Ran El-Yaniv - RankMe

139 papers A* 25A 4B 1Journal 82Unranked 25

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Beyond Next Token Probabilities: Learnable, Fast Detection of Hallucinations and Data Contamination on LLM Output Distributions. AAAI Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron
2026	J	`jnl`	CRoCoDiL: Continuous and Robust Conditioned Diffusion for Language. CoRR Roy Uziel, Omer Belhasin, Itay Levi, Akhiad Bercovich, Ran El-Yaniv, Ran Zilberstein, Michael Elad
2026	J	`jnl`	Extending Puzzle for Mixture-of-Experts Reasoning Models with Application to GPT-OSS Acceleration. CoRR Akhiad Bercovich, Nir Ailon, Vladimir Anisimov, Tomer Asida, Nave Assaf, Mohammad Dabbah, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Roi Koren, Itay Levy, Zach Moshe, Pavlo Molchanov, Najeeb Nabwani, Mostofa Patwari, Omri Puny, Tomer Ronen, Itamar Schen, Elad Segal, Ido Shahaf, Oren Tropp, Ran Zilberstein, Ran El-Yaniv
2026	J	`jnl`	Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips. Trans. Mach. Learn. Res. Ido Galil, Moshe Kimhi, Ran El-Yaniv
2026	J	`jnl`	When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation. CoRR Shani Goren, Ido Galil, Ran El-Yaniv
2025	J	`jnl`	Advancing Image Classification with Discrete Diffusion Classification Modeling. CoRR Omer Belhasin, Shelly Golan, Ran El-Yaniv, Michael Elad
2025	J	`jnl`	FFN Fusion: Rethinking Sequential Computation in Large Language Models. CoRR Akhiad Bercovich, Mohammad Dabbah, Omri Puny, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Ehud Karpas, Itay Levy, Zach Moshe, Najeeb Nabwani, Tomer Ronen, Itamar Schen, Elad Segal, Ido Shahaf, Oren Tropp, Ran Zilberstein, Ran El-Yaniv
2025	J	`jnl`	Learning on LLM Output Signatures for gray-box LLM Behavior Analysis. CoRR Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron
2025	J	`jnl`	No Data, No Optimization: A Lightweight Method To Disrupt Neural Networks With Sign-Flips. CoRR Ido Galil, Moshe Kimhi, Ran El-Yaniv
2025	A*	`conf`	Puzzle: Distillation-Based NAS for Inference-Optimized LLMs. ICML Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Netanel Haber, Ehud Karpas, Roi Koren, Itay Levy, Pavlo Molchanov, Shahar Mor, Zach Moshe, Najeeb Nabwani, Omri Puny, Ran Rubin, Itamar Schen, Ido Shahaf, Oren Tropp, Omer Ullman Argov, Ran Zilberstein, Ran El-Yaniv
2024	A*	`conf`	Hierarchical Selective Classification. NeurIPS Shani Goren, Ido Galil, Ran El-Yaniv
2024	J	`jnl`	Hierarchical Selective Classification. CoRR Shani Goren, Ido Galil, Ran El-Yaniv
2024	J	`jnl`	Puzzle: Distillation-Based NAS for Inference-Optimized LLMs. CoRR Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Netanel Haber, Ehud Karpas, Roi Koren, Itay Levy, Pavlo Molchanov, Shahar Mor, Zach Moshe, Najeeb Nabwani, Omri Puny, Ran Rubin, Itamar Schen, Ido Shahaf, Oren Tropp, Omer Ullman Argov, Ran Zilberstein, Ran El-Yaniv
2023	A*	`conf`	A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. ICLR Ido Galil, Mohammed Dabbah, Ran El-Yaniv
2023	J	`jnl`	A framework for benchmarking class-out-of-distribution detection and its application to ImageNet. CoRR Ido Galil, Mohammed Dabbah, Ran El-Yaniv
2023	J	`jnl`	What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers. CoRR Ido Galil, Mohammed Dabbah, Ran El-Yaniv
2023	A*	`conf`	What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? ICLR Ido Galil, Mohammed Dabbah, Ran El-Yaniv
2023	A*	`conf`	Window-Based Distribution Shift Detection for Deep Neural Networks. NeurIPS Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv
