7 citations · 19 across the 10 of their papers we have counts for
13 papers
What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation
Babak Barazandeh
The rank in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes better. We show that, under hard per-factor norm b…
OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories
Babak Barazandeh, Subhabrata Majumdar, George Michailidis
Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results. Current evaluation metrics reduce such a trajecto…
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
Babak Barazandeh, Subhabrata Majumdar, Vinay Prithyani +1
Large Language Models (LLMs) and high-dimensional perception networks increasingly rely on parameter-efficient fine-tuning (PEFT) to adapt to diverse operational contexts. However,…
Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning
Babak Barazandeh, Subhabrata Majumdar, Om Rajyaguru +1
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, offer compact and effective alternatives to full model fine-tuning by introducing low-rank updates to pre-trained weig…
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain
Dmitrii M. Ostrovskii, Babak Barazandeh, Meisam Razaviyayn
We study the problem of finding approximate first-order stationary points in optimization problems of the form , where the sets are conv…
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
Babak Barazandeh, Tianjian Huang, George Michailidis
Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs). However, most o…
Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models
Babak Barazandeh, Ali Ghafelebashi, Meisam Razaviyayn +1
Mixed linear regression (MLR) model is among the most exemplary statistical tools for modeling non-linear distributions using a mixture of linear models. When the additive noise in…
Solving a class of non-convex min-max games using adaptive momentum methods
Babak Barazandeh, Davoud Ataee Tarzanagh, George Michailidis
Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks. They use an exponential moving average of past gradients of the objective…
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method
Babak Barazandeh, Meisam Razaviyayn
Min-max saddle point games appear in a wide range of applications in machine leaning and signal processing. Despite their wide applicability, theoretical studies are mostly limited…
Training generative networks using random discriminators
Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi
In recent years, Generative Adversarial Networks (GANs) have drawn a lot of attentions for learning the underlying distribution of data in various applications. Despite their wide…
Designing an Optimal Portfolio for Iran's Stock Market with Genetic Algorithm using Neural Network Prediction of Risk and Return Stocks
Masoud Fekri, Babak Barazandeh
Optimal capital allocation between different assets is an important financial problem, which is generally framed as the portfolio optimization problem. General models include the s…
A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors
Babak Barazandeh, Mohammadhussein Rafieisakhaei, Sunwook Kim +3
Classification methods based on sparse estimation have drawn much attention recently, due to their effectiveness in processing high-dimensional data such as images. In this paper,…
On the Behavior of the Expectation-Maximization Algorithm for Mixture Models
Babak Barazandeh, Meisam Razaviyayn
Finite mixture models are among the most popular statistical models used in different data science disciplines. Despite their broad applicability, inference under these models typi…