
RegretOptimal Filtering
We consider the problem of filtering in linear statespace models (e.g.,...
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Stability and Identification of Random Asynchronous Linear TimeInvariant Systems
In many computational tasks and dynamical systems, asynchrony and random...
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Robustifying Binary Classification to Adversarial Perturbation
Despite the enormous success of machine learning models in various appli...
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The Performance Analysis of Generalized Margin Maximizer (GMM) on Separable Data
Logistic models are commonly used for binary classification tasks. The s...
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Regretoptimal control in dynamic environments
We consider the control of linear timevarying dynamical systems from th...
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Explore More and Improve Regret in Linear Quadratic Regulators
Stabilizing the unknown dynamics of a control system and minimizing regr...
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Support Constrained Generator Matrices of Gabidulin Codes in Characteristic Zero
Gabidulin codes over fields of characteristic zero were recently constru...
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Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
We study the problem of adaptive control in partially observable linear ...
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Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems
We study the problem of adaptive control in partially observable linear ...
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The Power of Linear Controllers in LQR Control
The Linear Quadratic Regulator (LQR) framework considers the problem of ...
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Achieving the fundamental convergencecommunication tradeoff with Differentially Quantized Gradient Descent
The problem of reducing the communication cost in distributed training t...
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Regret Minimization in Partially Observable Linear Quadratic Control
We study the problem of regret minimization in partially observable line...
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Universality in Learning from Linear Measurements
We study the problem of recovering a structured signal from independentl...
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Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
Most modern learning problems are highly overparameterized, meaning that...
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The Impact of Regularization on Highdimensional Logistic Regression
Logistic regression is commonly used for modeling dichotomous outcomes. ...
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A Stochastic Interpretation of Stochastic Mirror Descent: RiskSensitive Optimality
Stochastic mirror descent (SMD) is a fairly new family of algorithms tha...
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Gabidulin Codes with Support Constrained Generator Matrices
Gabidulin codes are the only known general construction of linear codes ...
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MOCZ for Blind ShortPacket Communication: Some Practical Aspects
We will investigate practical aspects for a recently introduced blind (n...
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Stochastic Linear Bandits with Hidden Low Rank Structure
Highdimensional representations often have a lower dimensional underlyi...
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Algorithms for Optimal Control with FixedRate Feedback
We consider a discretetime linear quadratic Gaussian networked control ...
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Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
Stochastic descent methods (of the gradient and mirror varieties) have b...
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Noncoherent ShortPacket Communication via Modulation on Conjugated Zeros
We introduce a novel blind (noncoherent) communication scheme, called mo...
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Optimum Linear Codes with Support Constraints over Small Fields
We consider the problem of designing optimal linear codes (in terms of h...
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Further Progress on the GMMDS Conjecture for ReedSolomon Codes
Designing good error correcting codes whose generator matrix has a suppo...
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A Precise Analysis of PhaseMax in Phase Retrieval
Recovering an unknown complex signal from the magnitude of linear combin...
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Symbol Error Rate Performance of Boxrelaxation Decoders in Massive MIMO
The maximumlikelihood (ML) decoder for symbol detection in large multip...
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Entropic Causality and Greedy Minimum Entropy Coupling
We study the problem of identifying the causal relationship between two ...
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Entropic Causal Inference
We consider the problem of identifying the causal direction between two ...
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Finding Dense Clusters via "Low Rank + Sparse" Decomposition
Finding "densely connected clusters" in a graph is in general an importa...
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New Null Space Results and Recovery Thresholds for Matrix Rank Minimization
Nuclear norm minimization (NNM) has recently gained significant attentio...
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Necessary and Sufficient Conditions for Success of the Nuclear Norm Heuristic for Rank Minimization
Minimizing the rank of a matrix subject to constraints is a challenging ...
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Babak Hassibi
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Professor and Executive Officer of Electrical Engineering at Caltech