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Ran El-Yaniv
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2020 – today
- 2024
- [i40]Shani Goren, Ido Galil, Ran El-Yaniv:
Hierarchical Selective Classification. CoRR abs/2405.11533 (2024) - 2023
- [c52]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. ICLR 2023 - [c51]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? ICLR 2023 - [c50]Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv:
Window-Based Distribution Shift Detection for Deep Neural Networks. NeurIPS 2023 - [i39]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers. CoRR abs/2302.11874 (2023) - [i38]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet. CoRR abs/2302.11893 (2023) - 2022
- [c49]Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv:
TransBoost: Improving the Best ImageNet Performance using Deep Transduction. NeurIPS 2022 - [i37]Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv:
TransBoost: Improving the Best ImageNet Performance using Deep Transduction. CoRR abs/2205.13331 (2022) - [i36]Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
Which models are innately best at uncertainty estimation? CoRR abs/2206.02152 (2022) - [i35]Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv:
Distribution Shift Detection for Deep Neural Networks. CoRR abs/2210.10897 (2022) - 2021
- [c48]Liran Katzir, Gal Elidan, Ran El-Yaniv:
Net-DNF: Effective Deep Modeling of Tabular Data. ICLR 2021 - [c47]Ido Galil, Ran El-Yaniv:
Disrupting Deep Uncertainty Estimation Without Harming Accuracy. NeurIPS 2021: 21285-21296 - [c46]Gal Sadeh Kenigsfield, Ran El-Yaniv:
TranstextNet: Transducing Text for Recognizing Unseen Visual Relationships. WACV 2021: 1954-1963 - [i34]Shai Ben-Assayag, Ran El-Yaniv:
Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN. CoRR abs/2107.08387 (2021) - [i33]Ido Galil, Ran El-Yaniv:
Disrupting Deep Uncertainty Estimation Without Harming Accuracy. CoRR abs/2110.13741 (2021) - [i32]Mohammed Dabbah, Ran El-Yaniv:
Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners. CoRR abs/2111.13550 (2021) - 2020
- [c45]Guy Uziel, Ran El-Yaniv:
Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. AISTATS 2020: 100-110 - [c44]Shunit Haviv Hakimi, Nadav Bhonker, Ran El-Yaniv:
BebopNet: Deep Neural Models for Personalized Jazz Improvisations. ISMIR 2020: 828-836 - [i31]Ami Abutbul, Gal Elidan, Liran Katzir, Ran El-Yaniv:
DNF-Net: A Neural Architecture for Tabular Data. CoRR abs/2006.06465 (2020) - [i30]Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, Ran El-Yaniv:
HydroNets: Leveraging River Structure for Hydrologic Modeling. CoRR abs/2007.00595 (2020)
2010 – 2019
- 2019
- [j33]Roei Gelbhart, Ran El-Yaniv:
The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient. J. Mach. Learn. Res. 20: 33:1-33:38 (2019) - [c43]Yair Feldman, Ran El-Yaniv:
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. ACL (1) 2019: 2296-2309 - [c42]Yonatan Geifman, Guy Uziel, Ran El-Yaniv:
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers. ICLR (Poster) 2019 - [c41]Yonatan Geifman, Ran El-Yaniv:
SelectiveNet: A Deep Neural Network with an Integrated Reject Option. ICML 2019: 2151-2159 - [c40]Yonatan Geifman, Ran El-Yaniv:
Deep Active Learning with a Neural Architecture Search. NeurIPS 2019: 5974-5984 - [i29]Yonatan Geifman, Ran El-Yaniv:
