More on the Power of Random Walks: Uniform Self-Stabilizing Randomized Algorithms (Preliminary Report). - Efthymios Anagnostou; Ran El-Yaniv (WDAG, 1991)

Competitive Analysis of Financial Games - Ran El-Yaniv; Amos Fiat; Richard M. Karp; G. Turpin (FOCS, 1992)

Memory Adaptive Self-Stabilizing Protocols (Extended Abstract). - Efthymios Anagnostou; Ran El-Yaniv; Vassos Hadzilacos (WDAG, 1992)

The Mortgage Problem. - Ran El-Yaniv; Richard M. Karp (ISTCS, 1993)

On-line algorithms and financial decision making. - Ran El-Yaniv (1994)

The Statistical Adversary Allows Optimal Money-Making Trading Strategies. - Andrew Chou; Jeremy R. Cooperstock; Ran El-Yaniv; Michael Klugerman; Frank Thomson Leighton (SODA, 1995)

Geometric Two-Server Algorithms. - Ran El-Yaniv; Jon M. Kleinberg (Inf. Process. Lett., 1995)

Competitive Solutions for On-line Financial Problems. - Ran El-Yaniv (Online Algorithms, 1996)

On Ranomization in Online Computation. - Allan Borodin; Ran El-Yaniv (CCC, 1997)

Agnostic Classification of Markovian Sequences. - Ran El-Yaniv; Shai Fine; Naftali Tishby (NIPS, 1997)

Online List Accessing Algorithms and Their Applications: Recent Empirical Evidence. - Ran Bachrach; Ran El-Yaniv (SODA, 1997)

Nearly Optimal Competitive Online Replacement Policies. - Ran El-Yaniv; Richard M. Karp (Math. Oper. Res., 1997)

Online computation and competitive analysis. - Allan Borodin; Ran El-Yaniv (Cambridge University Press, 1998)

Universal Classification Applied to Musical Sequences. - Shlomo Dubnov; Gérard Assayag; Ran El-Yaniv (ICMC, 1998)

Competitive Solutions for Online Financial Prob. - Ran El-Yaniv (ACM Comput. Surv., 1998)

Granular Synthesis of Sound Textures Using Statistical Learning. - Ziv Bar-Joseph; Dani Lischinski; Michael Werman; Shlomo Dubnov; Ran El-Yaniv (ICMC, 1999)

Competitive Optimal On-Line Leasing. - Ran El-Yaniv; Ron Kaniel; Nathan Linial (Algorithmica, 1999)

On Randomization in On-Line Computation. - Allan Borodin; Ran El-Yaniv (Inf. Comput., 1999)

Localized Boosting. - Ron Meir; Ran El-Yaniv; Shai Ben-David (COLT, 2000)

On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). - Allan Borodin; Ran El-Yaniv; Vincent Gogan (LATIN, 2000)

Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. - Gregory Shakhnarovich; Ran El-Yaniv; Yoram Baram (ICML, 2001)

Iterative Double Clustering for Unsupervised and Semi-Supervised Learning. - Ran El-Yaniv; Oren Souroujon (NIPS, 2001)

On Feature Distributional Clustering for Text Categorization. - Ron Bekkerman; Ran El-Yaniv; Yoad Winter; Naftali Tishby (SIGIR, 2001)

Optimal Search and One-Way Trading Online Algorithms. - Ran El-Yaniv; Amos Fiat; Richard M. Karp; G. Turpin (Algorithmica, 2001)

Texture Mixing and Texture Movie Synthesis Using Statistical Learning. - Ziv Bar-Joseph; Ran El-Yaniv; Dani Lischinski; Michael Werman (IEEE Trans. Vis. Comput. Graph., 2001)

Variance Optimized Bagging. - Philip Derbeko; Ran El-Yaniv; Ron Meir (ECML, 2002)

On the Competitive Theory and Practice of Online List Accessing Algorithms. - Ran Bachrach; Ran El-Yaniv; M. Reinstadtler (Algorithmica, 2002)

Synthesizing Sound Textures through Wavelet Tree Learning. - Shlomo Dubnov; Ziv Bar-Joseph; Ran El-Yaniv; Dani Lischinski; Michael Werman (IEEE Computer Graphics and Applications, 2002)

