Search Results for author:
Found 21 papers, 3 papers with code
Stochastic Resource Allocation via Dual Tail Waterfilling
On the other hand, ergodic-optimal resource allocation policies are often susceptible to the statistical dispersion of heavy-tailed fading channels, leading to relatively frequent drastic performance drops.
Robust and Reliable Stochastic Resource Allocation via Tail Waterfilling
Stochastic allocation of resources in the context of wireless systems ultimately demands reactive decision making for meaningfully optimizing network-wide random utilities, while respecting certain resource constraints.
Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design
Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements.
Risk-Aware Stability of Discrete-Time Systems
We develop a generalized stability framework for stochastic discrete-time systems, where the generality pertains to the ways in which the distribution of the state energy can be characterized.
Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization
Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations.
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD
For nonconvex smooth losses, we prove that full-batch GD efficiently generalizes close to any stationary point at termination, and recovers the generalization error guarantees of stochastic algorithms with fewer assumptions.
Black-Box Generalization: Stability of Zeroth-Order Learning
These bounds coincide with those for SGD, and rather surprisingly are independent of $d$, $K$ and the batch size $m$, under appropriate choices of a slightly decreased learning rate.
Model-Free Learning of Optimal Deterministic Resource Allocations in Wireless Systems via Action-Space Exploration
Wireless systems resource allocation refers to perpetual and challenging nonconvex constrained optimization tasks, which are especially timely in modern communications and networking setups involving multiple users with heterogeneous objectives and imprecise or even unknown models and/or channel statistics.
Uncertainty Principles in Risk-Aware Statistical Estimation
We present a new uncertainty principle for risk-aware statistical estimation, effectively quantifying the inherent trade-off between mean squared error ($\mse$) and risk, the latter measured by the associated average predictive squared error variance ($\sev$), for every admissible estimator of choice.
Noisy Linear Convergence of Stochastic Gradient Descent for CV@R Statistical Learning under Polyak-Łojasiewicz Conditions
Conditional Value-at-Risk ($\mathrm{CV@R}$) is one of the most popular measures of risk, which has been recently considered as a performance criterion in supervised statistical learning, as it is related to desirable operational features in modern applications, such as safety, fairness, distributional robustness, and prediction error stability.
Zeroth-order Deterministic Policy Gradient
Deterministic Policy Gradient (DPG) removes a level of randomness from standard randomized-action Policy Gradient (PG), and demonstrates substantial empirical success for tackling complex dynamic problems involving Markov decision processes.
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme
First, we propose a (non-private) successive elimination algorithm for strictly optimal best-arm identification, we show that our algorithm is $\delta$-PAC and we characterize its sample complexity.
Zeroth-order Stochastic Compositional Algorithms for Risk-Aware Learning
We then present a complete analysis of the $\textit{Free-MESSAGE}^{p}$ algorithm, which establishes convergence in a user-tunable neighborhood of the optimal solutions of the original problem for convex costs, as well as explicit convergence rates for convex, weakly convex, and strongly convex costs, and in a unified way.
Risk-Aware MMSE Estimation
Despite the simplicity and intuitive interpretation of Minimum Mean Squared Error (MMSE) estimators, their effectiveness in certain scenarios is questionable.
Model-Free Learning of Optimal Ergodic Policies in Wireless Systems
Upon further assuming the use of near-universal policy parameterizations, we also develop explicit bounds on the gap between optimal values of initial, infinite dimensional resource allocation problems, and dual values of their parameterized smoothed surrogates.
Optimal Rates for Learning Hidden Tree Structures
Specifically, we show that the finite sample complexity of the Chow-Liu algorithm for ensuring exact structure recovery from noisy data is inversely proportional to the information threshold squared (provided it is positive), and scales almost logarithmically relative to the number of nodes over a given probability of failure.
Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications
To meet the quality-of-service guarantees of the network, a key prerequisite for beamforming is relay selection.
Predictive Learning on Hidden Tree-Structured Ising Models
In the absence of noise, predictive learning on Ising models was recently studied by Bresler and Karzand (2020); this paper quantifies how noise in the hidden model impacts the tasks of structure recovery and marginal distribution estimation by proving upper and lower bounds on the sample complexity.
Recursive Optimization of Convex Risk Measures: Mean-Semideviation Models
2) Assuming a strongly convex cost, we show that, for fixed semideviation order $p>1$ and for $\epsilon\in\left[0, 1\right)$, the MESSAGEp algorithm achieves a squared-${\cal L}_{2}$ solution suboptimality rate of the order of ${\cal O}(n^{-\left(1-\epsilon\right)/2})$ iterations, where, for $\epsilon>0$, pathwise convergence is simultaneously guaranteed.
Grid Based Nonlinear Filtering Revisited: Recursive Estimation & Asymptotic Optimality
We revisit the development of grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise.
Uniform {\varepsilon}-Stability of Distributed Nonlinear Filtering over DNAs: Gaussian-Finite HMMs
In this work, we study stability of distributed filtering of Markov chains with finite state space, partially observed in conditionally Gaussian noise.
