1 citations · 1 across the 2 of their papers we have counts for
4 papers
LARP: Learner-Agnostic Robust Data Prefiltering
Kristian Minchev, Dimitar I. Dimitrov, Nikola Konstantinov
Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance. This creates a ne…
The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs
Jasper Dekoninck, Ivo Petrov, Kristian Minchev +13
In recent months, large language models (LLMs) have made significant progress in mathematical proof generation, but further advancement is hindered by the lack of a large-scale, hi…
Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad
Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev +5
Recent math benchmarks for large language models (LLMs) such as MathArena indicate that state-of-the-art reasoning models achieve impressive performance on mathematical competition…
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning
Dimitar Chakarov, Nikita Tsoy, Kristian Minchev +1
Federated learning (FL) is a distributed collaborative learning method, where multiple clients learn together by sharing gradient updates instead of raw data. However, it is well-k…