Ravid Shwartz-Ziv
15 papers · 2022–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Cross-Pollinator (15) π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (23) π§ Keyword Pioneer
π
Conference Polyglot
(6)
π
Triple Crown
β‘
Prolific Year
(7)
π
Century Club
(15)
β
The Questioner
Conferences
ICLR (5)
NIPS (5)
ICML (2)
AISTATS (1)
CVPR (1)
EMNLP (1)
Top co-authors
Keywords
self-supervised learning
(2)
large language model
(2)
representation learning
(2)
information theory
(2)
transfer learning
(1)
bayesian inference
(1)
natural language processing
(1)
argument mining
(1)
data augmentation
(1)
label smoothing
(1)
class imbalance
(1)
mutual information
(1)
performance evaluation
(1)
deep learning model
(1)
adaptive dropout
(1)
generalization bound
(1)
semantic clustering
(1)
uncertainty estimation
(1)
evaluation metric
(1)
hallucination detection
(1)
Papers
Layer by Layer: Uncovering Hidden Representations in Language Models
ICML 2025
Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable
AISTATS 2025
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation
CVPR 2025
The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
EMNLP 2025
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
ICLR 2025
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
ICLR 2025
LiveBench: A Challenging, Contamination-Limited LLM Benchmark
ICLR 2025
The Entropy Enigma: Success and Failure of Entropy Minimization
ICML 2024
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
NIPS 2024
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
ICLR 2024
Simplifying Neural Network Training Under Class Imbalance
NIPS 2023
Reverse Engineering Self-Supervised Learning
NIPS 2023
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
ICLR 2023
An Information Theory Perspective on Variance-Invariance-Covariance Regularization
NIPS 2023
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
NIPS 2022