arXiv subjects

Reza Ghane

Publications and source records attributed to Reza Ghane.

13 recordsLinked to original sources

arXiv2026

Precise Performance of Linear Denoisers in the Proportional Regime

In the present paper we study the performance of linear denoisers for noisy data of the form $\mathbf{x} + \mathbf{z}$, where $\mathbf{x} \in \mathbb{R}^d$ is the desired data with zero mean and unknown covariance $\mathbf{\Sigma}$, and $\mathbf{z} \sim \mathcal{N}(0, \mathbf{\Sigma}_{\mathbf{z}})$ is additive noise. Since the covariance $\mathbf{\Sigma}$ is not known, the standard Wiener filter cannot be employed for denoising. Instead we assume we are given samples $\mathbf{x}_1,\dots,\mathbf{x}_n \in \mathbb{R}^d$ from the true distribution. A standard approach would then be to estimate $\mathbf{\Sigma}$ from the samples and use it to construct an ``empirical" Wiener filter. However, in this paper, motivated by the denoising step in diffusion models, we take a different approach whereby we train a linear denoiser $\mathbf{W}$ from the data itself. In particular, we synthetically construct noisy samples $\hat{\mathbf{x}}_i$ of the data by injecting the samples with Gaussian noise with covariance $\mathbf{\Sigma}_1 \neq \mathbf{\Sigma}_{\mathbf{z}}$ and find the best $\mathbf{W}$ that approximates $\mathbf{W}\hat{\mathbf{x}}_i \approx \mathbf{x}_i$ in a least-squares sense. In the proportional regime $\frac{n}{d} \rightarrow \kappa > 1$ we use the {\it Convex Gaussian Min-Max Theorem (CGMT)} to analytically find the closed form expression for the generalization error of the denoiser obtained from this process. Using this expression one can optimize over $\mathbf{\Sigma}_1$ to find the best possible denoiser. Our numerical simulations show that our denoiser outperforms the ``empirical" Wiener filter in many scenarios and approaches the optimal Wiener filter as $\kappa\rightarrow\infty$.

stat.ML↗

arXiv2026

Dual Space Preconditioning for Gradient Descent in the Overparameterized Regime

In this work, we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalized Gradient Descent and Gradient Clipping. We consider preconditioners of the form $\nabla K$, where $K: \mathbb{R}^{d \times k} \to \mathbb{R}$ is convex and apply $\nabla K(\cdot)$ to train an over-parameterized linear model with a convex loss of the form $\ell(X W - Y)$, for weights $W \in \mathbb{R}^{d \times k}$, labels $Y \in \mathbb{R}^{n \times k}$ and data $X \in \mathbb{R}^{n \times d}$. Under the aforementioned assumptions, we prove that the iterates of the full-batch preconditioned gradient descent converge at an exponential rate to a point $W_{\infty} \in \mathbb{R}^{d \times k}$ satisfying $XW_{\infty} = Y$. We also study the implicit bias of Dual Space Preconditioned Gradient Descent. First, we demonstrate analytically and empirically that, for general $K(\cdot)$, $W_\infty$ depends on the chosen constant step size, hindering a precise characterization of the implicit bias. We also provide an approximate implicit bias property for general preconditioners, namely, $\|W_0 - W_{\infty}\|_F \le c \|W_0 - W_{\text{GD}, \infty}\|_F$ for a constant $c>0$ and $W_{\text{GD}, \infty}$ denoting the convergence point of GD initialized at $W_0$. Furthermore, for preconditioners of the form $K(G) = h(\|G\|_F)$, known as {\it isotropic preconditioners}, and for the stochastic variation of the algorithm with arbitrary batch-size, we prove linear convergence to $W_{\text{GD}, \infty}$. Finally, in the experiments, we demonstrate faster convergence on a nonlinear model obtained using the smoothed matrix elastic-net as a preconditioner.

stat.ML↗

arXiv2026

Implicit Bias and Convergence of Matrix Stochastic Mirror Descent

We investigate Stochastic Mirror Descent (SMD) with matrix parameters and vector-valued predictions, a framework relevant to multi-class classification and matrix completion problems. Focusing on the overparameterized regime, where the total number of parameters exceeds the number of training samples, we prove that SMD with matrix mirror functions $\psi(\cdot)$ converges exponentially to a global interpolator. Furthermore, we generalize classical implicit bias results of vector SMD by demonstrating that the matrix SMD algorithm converges to the unique solution minimizing the Bregman divergence induced by $\psi(\cdot)$ from initialization subject to interpolating the data. These findings reveal how matrix mirror maps dictate inductive bias in high-dimensional, multi-output problems.