SelectiveNet: A Deep Neural Network with an Integrated Reject Option. CoRR abs/1901.09192 (2019) - [i28]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:
ML for Flood Forecasting at Scale. CoRR abs/1901.09583 (2019) - [i27]Yair Feldman, Ran El-Yaniv:
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. CoRR abs/1906.06606 (2019) - [i26]Gal Sadeh Kenigsfield, Ran El-Yaniv:
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships. CoRR abs/1910.12324 (2019) - [i25]Shai Rozenberg, Gal Elidan, Ran El-Yaniv:
Improved Detection of Adversarial Attacks via Penetration Distortion Maximization. CoRR abs/1911.00870 (2019) - [i24]Guy Shalev, Ran El-Yaniv, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo:
Accurate Hydrologic Modeling Using Less Information. CoRR abs/1911.09427 (2019) - 2018
- [c39]Bar Hilleli, Ran El-Yaniv:
Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example. AAAI 2018: 1471-1478 - [c38]Guy Uziel, Ran El-Yaniv:
Growth-Optimal Portfolio Selection under CVaR Constraints. AISTATS 2018: 48-57 - [c37]Izhak Golan, Ran El-Yaniv:
Deep Anomaly Detection Using Geometric Transformations. NeurIPS 2018: 9781-9791 - [i23]Yonatan Geifman, Guy Uziel, Ran El-Yaniv:
Boosting Uncertainty Estimation for Deep Neural Classifiers. CoRR abs/1805.08206 (2018) - [i22]Izhak Golan, Ran El-Yaniv:
Deep Anomaly Detection Using Geometric Transformations. CoRR abs/1805.10917 (2018) - [i21]Yonatan Geifman, Ran El-Yaniv:
Deep Active Learning with a Neural Architecture Search. CoRR abs/1811.07579 (2018) - 2017
- [j32]Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. J. Mach. Learn. Res. 18: 187:1-187:30 (2017) - [j31]Noam Segev, Maayan Harel, Shie Mannor
, Koby Crammer, Ran El-Yaniv:
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. IEEE Trans. Pattern Anal. Mach. Intell. 39(9): 1811-1824 (2017) - [c36]Guy Uziel, Ran El-Yaniv:
Multi-Objective Non-parametric Sequential Prediction. NIPS 2017: 3372-3380 - [c35]Yonatan Geifman, Ran El-Yaniv:
Selective Classification for Deep Neural Networks. NIPS 2017: 4878-4887 - [i20]Guy Uziel, Ran El-Yaniv:
Multi-Objective Non-parametric Sequential Prediction. CoRR abs/1703.01680 (2017) - [i19]Roei Gelbhart, Ran El-Yaniv:
The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient. CoRR abs/1703.06536 (2017) - [i18]Ran El-Yaniv, Yonatan Geifman, Yair Wiener:
The Prediction Advantage: A Universally Meaningful Performance Measure for Classification and Regression. CoRR abs/1705.08499 (2017) - [i17]Yonatan Geifman, Ran El-Yaniv:
Selective Classification for Deep Neural Networks. CoRR abs/1705.08500 (2017) - [i16]Guy Uziel, Ran El-Yaniv:
Growth-Optimal Portfolio Selection under CVaR Constraints. CoRR abs/1705.09800 (2017) - [i15]Yonatan Geifman, Ran El-Yaniv:
Deep Active Learning over the Long Tail. CoRR abs/1711.00941 (2017) - 2016
- [c34]Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Binarized Neural Networks. NIPS 2016: 4107-4115 - [c33]Omer Katz, Ran El-Yaniv, Eran Yahav:
Estimating types in binaries using predictive modeling. POPL 2016: 313-326 - [i14]Guy Uziel, Ran El-Yaniv:
Online Learning of Portfolio Ensembles with Sector Exposure Regularization. CoRR abs/1604.03266 (2016) - [i13]Guy Uziel, Ran El-Yaniv:
Online Learning of Commission Avoidant Portfolio Ensembles. CoRR abs/1605.00788 (2016) - [i12]Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio:
