	High Probability Guarantees For Federated Learning.
two-dimensional grid	Age of Gossip on a Grid
two-sample testing	Likelihood-free hypothesis testing
Two-time-scale stochastic optimization	A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
U
UCB	Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
unbalanced network	Three-phase Transformer Modeling
Uncertain Sensitive Attributes	Group Fairness with Uncertainty in Sensitive Attributes
unconstrained codes	Embracing errors is more effective than avoiding them through constrained coding for DNA data storage
Uncoordinated MAC	On Energy-Delay Tradeoff in Uncoordinated MAC
uniformity testing	The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives
Uniqueness	United We Fall: On the Nash Equilibria of Multiplex Network Games
unitary voltage system	Three-phase Transformer Modeling
unnormalized statistical models	Score-Based Bayesian Quickest Change Detection
Unobserved Confounding	On counterfactual inference with unobserved confounding
unsupervised learning	On representation learning with the InfoNCE loss
V
Variational Federated Learning	A variational approach to federated and robust learning
variational inequalities problems	Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
variational inference	Asymptotics for alpha-posteriors with applications to variational inference
verification	Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
W
Wasserstein distance	Training generative models from privatized data Wasserstein Distributionally Robust Regret-Optimal Control under Partial Observability
Weak Feller Continuity	Reinforcement Learning for Stochastic Control under Weak Feller Continuous Models and General Information Structures
wireless networks	Distributed MARL for Scheduling in Conflict Graphs
wiretap channel	Privacy Amplification Against Quantum Side Information Via Regular Random Binning Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
Z
zero-sum	Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
zero-sum games	Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games

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