| 2026 | AAAI | Beyond Next Token Probabilities: Learnable, Fast Detection of Hallucinations and Data Contamination on LLM Output Distributions. | Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg, Yftah Ziser, Ran El-Yaniv, Gal Chechik, Haggai Maron |
| 2025 | ICML | 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, 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 | ICLR | A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. | Ido Galil, Mohammed Dabbah, Ran El-Yaniv |
| 2023 | ICLR | What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? | Ido Galil, Mohammed Dabbah, Ran El-Yaniv |
| 2021 | ICLR | Net-DNF: Effective Deep Modeling of Tabular Data. | Liran Katzir, Gal Elidan, Ran El-Yaniv |
| 2021 | WACV | TranstextNet: Transducing Text for Recognizing Unseen Visual Relationships. | Gal Sadeh Kenigsfield, Ran El-Yaniv |
| 2020 | AISTATS | Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. | Guy Uziel, Ran El-Yaniv |
| 2019 | ACL | Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. | Yair Feldman, Ran El-Yaniv |
| 2019 | ICLR | Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers. | Yonatan Geifman, Guy Uziel, Ran El-Yaniv |
| 2019 | ICML | SelectiveNet: A Deep Neural Network with an Integrated Reject Option. | Yonatan Geifman, Ran El-Yaniv |
| 2018 | AAAI | Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example. | Bar Hilleli, Ran El-Yaniv |
| 2018 | AISTATS | Growth-Optimal Portfolio Selection under CVaR Constraints. | Guy Uziel, Ran El-Yaniv |
| 2016 | POPL | Estimating types in binaries using predictive modeling. | Omer Katz, Ran El-Yaniv, Eran Yahav |
| 2014 | ICML | Concept Drift Detection Through Resampling. | Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer |
| 2007 | COLT | Transductive Rademacher Complexity and Its Applications. | Ran El-Yaniv, Dmitry Pechyony |
| 2006 | COLT | Stable Transductive Learning. | Ran El-Yaniv, Dmitry Pechyony |
| 2005 | ICML | Multi-way distributional clustering via pairwise interactions. | Ron Bekkerman, Ran El-Yaniv, Andrew McCallum |
| 2003 | ICML | Online Choice of Active Learning Algorithms. | Yoram Baram, Ran El-Yaniv, Kobi Luz |
| 2001 | ICML | Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. | Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram |
| 2001 | SIGIR | On Feature Distributional Clustering for Text Categorization. | Ron Bekkerman, Ran El-Yaniv, Yoad Winter, Naftali Tishby |
| 2000 | COLT | Localized Boosting. | Ron Meir, Ran El-Yaniv, Shai Ben-David |
| 2000 | LATIN | On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). | Allan Borodin, Ran El-Yaniv, Vincent Gogan |
| 1997 | SODA | Online List Accessing Algorithms and Their Applications: Recent Empirical Evidence. | Ran Bachrach, Ran El-Yaniv |
| 1995 | SODA | The Statistical Adversary Allows Optimal Money-Making Trading Strategies. | Andrew Chou, Jeremy R. Cooperstock, Ran El-Yaniv, Michael Klugerman, Frank Thomson Leighton |
| 1992 | FOCS | Competitive Analysis of Financial Games | Ran El-Yaniv, Amos Fiat, Richard M. Karp, G. Turpin |