On Online Learning of Decision Lists. - Ziv Nevo; Ran El-Yaniv (J. Mach. Learn. Res., 2002)

A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles. - Shlomo Dubnov; Ran El-Yaniv; Yoram Gdalyahu; Elad Schneidman; Naftali Tishby; Golan Yona (Mach. Learn., 2002)

Error Bounds for Transductive Learning via Compression and Clustering. - Philip Derbeko; Ran El-Yaniv; Ron Meir (NIPS, 2003)

Towards Behaviometric Security Systems: Learning to Identify a Typist. - Mordechai Nisenson; Ido Yariv; Ran El-Yaniv; Ron Meir (PKDD, 2003)

Resilient-optimal interactive consistency in constant time. - Michael Ben-Or; Ran El-Yaniv (Distributed Comput., 2003)

Distributional Word Clusters vs. Words for Text Categorization. - Ron Bekkerman; Ran El-Yaniv; Naftali Tishby; Yoad Winter (J. Mach. Learn. Res., 2003)

On Prediction Using Variable Order Markov Models. - Ron Begleiter; Ran El-Yaniv; Golan Yona (J. Artif. Intell. Res., 2004)

Can We Learn to Beat the Best Stock. - Allan Borodin; Ran El-Yaniv; Vincent Gogan (J. Artif. Intell. Res., 2004)

Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. - Philip Derbeko; Ran El-Yaniv; Ron Meir (J. Artif. Intell. Res., 2004)

Online Choice of Active Learning Algorithms. - Yoram Baram; Ran El-Yaniv; Kobi Luz (J. Mach. Learn. Res., 2004)

How to Better Use Expert Advice. - Rani Yaroshinsky; Ran El-Yaniv; Steven S. Seiden (Mach. Learn., 2004)

Multi-way distributional clustering via pairwise interactions. - Ron Bekkerman; Ran El-Yaniv; Andrew McCallum (ICML, 2005)

Correcting BLAST e-Values for Low-Complexity Segments. - Itai Sharon; Aaron Birkland; Kuan Y. Chang; Ran El-Yaniv; Golan Yona (J. Comput. Biol., 2005)

Effective transductive learning via objective model selection. - Ran El-Yaniv; Leonid Gerzon (Pattern Recognit. Lett., 2005)

Stable Transductive Learning. - Ran El-Yaniv; Dmitry Pechyony (COLT, 2006)

Optimal Single-Class Classification Strategies. - Ran El-Yaniv; Mordechai Nisenson (NIPS, 2006)

Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition. - Ron Begleiter; Ran El-Yaniv (J. Mach. Learn. Res., 2006)

Size-density spectra and their application to image classification. - Igor Zingman; Ron Meir; Ran El-Yaniv (Pattern Recognit., 2007)

Large margin vs. large volume in transductive learning. - Ran El-Yaniv; Dmitry Pechyony; Vladimir Vapnik (Mach. Learn., 2008)

Repairing self-confident active-transductive learners using systematic exploration. - Ron Begleiter; Ran El-Yaniv; Dmitry Pechyony (Pattern Recognit. Lett., 2008)

Better multiclass classification via a margin-optimized single binary problem. - Ran El-Yaniv; Dmitry Pechyony; Elad Yom-Tov (Pattern Recognit. Lett., 2008)

Transductive Rademacher Complexity and its Applications. - Ran El-Yaniv; Dmitry Pechyony (J. Artif. Intell. Res., 2009)

Hierarchical Multiclass Decompositions with Application to Authorship Determination - Ran El-Yaniv; Noam Etzion-Rosenberg (CoRR, 2010)

On the Foundations of Adversarial Single-Class Classification - Ran El-Yaniv; Mordechai Nisenson (CoRR, 2010)

On the Foundations of Noise-free Selective Classification. - Ran El-Yaniv; Yair Wiener (J. Mach. Learn. Res., 2010)

Selective Prediction of Financial Trends with Hidden Markov Models. - Dmitry Pidan; Ran El-Yaniv (NIPS, 2011)

Agnostic Selective Classification. - Yair Wiener; Ran El-Yaniv (NIPS, 2011)

Pointwise Tracking the Optimal Regression Function. - Yair Wiener; Ran El-Yaniv (NIPS, 2012)