2022	J	`jnl`	Distribution Shift Detection for Deep Neural Networks. CoRR Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv
2022	A*	`conf`	TransBoost: Improving the Best ImageNet Performance using Deep Transduction. NeurIPS Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv
2022	J	`jnl`	TransBoost: Improving the Best ImageNet Performance using Deep Transduction. CoRR Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv
2022	J	`jnl`	Which models are innately best at uncertainty estimation? CoRR Ido Galil, Mohammed Dabbah, Ran El-Yaniv
2021	A*	`conf`	Disrupting Deep Uncertainty Estimation Without Harming Accuracy. NeurIPS Ido Galil, Ran El-Yaniv
2021	J	`jnl`	Disrupting Deep Uncertainty Estimation Without Harming Accuracy. CoRR Ido Galil, Ran El-Yaniv
2021	A*	`conf`	Net-DNF: Effective Deep Modeling of Tabular Data. ICLR Liran Katzir, Gal Elidan, Ran El-Yaniv
2021	J	`jnl`	Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN. CoRR Shai Ben-Assayag, Ran El-Yaniv
2021	A	`conf`	TranstextNet: Transducing Text for Recognizing Unseen Visual Relationships. WACV Gal Sadeh Kenigsfield, Ran El-Yaniv
2021	J	`jnl`	Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners. CoRR Mohammed Dabbah, Ran El-Yaniv
2020	—	`conf`	BebopNet: Deep Neural Models for Personalized Jazz Improvisations. ISMIR Shunit Haviv Hakimi, Nadav Bhonker, Ran El-Yaniv
2020	J	`jnl`	DNF-Net: A Neural Architecture for Tabular Data. CoRR Ami Abutbul, Gal Elidan, Liran Katzir, Ran El-Yaniv
2020	J	`jnl`	HydroNets: Leveraging River Structure for Hydrologic Modeling. CoRR Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, Ran El-Yaniv
2020	A	`conf`	Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. AISTATS Guy Uziel, Ran El-Yaniv
2019	J	`jnl`	Accurate Hydrologic Modeling Using Less Information. CoRR Guy Shalev, Ran El-Yaniv, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo
2019	—	`conf`	Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers. ICLR (Poster) Yonatan Geifman, Guy Uziel, Ran El-Yaniv
2019	A*	`conf`	Deep Active Learning with a Neural Architecture Search. NeurIPS Yonatan Geifman, Ran El-Yaniv
2019	J	`jnl`	Improved Detection of Adversarial Attacks via Penetration Distortion Maximization. CoRR Shai Rozenberg, Gal Elidan, Ran El-Yaniv
2019	J	`jnl`	Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships. CoRR Gal Sadeh Kenigsfield, Ran El-Yaniv
2019	J	`jnl`	ML for Flood Forecasting at Scale. CoRR 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, Oleg Zlydenko, Yossi Matias
2019	—	`conf`	Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. ACL (1) Yair Feldman, Ran El-Yaniv
2019	J	`jnl`	Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. CoRR Yair Feldman, Ran El-Yaniv
2019	A*	`conf`	SelectiveNet: A Deep Neural Network with an Integrated Reject Option. ICML Yonatan Geifman, Ran El-Yaniv
2019	J	`jnl`	SelectiveNet: A Deep Neural Network with an Integrated Reject Option. CoRR Yonatan Geifman, Ran El-Yaniv
2019	J	`jnl`	The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient. J. Mach. Learn. Res. Roei Gelbhart, Ran El-Yaniv
2018	J	`jnl`	Boosting Uncertainty Estimation for Deep Neural Classifiers. CoRR Yonatan Geifman, Guy Uziel, Ran El-Yaniv
2018	J	`jnl`	Deep Active Learning with a Neural Architecture Search. CoRR Yonatan Geifman, Ran El-Yaniv
2018	A*	`conf`	Deep Anomaly Detection Using Geometric Transformations. NeurIPS Izhak Golan, Ran El-Yaniv
2018	J	`jnl`	Deep Anomaly Detection Using Geometric Transformations. CoRR Izhak Golan, Ran El-Yaniv
2018	A	`conf`	Growth-Optimal Portfolio Selection under CVaR Constraints. AISTATS Guy Uziel, Ran El-Yaniv
