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Georgios Giannakis

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8 papers

1 author row

NeurIPS Conference 2025 Conference Paper

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models

Yilang Zhang
Bingcong Li
Georgios Giannakis

Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank factorization per step that minimizes an upper bound on the loss. The resultant refactored low-rank adaptation (RefLoRA) method promotes a flatter loss landscape, along with consistent and balanced weight updates, thus speeding up stable convergence. Extensive experiments evaluate RefLoRA on natural language understanding, and commonsense reasoning tasks with popular large language models including DeBERTaV3, LLaMA-7B, LLaMA2-7B and LLaMA3-8B. The numerical tests corroborate that RefLoRA converges faster, outperforms various benchmarks, and enjoys negligible computational overhead compared to state-of-the-art LoRA variants.

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NeurIPS Conference 2023 Conference Paper

Enhancing Sharpness-Aware Optimization Through Variance Suppression

Bingcong Li
Georgios Giannakis

Sharpness-aware minimization (SAM) has well documented merits in enhancing generalization of deep neural networks, even without sizable data augmentation. Embracing the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing the maximum loss caused by an adversary perturbing parameters within the neighborhood. Although critical to account for sharpness of the loss function, such an ' over-friendly adversary' can curtail the outmost level of generalization. The novel approach of this contribution fosters stabilization of adversaries through variance suppression (VaSSO) to avoid such friendliness. VaSSO's provable stability safeguards its numerical improvement over SAM in model-agnostic tasks, including image classification and machine translation. In addition, experiments confirm that VaSSO endows SAM with robustness against high levels of label noise. Code is available at https: //github. com/BingcongLi/VaSSO.

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NeurIPS Conference 2021 Conference Paper

Heavy Ball Momentum for Conditional Gradient

Bingcong Li
Alireza Sadeghi
Georgios Giannakis

Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications. Unlike projection-based methods, momentum cannot improve the convergence rate of FW, in general. This limitation motivates the present work, which deals with heavy ball momentum, and its impact to FW. Specifically, it is established that heavy ball offers a unifying perspective on the primal-dual (PD) convergence, and enjoys a tighter \textit{per iteration} PD error rate, for multiple choices of step sizes, where PD error can serve as the stopping criterion in practice. In addition, it is asserted that restart, a scheme typically employed jointly with Nesterov's momentum, can further tighten this PD error bound. Numerical results demonstrate the usefulness of heavy ball momentum in FW iterations.

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NeurIPS Conference 2020 Conference Paper

Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis

Gang Wang
Songtao Lu
Georgios Giannakis
Gerald Tesauro
Jian Sun

The present contribution deals with decentralized policy evaluation in multi-agent Markov decision processes using temporal-difference (TD) methods with linear function approximation for scalability. The agents cooperate to estimate the value function of such a process by observing continual state transitions of a shared environment over the graph of interconnected nodes (agents), along with locally private rewards. Different from existing consensus-type TD algorithms, the approach here develops a simple decentralized TD tracker by wedding TD learning with gradient tracking techniques. The non-asymptotic properties of the novel TD tracker are established for both independent and identically distributed (i. i. d. ) as well as Markovian transitions through a unifying multistep Lyapunov analysis. In contrast to the prior art, the novel algorithm forgoes the limiting error bounds on the number of agents, which endows it with performance comparable to that of centralized TD methods that are the sharpest known to date.

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NeurIPS Conference 2019 Conference Paper

Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

Jun Sun
Tianyi Chen
Georgios Giannakis
Zaiyue Yang

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first quantize the computed gradients, and then skip less informative quantized gradient communications by reusing outdated gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized gradient that is henceforth abbreviated as LAQ. Our LAQ can provably attain the same linear convergence rate as the gradient descent in the strongly convex case, while effecting major savings in the communication overhead both in transmitted bits as well as in communication rounds. Empirically, experiments with real data corroborate a significant communication reduction compared to existing gradient- and stochastic gradient-based algorithms.

