9 papers
Online Conformal Prediction via Universal Portfolio Algorithms
Tuo Liu, Edgar Dobriban, Francesco Orabona
Online conformal prediction (OCP) seeks prediction intervals that achieve long-run coverage for arbitrary (possibly adversarial) data streams, while remaining as informative…
A Best-of-Both-Worlds Proof for Tsallis-INF without Fenchel Conjugates
Wei-Cheng Lee, Francesco Orabona
In this short note, we present a simple derivation of the best-of-both-world guarantee for the Tsallis-INF multi-armed bandit algorithm from J. Zimmert and Y. Seldin. Tsallis-INF:…
Beyond the Ideal: Analyzing the Inexact Muon Update
Egor Shulgin, Sultan AlRashed, Francesco Orabona +1
The Muon optimizer has rapidly emerged as a powerful, geometry-aware alternative to AdamW, demonstrating strong performance in large-scale training of neural networks. However, a c…
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch +11
Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of…
Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization
Tuo Liu, El Mehdi Saad, Wojciech Kotłowski +1
Dual averaging and gradient descent with their stochastic variants stand as the two canonical recipe books for first-order optimization: Every modern variant can be viewed as a des…
STaR-Bets: Sequential Target-Recalculating Bets for Tighter Confidence Intervals
Václav Voráček, Francesco Orabona
The construction of confidence intervals for the mean of a bounded random variable is a classical problem in statistics with numerous applications in machine learning and virtually…