7 citations · 34 across the 22 of their papers we have counts for
26 papers
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…
Advocate for Complete Benchmarks for Formal Reasoning with Formal/Informal Statements and Formal/Informal Proofs
Roozbeh Yousefzadeh, Xuenan Cao
This position paper provides a critical but constructive discussion of current practices in benchmarking and evaluative practices in the field of formal reasoning and automated the…
Mathesis: Towards Formal Theorem Proving from Natural Languages
Yu Xuejun, Jianyuan Zhong, Zijin Feng +17
Recent advances in large language models show strong promise for formal reasoning. However, most LLM-based theorem provers have long been constrained by the need for expert-written…
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…
A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems
Roozbeh Yousefzadeh, Xuenan Cao, Azim Ospanov
Using AI to write formal proofs for mathematical problems is a challenging task that has seen some advancements in recent years. Automated systems such as Lean can verify the corre…
An Ambiguity Measure for Recognizing the Unknowns in Deep Learning
Roozbeh Yousefzadeh
We study the understanding of deep neural networks from the scope in which they are trained on. While the accuracy of these models is usually impressive on the aggregate level, the…
Large Language Models' Understanding of Math: Source Criticism and Extrapolation
Roozbeh Yousefzadeh, Xuenan Cao
It has been suggested that large language models such as GPT-4 have acquired some form of understanding beyond the correlations among the words in text including some understanding…
Should Machine Learning Models Report to Us When They Are Clueless?
Roozbeh Yousefzadeh, Xuenan Cao
The right to AI explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolati…
Over-parameterization: A Necessary Condition for Models that Extrapolate
Roozbeh Yousefzadeh
In this work, we study over-parameterization as a necessary condition for having the ability for the models to extrapolate outside the convex hull of training set. We specifically,…
Deep Learning Generalization, Extrapolation, and Over-parameterization
Roozbeh Yousefzadeh
We study the generalization of over-parameterized deep networks (for image classification) in relation to the convex hull of their training sets. Despite their great success, gener…
Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
Roozbeh Yousefzadeh
The success of deep neural networks in image classification and learning can be partly attributed to the features they extract from images. It is often speculated about the propert…
To what extent should we trust AI models when they extrapolate?
Roozbeh Yousefzadeh, Xuenan Cao
Many applications affecting human lives rely on models that have come to be known under the umbrella of machine learning and artificial intelligence. These AI models are usually co…
A Homotopy Algorithm for Optimal Transport
Roozbeh Yousefzadeh
The optimal transport problem has many applications in machine learning, physics, biology, economics, etc. Although its goal is very clear and mathematically well-defined, finding…
Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models
Roozbeh Yousefzadeh, Jessica A. Mollick
We study the functional task of deep learning image classification models and show that image classification requires extrapolation capabilities. This suggests that new theories ha…
Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods
Roozbeh Yousefzadeh
Medical image datasets can have large number of images representing patients with different health conditions and various disease severity. When dealing with raw unlabeled image da…
Federated Learning without Revealing the Decision Boundaries
Roozbeh Yousefzadeh
We consider the recent privacy preserving methods that train the models not on original images, but on mixed images that look like noise and hard to trace back to the original imag…
A Sketching Method for Finding the Closest Point on a Convex Hull
Roozbeh Yousefzadeh
We develop a sketching algorithm to find the point on the convex hull of a dataset, closest to a query point outside it. Studying the convex hull of datasets can provide useful inf…
Deep Learning Generalization and the Convex Hull of Training Sets
Roozbeh Yousefzadeh
We study the generalization of deep learning models in relation to the convex hull of their training sets. A trained image classifier basically partitions its domain via decision b…
A Probabilistic Framework for Optimizing Real-time Decisions in Humanitarian Aid Delivery Systems
Roozbeh Yousefzadeh
This paper presents a computationally efficient model for optimizing real-time decisions in humanitarian aid delivery systems. Our formulation models a hierarchical system and is a…
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
Roozbeh Yousefzadeh, Furong Huang
Deep learning models extract, before a final classification layer, features or patterns which are key for their unprecedented advantageous performance. However, the process of comp…
Using Wavelets to Analyze Similarities in Image-Classification Datasets
Roozbeh Yousefzadeh
Deep learning image classifiers usually rely on huge training sets and their training process can be described as learning the similarities and differences among training images. B…
Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis
Roozbeh Yousefzadeh, Dianne P. O'Leary
Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Nevertheless, they are consist…
A Probabilistic Framework and a Homotopy Method for Real-time Hierarchical Freight Dispatch Decisions
Roozbeh Yousefzadeh, Dianne P. O'Leary
We propose a real-time decision framework for multimodal freight dispatch through a system of hierarchical hubs, using a probabilistic model for transit times. Instead of assigning…
Investigating Decision Boundaries of Trained Neural Networks
Roozbeh Yousefzadeh, Dianne P O'Leary
Deep learning models have been the subject of study from various perspectives, for example, their training process, interpretation, generalization error, robustness to adversarial…