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. CoRR abs/1609.07061 (2016) - [i11]Bar Hilleli, Ran El-Yaniv:
Deep Learning of Robotic Tasks using Strong and Weak Human Supervision. CoRR abs/1612.01086 (2016) - 2015
- [j30]Yair Wiener, Ran El-Yaniv:
Agnostic Pointwise-Competitive Selective Classification. J. Artif. Intell. Res. 52: 171-201 (2015) - [j29]Yair Wiener, Steve Hanneke, Ran El-Yaniv:
A compression technique for analyzing disagreement-based active learning. J. Mach. Learn. Res. 16: 713-745 (2015) - [i10]Noam Segev, Maayan Harel, Shie Mannor, Koby Crammer, Ran El-Yaniv:
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. CoRR abs/1511.01258 (2015) - 2014
- [c32]Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer:
Concept Drift Detection Through Resampling. ICML 2014: 1009-1017 - [i9]Ran El-Yaniv, Dmitry Pechyony:
Transductive Rademacher Complexity and its Applications. CoRR abs/1401.3441 (2014) - [i8]Yair Wiener, Steve Hanneke, Ran El-Yaniv:
A Compression Technique for Analyzing Disagreement-Based Active Learning. CoRR abs/1404.1504 (2014) - 2013
- [i7]Ran El-Yaniv, David Yanay:
Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness. CoRR abs/1311.2252 (2013) - 2012
- [j28]Ran El-Yaniv, Yair Wiener:
Active Learning via Perfect Selective Classification. J. Mach. Learn. Res. 13: 255-279 (2012) - [c31]Yair Wiener, Ran El-Yaniv:
Pointwise Tracking the Optimal Regression Function. NIPS 2012: 2051-2059 - [c30]Ran El-Yaniv, David Yanay:
Supervised Learning of Semantic Relatedness. ECML/PKDD (1) 2012: 744-759 - [i6]Ran El-Yaniv, Alexandra Faynburd:
Autoregressive short-term prediction of turning points using support vector regression. CoRR abs/1209.0127 (2012) - 2011
- [c29]Dmitry Pidan, Ran El-Yaniv:
Selective Prediction of Financial Trends with Hidden Markov Models. NIPS 2011: 855-863 - [c28]Yair Wiener, Ran El-Yaniv:
Agnostic Selective Classification. NIPS 2011: 1665-1673 - [i5]Allan Borodin, Ran El-Yaniv, Vincent Gogan:
Can We Learn to Beat the Best Stock. CoRR abs/1107.0036 (2011) - [i4]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. CoRR abs/1107.0046 (2011) - [i3]Ron Begleiter, Ran El-Yaniv, Golan Yona:
On Prediction Using Variable Order Markov Models. CoRR abs/1107.0051 (2011) - 2010
- [j27]Ran El-Yaniv, Yair Wiener:
On the Foundations of Noise-free Selective Classification. J. Mach. Learn. Res. 11: 1605-1641 (2010) - [i2]Ran El-Yaniv, Noam Etzion-Rosenberg:
Hierarchical Multiclass Decompositions with Application to Authorship Determination. CoRR abs/1010.2102 (2010) - [i1]Ran El-Yaniv, Mordechai Nisenson:
On the Foundations of Adversarial Single-Class Classification. CoRR abs/1010.4466 (2010)
2000 – 2009
- 2009
- [j26]Ran El-Yaniv, Dmitry Pechyony:
Transductive Rademacher Complexity and its Applications. J. Artif. Intell. Res. 35: 193-234 (2009) - 2008
- [j25]Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik:
Large margin vs. large volume in transductive learning. Mach. Learn. 72(3): 173-188 (2008) - [j24]Ron Begleiter, Ran El-Yaniv, Dmitry Pechyony:
Repairing self-confident active-transductive learners using systematic exploration. Pattern Recognit. Lett. 29(9): 1245-1251 (2008) - [j23]Ran El-Yaniv, Dmitry Pechyony, Elad Yom-Tov
Better multiclass classification via a margin-optimized single binary problem. Pattern Recognit. Lett. 29(14): 1954-1959 (2008) - [c27]Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik:
Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) 2008: 9-10 - 2007
- [j22]Igor Zingman, Ron Meir, Ran El-Yaniv:
Size-density spectra and their application to image classification. Pattern Recognit. 40(12): 3336-3348 (2007) - [c26]Ran El-Yaniv, Dmitry Pechyony:
Transductive Rademacher Complexity and Its Applications. COLT 2007: 157-171 - 2006
- [j21]Ron Begleiter, Ran El-Yaniv:
Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition. J. Mach. Learn. Res. 7: 379-411 (2006) - [c25]Ran El-Yaniv, Dmitry Pechyony:
Stable Transductive Learning. COLT 2006: 35-49 - [c24]Ran El-Yaniv, Mordechai Nisenson:
Optimal Single-Class Classification Strategies. NIPS 2006: 377-384 - 2005
- [j20]Itai Sharon
Correcting BLAST e-Values for Low-Complexity Segments. J. Comput. Biol. 12(7): 980-1003 (2005) - [j19]Ran El-Yaniv, Leonid Gerzon:
Effective transductive learning via objective model selection. Pattern Recognit. Lett. 26(13): 2104-2115 (2005) - [c23]Ron Bekkerman, Ran El-Yaniv, Andrew McCallum:
Multi-way distributional clustering via pairwise interactions. ICML 2005: 41-48 - 2004
- [j18]Allan Borodin, Ran El-Yaniv, Vincent Gogan:
Can We Learn to Beat the Best Stock. J. Artif. Intell. Res. 21: 579-594 (2004) - [j17]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. J. Artif. Intell. Res. 22: 117-142 (2004) - [j16]Ron Begleiter, Ran El-Yaniv, Golan Yona:
On Prediction Using Variable Order Markov Models. J. Artif. Intell. Res. 22: 385-421 (2004) - [j15]Yoram Baram, Ran El-Yaniv, Kobi Luz:
Online Choice of Active Learning Algorithms. J. Mach. Learn. Res. 5: 255-291 (2004) - [j14]Rani Yaroshinsky, Ran El-Yaniv, Steven S. Seiden:
How to Better Use Expert Advice. Mach. Learn. 55(3): 271-309 (2004) - 2003
- [j13]Michael Ben-Or
Resilient-optimal interactive consistency in constant time. Distributed Comput. 16(4): 249-262 (2003) - [j12]Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, Yoad Winter:
Distributional Word Clusters vs. Words for Text Categorization. J. Mach. Learn. Res. 3: 1183-1208 (2003) - [c22]Yoram Baram, Ran El-Yaniv, Kobi Luz:
Online Choice of Active Learning Algorithms. ICML 2003: 19-26 - [c21]Allan Borodin, Ran El-Yaniv, Vincent Gogan:
Can We Learn to Beat the Best Stock. NIPS 2003: 345-352 - [c20]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Error Bounds for Transductive Learning via Compression and Clustering. NIPS 2003: 1085-1092 - [c19]Mordechai Nisenson, Ido Yariv, Ran El-Yaniv, Ron Meir:
Towards Behaviometric Security Systems: Learning to Identify a Typist. PKDD 2003: 363-374 - 2002
- [j11]Ran Bachrach
On the Competitive Theory and Practice of Online List Accessing Algorithms. Algorithmica 32(2): 201-245 (2002) - [j10]Shlomo Dubnov
Synthesizing Sound Textures through Wavelet Tree Learning. IEEE Computer Graphics and Applications 22(4): 38-48 (2002) - [j9]Ziv Nevo, Ran El-Yaniv:
On Online Learning of Decision Lists. J. Mach. Learn. Res. 3: 271-301 (2002) - [j8]Shlomo Dubnov
A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles. Mach. Learn. 47(1): 35-61 (2002) - [c18]Philip Derbeko, Ran El-Yaniv, Ron Meir:
Variance Optimized Bagging. ECML 2002: 60-71 - 2001
- [j7]Ran El-Yaniv, Amos Fiat, Richard M. Karp, G. Turpin:
Optimal Search and One-Way Trading Online Algorithms. Algorithmica 30(1): 101-139 (2001) - [j6]Ziv Bar-Joseph