2018	A*	`conf`	Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example. AAAI Bar Hilleli, Ran El-Yaniv
2017	J	`jnl`	Deep Active Learning over the Long Tail. CoRR Yonatan Geifman, Ran El-Yaniv
2017	J	`jnl`	Growth-Optimal Portfolio Selection under CVaR Constraints. CoRR Guy Uziel, Ran El-Yaniv
2017	J	`jnl`	Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. IEEE Trans. Pattern Anal. Mach. Intell. Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv
2017	—	`conf`	Multi-Objective Non-parametric Sequential Prediction. NIPS Guy Uziel, Ran El-Yaniv
2017	J	`jnl`	Multi-Objective Non-parametric Sequential Prediction. CoRR Guy Uziel, Ran El-Yaniv
2017	J	`jnl`	Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. J. Mach. Learn. Res. Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
2017	—	`conf`	Selective Classification for Deep Neural Networks. NIPS Yonatan Geifman, Ran El-Yaniv
2017	J	`jnl`	Selective Classification for Deep Neural Networks. CoRR Yonatan Geifman, Ran El-Yaniv
2017	J	`jnl`	The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression. CoRR Ran El-Yaniv, Yonatan Geifman, Yair Wiener
2017	J	`jnl`	The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient. CoRR Roei Gelbhart, Ran El-Yaniv
2016	—	`conf`	Binarized Neural Networks. NIPS Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
2016	J	`jnl`	Deep Learning of Robotic Tasks using Strong and Weak Human Supervision. CoRR Bar Hilleli, Ran El-Yaniv
2016	A*	`conf`	Estimating types in binaries using predictive modeling. POPL Omer Katz, Ran El-Yaniv, Eran Yahav
2016	J	`jnl`	Online Learning of Commission Avoidant Portfolio Ensembles. CoRR Guy Uziel, Ran El-Yaniv
2016	J	`jnl`	Online Learning of Portfolio Ensembles with Sector Exposure Regularization. CoRR Guy Uziel, Ran El-Yaniv
2016	J	`jnl`	Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. CoRR Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
2015	J	`jnl`	A compression technique for analyzing disagreement-based active learning. J. Mach. Learn. Res. Yair Wiener, Steve Hanneke, Ran El-Yaniv
2015	J	`jnl`	Agnostic Pointwise-Competitive Selective Classification. J. Artif. Intell. Res. Yair Wiener, Ran El-Yaniv
2015	J	`jnl`	Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. CoRR Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv
2014	J	`jnl`	A Compression Technique for Analyzing Disagreement-Based Active Learning. CoRR Yair Wiener, Steve Hanneke, Ran El-Yaniv
2014	A*	`conf`	Concept Drift Detection Through Resampling. ICML Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer
2014	J	`jnl`	Transductive Rademacher Complexity and its Applications. CoRR Ran El-Yaniv, Dmitry Pechyony
2013	J	`jnl`	Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness. CoRR Ran El-Yaniv, David Yanay
2012	J	`jnl`	Active Learning via Perfect Selective Classification. J. Mach. Learn. Res. Ran El-Yaniv, Yair Wiener
2012	J	`jnl`	Autoregressive short-term prediction of turning points using support vector regression CoRR Ran El-Yaniv, Alexandra Faynburd
2012	—	`conf`	Pointwise Tracking the Optimal Regression Function. NIPS Yair Wiener, Ran El-Yaniv
2012	—	`conf`	Supervised Learning of Semantic Relatedness. ECML/PKDD (1) Ran El-Yaniv, David Yanay
2011	—	`conf`	Agnostic Selective Classification. NIPS Yair Wiener, Ran El-Yaniv
2011	J	`jnl`	Can We Learn to Beat the Best Stock CoRR Allan Borodin, Ran El-Yaniv, Vincent Gogan
2011	J	`jnl`	Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms CoRR Philip Derbeko, Ran El-Yaniv, Ron Meir
2011	J	`jnl`	On Prediction Using Variable Order Markov Models CoRR Ron Begleiter, Ran El-Yaniv, Golan Yona