Supervised Learning of Semantic Relatedness. - Ran El-Yaniv; David Yanay (ECML/PKDD (1), 2012)

Autoregressive short-term prediction of turning points using support vector regression - Ran El-Yaniv; Alexandra Faynburd (CoRR, 2012)

Active Learning via Perfect Selective Classification. - Ran El-Yaniv; Yair Wiener (J. Mach. Learn. Res., 2012)

Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness. - Ran El-Yaniv; David Yanay (CoRR, 2013)

Concept Drift Detection Through Resampling. - Maayan Harel; Shie Mannor; Ran El-Yaniv; Koby Crammer (ICML, 2014)

Agnostic Pointwise-Competitive Selective Classification. - Yair Wiener; Ran El-Yaniv (J. Artif. Intell. Res., 2015)

A compression technique for analyzing disagreement-based active learning. - Yair Wiener; Steve Hanneke; Ran El-Yaniv (J. Mach. Learn. Res., 2015)

Binarized Neural Networks. - Itay Hubara; Matthieu Courbariaux; Daniel Soudry; Ran El-Yaniv; Yoshua Bengio (NIPS, 2016)

Estimating types in binaries using predictive modeling. - Omer Katz; Ran El-Yaniv; Eran Yahav (POPL, 2016)

Deep Learning of Robotic Tasks using Strong and Weak Human Supervision. - Bar Hilleli; Ran El-Yaniv (CoRR, 2016)

Online Learning of Portfolio Ensembles with Sector Exposure Regularization. - Guy Uziel; Ran El-Yaniv (CoRR, 2016)

Online Learning of Commission Avoidant Portfolio Ensembles. - Guy Uziel; Ran El-Yaniv (CoRR, 2016)

Selective Classification for Deep Neural Networks. - Yonatan Geifman; Ran El-Yaniv (NIPS, 2017)

Multi-Objective Non-parametric Sequential Prediction. - Guy Uziel; Ran El-Yaniv (NIPS, 2017)

The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression. - Ran El-Yaniv; Yonatan Geifman; Yair Wiener (CoRR, 2017)

Deep Active Learning over the Long Tail. - Yonatan Geifman; Ran El-Yaniv (CoRR, 2017)

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. - Itay Hubara; Matthieu Courbariaux; Daniel Soudry; Ran El-Yaniv; Yoshua Bengio (J. Mach. Learn. Res., 2017)

Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. - Noam Segev; Maayan Harel; Shie Mannor; Koby Crammer; Ran El-Yaniv (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example. - Bar Hilleli; Ran El-Yaniv (AAAI, 2018)

Growth-Optimal Portfolio Selection under CVaR Constraints. - Guy Uziel; Ran El-Yaniv (AISTATS, 2018)

Deep Anomaly Detection Using Geometric Transformations. - Izhak Golan; Ran El-Yaniv (NeurIPS, 2018)

Boosting Uncertainty Estimation for Deep Neural Classifiers. - Yonatan Geifman; Guy Uziel; Ran El-Yaniv (CoRR, 2018)

Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. - Yair Feldman; Ran El-Yaniv (ACL (1), 2019)

Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers. - Yonatan Geifman; Guy Uziel; Ran El-Yaniv (ICLR (Poster), 2019)

SelectiveNet: A Deep Neural Network with an Integrated Reject Option. - Yonatan Geifman; Ran El-Yaniv (ICML, 2019)

Deep Active Learning with a Neural Architecture Search. - Yonatan Geifman; Ran El-Yaniv (NeurIPS, 2019)

ML for Flood Forecasting at Scale. - Sella Nevo; Vova Anisimov; Gal Elidan; Ran El-Yaniv; Pete Giencke; Yotam Gigi; Avinatan Hassidim; Zach Moshe; Mor Schlesinger; Guy Shalev; Ajai Tirumali; Ami Wiesel; et al. (CoRR, 2019)

Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships. - Gal Sadeh Kenigsfield; Ran El-Yaniv (CoRR, 2019)

Improved Detection of Adversarial Attacks via Penetration Distortion Maximization. - Shai Rozenberg; Gal Elidan; Ran El-Yaniv (CoRR, 2019)