stat.ML↗

arXiv2025

One-Bit Quantization for Random Features Models

Recent advances in neural networks have led to significant computational and memory demands, spurring interest in one-bit weight compression to enable efficient inference on resource-constrained devices. However, the theoretical underpinnings of such compression remain poorly understood. We address this gap by analyzing one-bit quantization in the Random Features model, a simplified framework that corresponds to neural networks with random representations. We prove that, asymptotically, quantizing weights of all layers except the last incurs no loss in generalization error, compared to the full precision random features model. Our findings offer theoretical insights into neural network compression. We also demonstrate empirically that one-bit quantization leads to significant inference speed ups for the Random Features models even on a laptop GPU, confirming the practical benefits of our work. Additionally, we provide an asymptotically precise characterization of the generalization error for Random Features with an arbitrary number of layers. To the best of our knowledge, our analysis yields more general results than all previous works in the related literature.

cs.LG↗

arXiv2025

A Precise Performance Analysis of the Randomized Singular Value Decomposition

The Randomized Singular Value Decomposition (RSVD) is a widely used algorithm for efficiently computing low-rank approximations of large matrices, without the need to construct a full-blown SVD. Of interest, of course, is the approximation error of RSVD compared to the optimal low-rank approximation error obtained from the SVD. While the literature provides various upper and lower error bounds for RSVD, in this paper we derive precise asymptotic expressions that characterize its approximation error as the matrix dimensions grow to infinity. Our expressions depend only on the singular values of the matrix, and we evaluate them for two important matrix ensembles: those with power law and bilevel singular value distributions. Our results aim to quantify the gap between the existing theoretical bounds and the actual performance of RSVD. Furthermore, we extend our analysis to polynomial-filtered RSVD, deriving performance characterizations that provide insights into optimal filter selection.

math.NA↗

arXiv2025

Beyond Quadratic Costs in LQR: Bregman Divergence Control

In the past couple of decades, the use of ``non-quadratic" convex cost functions has revolutionized signal processing, machine learning, and statistics, allowing one to customize solutions to have desired structures and properties. However, the situation is not the same in control where the use of quadratic costs still dominates, ostensibly because determining the ``value function", i.e., the optimal expected cost-to-go, which is critical to the construction of the optimal controller, becomes computationally intractable as soon as one considers general convex costs. As a result, practitioners often resort to heuristics and approximations, such as model predictive control that only looks a few steps into the future. In the quadratic case, the value function is easily determined by solving Riccati equations. In this work, we consider a special class of convex cost functions constructed from Bregman divergence and show how, with appropriate choices, they can be used to fully extend the framework developed for the quadratic case. The resulting optimal controllers are infinite horizon, come with stability guarantees, and have state-feedback, or estimated state-feedback, laws. They exhibit a much wider range of behavior than their quadratic counterparts since the feedback laws are nonlinear. The approach can be applied to several cases of interest, including safety control, sparse control, and bang-bang control.

eess.SY↗

arXiv2025

Beyond Quadratic Costs: A Bregman Divergence Approach to H$_\infty$ Control

In the past couple of decades, non-quadratic convex penalties have reshaped signal processing and machine learning; in robust control, however, general convex costs break the Riccati and storage function structure that make the design tractable. Practitioners thus default to approximations, heuristics or robust model predictive control that are solved online for short horizons. We close this gap by extending $H_\infty$ control of discrete-time linear systems to strictly convex penalties on state, input, and disturbance, recasting the objective with Bregman divergences that admit a completion-of-squares decomposition. The result is a closed-form, time-invariant, full-information stabilizing controller that minimizes a worst-case performance ratio over the infinite horizon. Necessary and sufficient existence/optimality conditions are given by a Riccati-like identity together with a concavity requirement; with quadratic costs, these collapse to the classical $H_\infty$ algebraic Riccati equation and the associated negative-semidefinite condition, recovering the linear central controller. Otherwise, the optimal controller is nonlinear and can enable safety envelopes, sparse actuation, and bang-bang policies with rigorous $H_\infty$ guarantees.

eess.SY↗

arXiv2025

Robust Mean Estimation With Auxiliary Samples

In data-driven learning and inference tasks, the high cost of acquiring samples from the target distribution often limits performance. A common strategy to mitigate this challenge is to augment the limited target samples with data from a more accessible "auxiliary" distribution. This paper establishes fundamental limits of this approach by analyzing the improvement in the mean square error (MSE) when estimating the mean of the target distribution. Using the Wasserstein-2 metric to quantify the distance between distributions, we derive expressions for the worst-case MSE when samples are drawn (with labels) from both a target distribution and an auxiliary distribution within a specified Wasserstein-2 distance from the target distribution. We explicitly characterize the achievable MSE and the optimal estimator in terms of the problem dimension, the number of samples from the target and auxiliary distributions, the Wasserstein-2 distance, and the covariance of the target distribution. We note that utilizing samples from the auxiliary distribution effectively improves the MSE when the squared radius of the Wasserstein-2 uncertainty ball is small compared to the variance of the true distribution and the number of samples from the true distribution is limited. Numerical simulations in the Gaussian location model illustrate the theoretical findings.

