1 citations · 1 across the 8 of their papers we have counts for
9 papers
Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
Liran Ringel, Ameen Ali, Yaniv Romano
Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it…
Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective
Osvaldo Simeone, Yaniv Romano
In context-specific applications such as robotics, telecommunications, and healthcare, artificial intelligence systems often face the challenge of limited training data. This scarc…
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs
Hen Davidov, Shai Feldman, Gilad Freidkin +1
We introduce time-to-unsafe-sampling, a novel safety measure for generative models, defined as the number of generations required by a large language model (LLM) to trigger an unsa…
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
Shai Feldman, Stephen Bates, Yaniv Romano
We introduce a framework for robust uncertainty quantification in situations where labeled training data are corrupted, through noisy or missing labels. We build on conformal predi…
Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach
Yarin Bar, Shalev Shaer, Yaniv Romano
We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution s…
Robust Conformal Prediction Using Privileged Information
Shai Feldman, Yaniv Romano
We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach…
Predictive Uncertainty Quantification with Missing Covariates
Margaux Zaffran, Julie Josse, Yaniv Romano +1
Predictive uncertainty quantification is crucial in decision-making problems. We investigate how to adequately quantify predictive uncertainty with missing covariates. A bottleneck…
Provably Robust Conformal Prediction with Improved Efficiency
Ge Yan, Yaniv Romano, Tsui-Wei Weng
Conformal prediction is a powerful tool to generate uncertainty sets with guaranteed coverage using any predictive model, under the assumption that the training and test data are i…
Quantum Sparse Coding
Yaniv Romano, Harel Primack, Talya Vaknin +5
The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is NP-…