Accurate Hydrologic Modeling Using Less Information. - Guy Shalev; Ran El-Yaniv; Daniel Klotz; Frederik Kratzert; Asher Metzger; Sella Nevo (CoRR, 2019)

The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient. - Roei Gelbhart; Ran El-Yaniv (J. Mach. Learn. Res., 2019)

Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. - Guy Uziel; Ran El-Yaniv (AISTATS, 2020)

BebopNet: Deep Neural Models for Personalized Jazz Improvisations. - Shunit Haviv Hakimi; Nadav Bhonker; Ran El-Yaniv (ISMIR, 2020)

DNF-Net: A Neural Architecture for Tabular Data. - Ami Abutbul; Gal Elidan; Liran Katzir 001; Ran El-Yaniv (CoRR, 2020)

HydroNets: Leveraging River Structure for Hydrologic Modeling. - Zach Moshe; Asher Metzger; Gal Elidan; Frederik Kratzert; Sella Nevo; Ran El-Yaniv (CoRR, 2020)

Net-DNF: Effective Deep Modeling of Tabular Data. - Liran Katzir 001; Gal Elidan; Ran El-Yaniv (ICLR, 2021)

Disrupting Deep Uncertainty Estimation Without Harming Accuracy. - Ido Galil; Ran El-Yaniv (NeurIPS, 2021)

TranstextNet: Transducing Text for Recognizing Unseen Visual Relationships. - Gal Sadeh Kenigsfield; Ran El-Yaniv (WACV, 2021)

Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN. - Shai Ben-Assayag; Ran El-Yaniv (CoRR, 2021)

Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners. - Mohammed Dabbah; Ran El-Yaniv (CoRR, 2021)

TransBoost: Improving the Best ImageNet Performance using Deep Transduction. - Omer Belhasin; Guy Bar-Shalom; Ran El-Yaniv (NeurIPS, 2022)

Which models are innately best at uncertainty estimation? - Ido Galil; Mohammed Dabbah; Ran El-Yaniv (CoRR, 2022)

Distribution Shift Detection for Deep Neural Networks. - Guy Bar-Shalom; Yonatan Geifman; Ran El-Yaniv (CoRR, 2022)

A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. - Ido Galil; Mohammed Dabbah; Ran El-Yaniv (ICLR, 2023)

What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? - Ido Galil; Mohammed Dabbah; Ran El-Yaniv (ICLR, 2023)

Window-Based Distribution Shift Detection for Deep Neural Networks. - Guy Bar-Shalom; Yonatan Geifman; Ran El-Yaniv (NeurIPS, 2023)

Hierarchical Selective Classification. - Shani Goren; Ido Galil; Ran El-Yaniv (NeurIPS, 2024)

Puzzle: Distillation-Based NAS for Inference-Optimized LLMs. - Akhiad Bercovich; Tomer Ronen; Talor Abramovich; Nir Ailon; Nave Assaf; Mohammad Dabbah; Ido Galil; Amnon Geifman; Yonatan Geifman; Izhak Golan; Netanel Haber; Ehud Karpas; et al. (CoRR, 2024)

No Data, No Optimization: A Lightweight Method To Disrupt Neural Networks With Sign-Flips. - Ido Galil; Moshe Kimhi; Ran El-Yaniv (CoRR, 2025)

Learning on LLM Output Signatures for gray-box LLM Behavior Analysis. - Guy Bar-Shalom; Fabrizio Frasca; Derek Lim; Yoav Gelberg; Yftah Ziser; Ran El-Yaniv; Gal Chechik; Haggai Maron (CoRR, 2025)

FFN Fusion: Rethinking Sequential Computation in Large Language Models. - Akhiad Bercovich; Mohammad Dabbah; Omri Puny; Ido Galil; Amnon Geifman; Yonatan Geifman; Izhak Golan; Ehud Karpas; Itay Levy; Zach Moshe; Najeeb Nabwani; Tomer Ronen; et al. (CoRR, 2025)

Llama-Nemotron: Efficient Reasoning Models. - Akhiad Bercovich; Itay Levy; Izik Golan; Mohammad Dabbah; Ran El-Yaniv; Omri Puny; Ido Galil; Zach Moshe; Tomer Ronen; Najeeb Nabwani; Ido Shahaf; Oren Tropp; et al. (CoRR, 2025)