math.ST↗

arXiv2025

Gaussian Universality for Diffusion Models

We investigate Gaussian Universality for data distributions generated via diffusion models. By Gaussian Universality we mean that the test error of a generalized linear model $f(\mathbf{W})$ trained for a classification task on the diffusion data matches the test error of $f(\mathbf{W})$ trained on the Gaussian Mixture with matching means and covariances per class.In other words, the test error depends only on the first and second order statistics of the diffusion-generated data in the linear setting. As a corollary, the analysis of the test error for linear classifiers can be reduced to Gaussian data from diffusion-generated data. Analysing the performance of models trained on synthetic data is a pertinent problem due to the surge of methods such as \cite{sehwag2024stretchingdollardiffusiontraining}. Moreover, we show that, for any $1$- Lipschitz scalar function $\phi$, $\phi(\mathbf{x})$ is close to $\mathbb{E} \phi(\mathbf{x})$ with high probability for $\mathbf{x}$ sampled from the conditional diffusion model corresponding to each class. Finally, we note that current approaches for proving universality do not apply to diffusion-generated data as the covariance matrices of the data tend to have vanishing minimum singular values, contrary to the assumption made in the literature. This leaves extending previous mathematical universality results as an intriguing open question.

stat.ML↗

arXiv2024

Universality in Transfer Learning for Linear Models

We study the problem of transfer learning and fine-tuning in linear models for both regression and binary classification. In particular, we consider the use of stochastic gradient descent (SGD) on a linear model initialized with pretrained weights and using a small training data set from the target distribution. In the asymptotic regime of large models, we provide an exact and rigorous analysis and relate the generalization errors (in regression) and classification errors (in binary classification) for the pretrained and fine-tuned models. In particular, we give conditions under which the fine-tuned model outperforms the pretrained one. An important aspect of our work is that all the results are "universal", in the sense that they depend only on the first and second order statistics of the target distribution. They thus extend well beyond the standard Gaussian assumptions commonly made in the literature. Furthermore, our universality results extend beyond standard SGD training to the test error of a classification task trained using a ridge regression.

cs.LG↗

arXiv2024

One-Bit Quantization and Sparsification for Multiclass Linear Classification with Strong Regularization

We study the use of linear regression for multiclass classification in the over-parametrized regime where some of the training data is mislabeled. In such scenarios it is necessary to add an explicit regularization term, $\lambda f(w)$, for some convex function $f(\cdot)$, to avoid overfitting the mislabeled data. In our analysis, we assume that the data is sampled from a Gaussian Mixture Model with equal class sizes, and that a proportion $c$ of the training labels is corrupted for each class. Under these assumptions, we prove that the best classification performance is achieved when $f(\cdot) = \|\cdot\|^2_2$ and $\lambda \to \infty$. We then proceed to analyze the classification errors for $f(\cdot) = \|\cdot\|_1$ and $f(\cdot) = \|\cdot\|_\infty$ in the large $\lambda$ regime and notice that it is often possible to find sparse and one-bit solutions, respectively, that perform almost as well as the one corresponding to $f(\cdot) = \|\cdot\|_2^2$.

cs.LG↗

arXiv2024

A Novel Gaussian Min-Max Theorem and its Applications

A celebrated result by Gordon allows one to compare the min-max behavior of two Gaussian processes if certain inequality conditions are met. The consequences of this result include the Gaussian min-max (GMT) and convex Gaussian min-max (CGMT) theorems which have had far-reaching implications in high-dimensional statistics, machine learning, non-smooth optimization, and signal processing. Both theorems rely on a pair of Gaussian processes, first identified by Slepian, that satisfy Gordon's comparison inequalities. In this paper, we identify such a new pair. The resulting theorems extend the classical GMT and CGMT Theorems from the case where the underlying Gaussian matrix in the primary process has iid rows to where it has independent but non-identically-distributed ones. The new CGMT is applied to the problems of multi-source Gaussian regression, as well as to binary classification of general Gaussian mixture models.

cs.LG↗

arXiv2023

Regularized Linear Regression for Binary Classification

Regularized linear regression is a promising approach for binary classification problems in which the training set has noisy labels since the regularization term can help to avoid interpolating the mislabeled data points. In this paper we provide a systematic study of the effects of the regularization strength on the performance of linear classifiers that are trained to solve binary classification problems by minimizing a regularized least-squares objective. We consider the over-parametrized regime and assume that the classes are generated from a Gaussian Mixture Model (GMM) where a fraction $c<\frac{1}{2}$ of the training data is mislabeled. Under these assumptions, we rigorously analyze the classification errors resulting from the application of ridge, $\ell_1$, and $\ell_\infty$ regression. In particular, we demonstrate that ridge regression invariably improves the classification error. We prove that $\ell_1$ regularization induces sparsity and observe that in many cases one can sparsify the solution by up to two orders of magnitude without any considerable loss of performance, even though the GMM has no underlying sparsity structure. For $\ell_\infty$ regularization we show that, for large enough regularization strength, the optimal weights concentrate around two values of opposite sign. We observe that in many cases the corresponding "compression" of each weight to a single bit leads to very little loss in performance. These latter observations can have significant practical ramifications.

cs.LG↗