8 citations · 9 across the 8 of their papers we have counts for
8 papers
Mean-Field Parallel Decoding for Discrete Diffusion Language Models
Tamim Zoabi, Ameen Ali, Liran Ringel +1
Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding. However, selecting tokens independently by marginal confidence limi…
Accelerating Speculative Decoding with Block Diffusion Draft Trees
Liran Ringel, Yaniv Romano
Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel.…
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…
Learning a Continue-Thinking Token for Enhanced Test-Time Scaling
Liran Ringel, Elad Tolochinsky, Yaniv Romano
Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference time. Recent studies have shown that ov…
Segment-Based Attention Masking for GPTs
Shahar Katz, Liran Ringel, Yaniv Romano +1
Modern Language Models (LMs) owe much of their success to masked causal attention, the backbone of Generative Pre-Trained Transformer (GPT) models. Although GPTs can process the en…
Semi-Supervised Risk Control via Prediction-Powered Inference
Bat-Sheva Einbinder, Liran Ringel, Yaniv Romano
The risk-controlling prediction sets (RCPS) framework is a general tool for transforming the output of any machine learning model to design a predictive rule with rigorous error ra…
Early Time Classification with Accumulated Accuracy Gap Control
Liran Ringel, Regev Cohen, Daniel Freedman +2
Early time classification algorithms aim to label a stream of features without processing the full input stream, while maintaining accuracy comparable to that achieved by applying…
Achieving Risk Control in Online Learning Settings
Shai Feldman, Liran Ringel, Stephen Bates +1
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage o…