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20162026
most citedJointly Measuring Diversity and Quality in Text Generation Models

11 citations · 20 across the 37 of their papers we have counts for

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cs.LG2026

Efficient Adversarial Attacks on High-dimensional Offline Bandits

Seyed Mohammad Hadi Hosseini, Amir Najafi, Mahdieh Soleymani Baghshah

Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large language models, by efficiently ident…

cs.LG2026

SUSD: Structured Unsupervised Skill Discovery through State Factorization

Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah

Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards. One of the most common USD approaches is to maximize the…

cs.LG2025

Limits and Gains of Test-Time Scaling in Vision-Language Reasoning

Mohammadjavad Ahmadpour, Amirmahdi Meighani, Payam Taebi +3

Test-time scaling (TTS) has emerged as a powerful paradigm for improving the reasoning ability of Large Language Models (LLMs) by allocating additional computation at inference, ye…

cs.LG2025

CER: Confidence Enhanced Reasoning in LLMs

Ali Razghandi, Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah

Ensuring the reliability of Large Language Models (LLMs) in complex reasoning tasks remains a formidable challenge, particularly in scenarios that demand precise mathematical calcu…

cs.LG2025

Inductive Biases for Zero-shot Systematic Generalization in Language-informed Reinforcement Learning

Negin Hashemi Dijujin, Seyed Roozbeh Razavi Rohani, Mohammad Mahdi Samiei +1

Sample efficiency and systematic generalization are two long-standing challenges in reinforcement learning. Previous studies have shown that involving natural language along with o…

cs.LG2024

CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives

Armin Saghafian, Amirmohammad Izadi, Negin Hashemi Dijujin +1

Grounding the instruction in the environment is a key step in solving language-guided goal-reaching reinforcement learning problems. In automated reinforcement learning, a key conc…