1 citations · 1 across the 14 of their papers we have counts for
16 papers
Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering
Haemin Park, Diego Klabjan, Martin W. Braun +2
Federated deep clustering seeks to learn clustering-friendly representations from decentralized unlabeled data while preserving client privacy. However, Deep Embedded Clustering (D…
Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness
Siqiao Mu, Diego Klabjan
We establish the first convergence guarantees of gradient descent for general feedforward neural networks of any width or depth, with any initialization or dataset. We only assume…
Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
Haemin Park, Diego Klabjan, Martin W. Braun +2
Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard ce…
Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Yoonsik Hong, Diego Klabjan
Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. Entities at each level can be co…
Rank-Accuracy Trade-off for LoRA: A Gradient-Flow Analysis
Michael Rushka, Diego Klabjan
Previous empirical studies have shown that LoRA achieves accuracy comparable to full-parameter methods on downstream fine-tuning tasks, even for rank-1 updates. By contrast, the th…
On the Convergence Rate of LoRA Gradient Descent
Siqiao Mu, Diego Klabjan
The low-rank adaptation (LoRA) algorithm for fine-tuning large models has grown popular in recent years due to its remarkable performance and low computational requirements. LoRA t…
Descend or Rewind? Stochastic Gradient Descent Unlearning
Siqiao Mu, Diego Klabjan
Machine unlearning algorithms aim to remove the impact of selected training data from a model without the computational expenses of retraining from scratch. Two such algorithms are…
A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models
Shuyang Wang, Somayeh Moazeni, Diego Klabjan
Designing effective prompts is essential to guiding large language models (LLMs) toward desired responses. Automated prompt engineering aims to reduce reliance on manual effort by…
A Mirror Descent Perspective of Smoothed Sign Descent
Shuyang Wang, Diego Klabjan
Recent work by Woodworth et al. (2020) shows that the optimization dynamics of gradient descent for overparameterized problems can be viewed as low-dimensional dual dynamics induce…
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling
Jaeyeon Jang, Diego Klabjan, Han Liu +5
Real-time dynamic scheduling is a crucial but notoriously challenging task in modern manufacturing processes due to its high decision complexity. Recently, reinforcement learning (…
Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization
Haemin Park, Diego Klabjan
Federated Learning (FL) faces significant challenges related to communication efficiency and performance reduction when scaling to many clients. To address these issues, we explore…
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions
Siqiao Mu, Diego Klabjan
Machine unlearning algorithms aim to efficiently remove data from a model without retraining it from scratch, in order to remove corrupted or outdated data or respect a user's ``ri…
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
Jaeyeon Jang, Diego Klabjan, Veena Mendiratta +1
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most exis…
Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics
Garrett Blum, Ryan Doris, Diego Klabjan +2
Stress-strain curves, or more generally, stress functions, are an extremely important characterization of a material's mechanical properties. However, stress functions are often di…
On the Second-Order Convergence of Biased Policy Gradient Algorithms
Siqiao Mu, Diego Klabjan
Since the objective functions of reinforcement learning problems are typically highly nonconvex, it is desirable that policy gradient, the most popular algorithm, escapes saddle po…
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading
Shuyang Wang, Diego Klabjan
We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms in a highly stochastic environment o…