2 citations · 18 across the 31 of their papers we have counts for
14 papers · 1 filter
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction
Arshia Abolghasemi, Seyed Reza Tavakoli, Parsa Naderi +7
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time ser…
-FUM: Federated Unlearning via min--max and -divergence
Radmehr Karimian, Amirhossein Bagheri, Meghdad Kurmanji +2
Federated learning (FL) enables collaborative training while keeping raw data on clients. Deletion requests and the discovery of poisoned data create a need to remove selected cont…
ReDiF: Resource-Efficient Few-Step Diffusion Distillation via Reinforcement Learning
Amirhossein Tighkhorshid, Zahra Dehghanian, Gholamali Aminian +2
Step distillation accelerates diffusion sampling by training a few-step student to imitate a many-step teacher, but distillation itself remains expensive. Typically, this requires…
FraudTransformer: Time-Aware GPT for Transaction Fraud Detection
Gholamali Aminian, Andrew Elliott, Tiger Li +8
Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransfor…
Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
Armin Behnamnia, Gholamali Aminian, Alireza Aghaei +3
Off-policy learning and evaluation leverage logged bandit feedback datasets, which contain context, action, propensity score, and feedback for each data point. These scenarios face…
Semi-pessimistic Reinforcement Learning
Jin Zhu, Xin Zhou, Jiaang Yao +5
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected data. However, it faces challenges of distributional shift, where the learned policy may enco…
UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification
Mohammad T. Teimuri, Zahra Dehghanian, Gholamali Aminian +1
Graph-structured datasets often suffer from class imbalance, which complicates node classification tasks. In this work, we address this issue by first providing an upper bound on p…
KL-Regularized RLHF with Multiple Reference Models: Exact Solutions and Sample Complexity
Gholamali Aminian, Amir R. Asadi, Idan Shenfeld +1
Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underuti…
Robust Semi-supervised Learning via -Divergence and -Rényi Divergence
Gholamali Aminian, Amirhossien Bagheri, Mahyar JafariNodeh +2
This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspira…
On the Generalization Error of Meta Learning for the Gibbs Algorithm
Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian +2
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm. Our exact characterization of the expected meta generalization error for t…
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
Gholamali Aminian, Saeed Masiha, Laura Toni +1
Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method,…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
Yuheng Bu, Gholamali Aminian, Laura Toni +2
We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, -…
Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Bounding the generalization error of a supervised learning algorithm is one of the most important problems in learning theory, and various approaches have been developed. However,…