1 citations · 1 across the 6 of their papers we have counts for
8 papers
Exposing Diversity Bias in Deep Generative Models: Statistical Origins and Correction of Diversity Error
Farzan Farnia, Mohammad Jalali, Azim Ospanov
Deep generative models have achieved great success in producing high-quality samples, making them a central tool across machine learning applications. Beyond sample quality, an imp…
miniF2F-Lean Revisited: Reviewing Limitations and Charting a Path Forward
Azim Ospanov, Farzan Farnia, Roozbeh Yousefzadeh
We perform a thorough analysis of the formal and informal statements in the miniF2F benchmark from the perspective of an AI system that is tasked to participate in a math Olympiad…
HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs
Azim Ospanov, Zijin Feng, Jiacheng Sun +3
Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments. However, purely informal reasoni…
APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning
Azim Ospanov, Farzan Farnia, Roozbeh Yousefzadeh
Formal reasoning and automated theorem proving constitute a challenging subfield of machine learning, in which machines are tasked with proving mathematical theorems using formal l…
Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings
Azim Ospanov, Mohammad Jalali, Farzan Farnia
The use of CLIP embeddings to assess the fidelity of samples produced by text-to-image generative models has been extensively explored in the literature. While the widely adopted C…
Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs
Mohammad Jalali, Azim Ospanov, Amin Gohari +1
Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce diverse outputs remains underexplored. Existing divers…
Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees
Azim Ospanov, Farzan Farnia
Evaluating the diversity of generative models without reference data poses methodological challenges. The reference-free Vendi and RKE scores address this by quantifying the divers…
Towards a Scalable Reference-Free Evaluation of Generative Models
Azim Ospanov, Jingwei Zhang, Mohammad Jalali +3
While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the una…