2011	—	`conf`	Selective Prediction of Financial Trends with Hidden Markov Models. NIPS Dmitry Pidan, Ran El-Yaniv
2010	J	`jnl`	Hierarchical Multiclass Decompositions with Application to Authorship Determination CoRR Ran El-Yaniv, Noam Etzion-Rosenberg
2010	J	`jnl`	On the Foundations of Adversarial Single-Class Classification CoRR Ran El-Yaniv, Mordechai Nisenson
2010	J	`jnl`	On the Foundations of Noise-free Selective Classification. J. Mach. Learn. Res. Ran El-Yaniv, Yair Wiener
2009	J	`jnl`	Transductive Rademacher Complexity and its Applications. J. Artif. Intell. Res. Ran El-Yaniv, Dmitry Pechyony
2008	J	`jnl`	Better multiclass classification via a margin-optimized single binary problem. Pattern Recognit. Lett. Ran El-Yaniv, Dmitry Pechyony, Elad Yom-Tov
2008	—	`conf`	Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
2008	J	`jnl`	Large margin vs. large volume in transductive learning. Mach. Learn. Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
2008	J	`jnl`	Repairing self-confident active-transductive learners using systematic exploration. Pattern Recognit. Lett. Ron Begleiter, Ran El-Yaniv, Dmitry Pechyony
2007	J	`jnl`	Size-density spectra and their application to image classification. Pattern Recognit. Igor Zingman, Ron Meir, Ran El-Yaniv
2007	A*	`conf`	Transductive Rademacher Complexity and Its Applications. COLT Ran El-Yaniv, Dmitry Pechyony
2006	—	`conf`	Optimal Single-Class Classification Strategies. NIPS Ran El-Yaniv, Mordechai Nisenson
2006	A*	`conf`	Stable Transductive Learning. COLT Ran El-Yaniv, Dmitry Pechyony
2006	J	`jnl`	Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition. J. Mach. Learn. Res. Ron Begleiter, Ran El-Yaniv
2005	J	`jnl`	Correcting BLAST e-Values for Low-Complexity Segments. J. Comput. Biol. Itai Sharon, Aaron Birkland, Kuan Y. Chang, Ran El-Yaniv, Golan Yona
2005	J	`jnl`	Effective transductive learning via objective model selection. Pattern Recognit. Lett. Ran El-Yaniv, Leonid Gerzon
2005	A*	`conf`	Multi-way distributional clustering via pairwise interactions. ICML Ron Bekkerman, Ran El-Yaniv, Andrew McCallum
2004	J	`jnl`	Can We Learn to Beat the Best Stock. J. Artif. Intell. Res. Allan Borodin, Ran El-Yaniv, Vincent Gogan
2004	J	`jnl`	Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. J. Artif. Intell. Res. Philip Derbeko, Ran El-Yaniv, Ron Meir
2004	J	`jnl`	How to Better Use Expert Advice. Mach. Learn. Rani Yaroshinsky, Ran El-Yaniv, Steven S. Seiden
2004	J	`jnl`	On Prediction Using Variable Order Markov Models. J. Artif. Intell. Res. Ron Begleiter, Ran El-Yaniv, Golan Yona
2004	J	`jnl`	Online Choice of Active Learning Algorithms. J. Mach. Learn. Res. Yoram Baram, Ran El-Yaniv, Kobi Luz
2003	—	`conf`	Can We Learn to Beat the Best Stock. NIPS Allan Borodin, Ran El-Yaniv, Vincent Gogan
2003	J	`jnl`	Distributional Word Clusters vs. Words for Text Categorization. J. Mach. Learn. Res. Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, Yoad Winter
2003	—	`conf`	Error Bounds for Transductive Learning via Compression and Clustering. NIPS Philip Derbeko, Ran El-Yaniv, Ron Meir
2003	A*	`conf`	Online Choice of Active Learning Algorithms. ICML Yoram Baram, Ran El-Yaniv, Kobi Luz
2003	J	`jnl`	Resilient-optimal interactive consistency in constant time. Distributed Comput. Michael Ben-Or, Ran El-Yaniv
2003	—	`conf`	Towards Behaviometric Security Systems: Learning to Identify a Typist. PKDD Mordechai Nisenson, Ido Yariv, Ran El-Yaniv, Ron Meir
2002	J	`jnl`	A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles. Mach. Learn. Shlomo Dubnov, Ran El-Yaniv, Yoram Gdalyahu, Elad Schneidman, Naftali Tishby, Golan Yona
