232 citations · 453 across the 44 of their papers we have counts for
7 papers · 1 filter
Scalable Utility-Aware Multiclass Calibration
Mahmoud Hegazy, Michael I. Jordan, Aymeric Dieuleveut
Ensuring that classifiers are well-calibrated, i.e., their predictions align with observed frequencies, is a minimal and fundamental requirement for classifiers to be viewed as tru…
Federated Majorize-Minimization: Beyond Parameter Aggregation
Aymeric Dieuleveut, Gersende Fort, Mahmoud Hegazy +1
This paper proposes a unified approach for designing stochastic optimization algorithms that robustly scale to the federated learning setting. Our work studies a class of Majorize-…
Valid Selection among Conformal Sets
Mahmoud Hegazy, Liviu Aolaritei, Michael I. Jordan +1
Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be av…
Tight analyses of first-order methods with error feedback
Daniel Berg Thomsen, Adrien Taylor, Aymeric Dieuleveut
Communication between agents often constitutes a major computational bottleneck in distributed learning. One of the most common mitigation strategies is to compress the information…
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
Paul Mangold, Alain Durmus, Aymeric Dieuleveut +1
This paper proposes a novel analysis for the Scaffold algorithm, a popular method for dealing with data heterogeneity in federated learning. While its convergence in deterministic…
Open Problem: Two Riddles in Heavy-Ball Dynamics
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
This short paper presents two open problems on the widely used Polyak's Heavy-Ball algorithm. The first problem is the method's ability to exactly \textit{accelerate} in dimension…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…