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NeurIPS Conference 2018 Conference Paper

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

Tianyi Chen
Georgios Giannakis
Tao Sun
Wotao Yin

This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation. Simple rules are designed to detect slowly-varying gradients and, therefore, trigger the reuse of outdated gradients. The resultant gradient-based algorithms are termed Lazily Aggregated Gradient --- justifying our acronym LAG used henceforth. Theoretically, the merits of this contribution are: i) the convergence rate is the same as batch gradient descent in strongly-convex, convex, and nonconvex cases; and, ii) if the distributed datasets are heterogeneous (quantified by certain measurable constants), the communication rounds needed to achieve a targeted accuracy are reduced thanks to the adaptive reuse of lagged gradients. Numerical experiments on both synthetic and real data corroborate a significant communication reduction compared to alternatives.

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NeurIPS Conference 2017 Conference Paper

Solving Most Systems of Random Quadratic Equations

Gang Wang
Georgios Giannakis
Yousef Saad
Jie Chen

This paper deals with finding an $n$-dimensional solution $\bm{x}$ to a system of quadratic equations $y_i=|\langle\bm{a}_i, \bm{x}\rangle|^2$, $1\le i \le m$, which in general is known to be NP-hard. We put forth a novel procedure, that starts with a \emph{weighted maximal correlation initialization} obtainable with a few power iterations, followed by successive refinements based on \emph{iteratively reweighted gradient-type iterations}. The novel techniques distinguish themselves from prior works by the inclusion of a fresh (re)weighting regularization. For certain random measurement models, the proposed procedure returns the true solution $\bm{x}$ with high probability in time proportional to reading the data $\{(\bm{a}_i; y_i)\}_{1\le i \le m}$, provided that the number $m$ of equations is some constant $c>0$ times the number $n$ of unknowns, that is, $m\ge cn$. Empirically, the upshots of this contribution are: i) perfect signal recovery in the high-dimensional regime given only an \emph{information-theoretic limit number} of equations; and, ii) (near-)optimal statistical accuracy in the presence of additive noise. Extensive numerical tests using both synthetic data and real images corroborate its improved signal recovery performance and computational efficiency relative to state-of-the-art approaches.

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NeurIPS Conference 2016 Conference Paper

Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow

Gang Wang
Georgios Giannakis

This paper puts forth a novel algorithm, termed \emph{truncated generalized gradient flow} (TGGF), to solve for $\bm{x}\in\mathbb{R}^n/\mathbb{C}^n$ a system of $m$ quadratic equations $y_i=|\langle\bm{a}_i, \bm{x}\rangle|^2$, $i=1, 2, \ldots, m$, which even for $\left\{\bm{a}_i\in\mathbb{R}^n/\mathbb{C}^n\right\}_{i=1}^m$ random is known to be \emph{NP-hard} in general. We prove that as soon as the number of equations $m$ is on the order of the number of unknowns $n$, TGGF recovers the solution exactly (up to a global unimodular constant) with high probability and complexity growing linearly with the time required to read the data $\left\{\left(\bm{a}_i; \, y_i\right)\right\}_{i=1}^m$. Specifically, TGGF proceeds in two stages: s1) A novel \emph{orthogonality-promoting} initialization that is obtained with simple power iterations; and, s2) a refinement of the initial estimate by successive updates of scalable \emph{truncated generalized gradient iterations}. The former is in sharp contrast to the existing spectral initializations, while the latter handles the rather challenging nonconvex and nonsmooth \emph{amplitude-based} cost function. Numerical tests demonstrate that: i) The novel orthogonality-promoting initialization method returns more accurate and robust estimates relative to its spectral counterparts; and ii) even with the same initialization, our refinement/truncation outperforms Wirtinger-based alternatives, all corroborating the superior performance of TGGF over state-of-the-art algorithms.

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