Texture Mixing and Texture Movie Synthesis Using Statistical Learning. IEEE Trans. Vis. Comput. Graph. 7(2): 120-135 (2001) - [c17]Ran El-Yaniv, Oren Souroujon:
Iterative Double Clustering for Unsupervised and Semi-supervised Learning. ECML 2001: 121-132 - [c16]Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram:
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. ICML 2001: 521-528 - [c15]Ran El-Yaniv, Oren Souroujon:
Iterative Double Clustering for Unsupervised and Semi-Supervised Learning. NIPS 2001: 1025-1032 - [c14]Ron Bekkerman, Ran El-Yaniv, Yoad Winter, Naftali Tishby:
On Feature Distributional Clustering for Text Categorization. SIGIR 2001: 146-153 - 2000
- [c13]Ron Meir, Ran El-Yaniv, Shai Ben-David:
Localized Boosting. COLT 2000: 190-199 - [c12]Allan Borodin, Ran El-Yaniv, Vincent Gogan:
On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). LATIN 2000: 173-196
1990 – 1999
- 1999
- [j5]Ran El-Yaniv, Ron Kaniel, Nathan Linial:
Competitive Optimal On-Line Leasing. Algorithmica 25(1): 116-140 (1999) - [j4]Allan Borodin, Ran El-Yaniv:
On Randomization in On-Line Computation. Inf. Comput. 150(2): 244-267 (1999) - [c11]Ziv Bar-Joseph, Dani Lischinski, Michael Werman, Shlomo Dubnov, Ran El-Yaniv:
Granular Synthesis of Sound Textures Using Statistical Learning. ICMC 1999 - 1998
- [b2]Allan Borodin, Ran El-Yaniv:
Online computation and competitive analysis. Cambridge University Press 1998, ISBN 978-0-521-56392-5, pp. I-XVIII, 1-414 - [j3]Ran El-Yaniv:
Competitive Solutions for Online Financial Prob. ACM Comput. Surv. 30(1): 28-69 (1998) - [c10]Shlomo Dubnov, Gérard Assayag, Ran El-Yaniv:
Universal Classification Applied to Musical Sequences. ICMC 1998 - 1997
- [j2]Ran El-Yaniv, Richard M. Karp:
Nearly Optimal Competitive Online Replacement Policies. Math. Oper. Res. 22(4): 814-839 (1997) - [c9]Allan Borodin, Ran El-Yaniv:
On Ranomization in Online Computation. CCC 1997: 226-238 - [c8]Ran El-Yaniv, Shai Fine, Naftali Tishby:
Agnostic Classification of Markovian Sequences. NIPS 1997: 465-471 - [c7]Ran Bachrach, Ran El-Yaniv:
Online List Accessing Algorithms and Their Applications: Recent Empirical Evidence. SODA 1997: 53-62 - 1996
- [c6]Ran El-Yaniv:
Competitive Solutions for On-line Financial Problems. Online Algorithms 1996: 326-372 - 1995
- [j1]Ran El-Yaniv, Jon M. Kleinberg:
Geometric Two-Server Algorithms. Inf. Process. Lett. 53(6): 355-358 (1995) - [c5]Andrew Chou, Jeremy R. Cooperstock, Ran El-Yaniv, Michael Klugerman, Frank Thomson Leighton:
The Statistical Adversary Allows Optimal Money-Making Trading Strategies. SODA 1995: 467-476 - 1994
- [b1]Ran El-Yaniv:
On-line algorithms and financial decision making. University of Toronto, Canada, 1994 - 1993
- [c4]Ran El-Yaniv, Richard M. Karp:
The Mortgage Problem. ISTCS 1993: 304-312 - 1992
- [c3]Ran El-Yaniv, Amos Fiat, Richard M. Karp, G. Turpin:
Competitive Analysis of Financial Games. FOCS 1992: 327-333 - [c2]Efthymios Anagnostou, Ran El-Yaniv, Vassos Hadzilacos:
Memory Adaptive Self-Stabilizing Protocols (Extended Abstract). WDAG 1992: 203-220 - 1991
- [c1]Efthymios Anagnostou, Ran El-Yaniv:
More on the Power of Random Walks: Uniform Self-Stabilizing Randomized Algorithms (Preliminary Report). WDAG 1991: 31-51
Coauthor Index
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