2002	J	`jnl`	On Online Learning of Decision Lists. J. Mach. Learn. Res. Ziv Nevo, Ran El-Yaniv
2002	J	`jnl`	On the Competitive Theory and Practice of Online List Accessing Algorithms. Algorithmica Ran Bachrach, Ran El-Yaniv, M. Reinstadtler
2002	J	`jnl`	Synthesizing Sound Textures through Wavelet Tree Learning. IEEE Computer Graphics and Applications Shlomo Dubnov, Ziv Bar-Joseph, Ran El-Yaniv, Dani Lischinski, Michael Werman
2002	—	`conf`	Variance Optimized Bagging. ECML Philip Derbeko, Ran El-Yaniv, Ron Meir
2001	—	`conf`	Iterative Double Clustering for Unsupervised and Semi-Supervised Learning. NIPS Ran El-Yaniv, Oren Souroujon
2001	—	`conf`	Iterative Double Clustering for Unsupervised and Semi-supervised Learning. ECML Ran El-Yaniv, Oren Souroujon
2001	A*	`conf`	On Feature Distributional Clustering for Text Categorization. SIGIR Ron Bekkerman, Ran El-Yaniv, Yoad Winter, Naftali Tishby
2001	J	`jnl`	Optimal Search and One-Way Trading Online Algorithms. Algorithmica Ran El-Yaniv, Amos Fiat, Richard M. Karp, G. Turpin
2001	A*	`conf`	Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. ICML Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram
2001	J	`jnl`	Texture Mixing and Texture Movie Synthesis Using Statistical Learning. IEEE Trans. Vis. Comput. Graph. Ziv Bar-Joseph, Ran El-Yaniv, Dani Lischinski, Michael Werman
2000	A*	`conf`	Localized Boosting. COLT Ron Meir, Ran El-Yaniv, Shai Ben-David
2000	B	`conf`	On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). LATIN Allan Borodin, Ran El-Yaniv, Vincent Gogan
1999	J	`jnl`	Competitive Optimal On-Line Leasing. Algorithmica Ran El-Yaniv, Ron Kaniel, Nathan Linial
1999	—	`conf`	Granular Synthesis of Sound Textures Using Statistical Learning. ICMC Ziv Bar-Joseph, Dani Lischinski, Michael Werman, Shlomo Dubnov, Ran El-Yaniv
1999	J	`jnl`	On Randomization in On-Line Computation. Inf. Comput. Allan Borodin, Ran El-Yaniv
1998	J	`jnl`	Competitive Solutions for Online Financial Prob. ACM Comput. Surv. Ran El-Yaniv
1998	—	`book`	Online computation and competitive analysis. Allan Borodin, Ran El-Yaniv
1998	—	`conf`	Universal Classification Applied to Musical Sequences. ICMC Shlomo Dubnov, Gérard Assayag, Ran El-Yaniv
1997	—	`conf`	Agnostic Classification of Markovian Sequences. NIPS Ran El-Yaniv, Shai Fine, Naftali Tishby
1997	J	`jnl`	Nearly Optimal Competitive Online Replacement Policies. Math. Oper. Res. Ran El-Yaniv, Richard M. Karp
1997	A	`conf`	On Ranomization in Online Computation. CCC Allan Borodin, Ran El-Yaniv
1997	A*	`conf`	Online List Accessing Algorithms and Their Applications: Recent Empirical Evidence. SODA Ran Bachrach, Ran El-Yaniv
1996	—	`conf`	Competitive Solutions for On-line Financial Problems. Online Algorithms Ran El-Yaniv
1995	J	`jnl`	Geometric Two-Server Algorithms. Inf. Process. Lett. Ran El-Yaniv, Jon M. Kleinberg
1995	A*	`conf`	The Statistical Adversary Allows Optimal Money-Making Trading Strategies. SODA Andrew Chou, Jeremy R. Cooperstock, Ran El-Yaniv, Michael Klugerman, Frank Thomson Leighton
1994	—	`—`	On-line algorithms and financial decision making. Ran El-Yaniv
1993	—	`conf`	The Mortgage Problem. ISTCS Ran El-Yaniv, Richard M. Karp
1992	A*	`conf`	Competitive Analysis of Financial Games FOCS Ran El-Yaniv, Amos Fiat, Richard M. Karp, G. Turpin
1992	—	`conf`	Memory Adaptive Self-Stabilizing Protocols (Extended Abstract). WDAG Efthymios Anagnostou, Ran El-Yaniv, Vassos Hadzilacos
1991	—	`conf`	More on the Power of Random Walks: Uniform Self-Stabilizing Randomized Algorithms (Preliminary Report). WDAG Efthymios Anagnostou, Ran El-Yaniv