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ORCIDRan Gilad-Bachrach
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- affiliation: Microsoft Research Redmond, WA, USA
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2020 – today
- 2024
[c37]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
:
TREE-G: Decision Trees Contesting Graph Neural Networks. AAAI 2024: 11032-11042- [c36]Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson:
Graph Neural Networks Use Graphs When They Shouldn't. ICML 2024 - [i26]Nir Weingarten, Zohar Yakhini, Moshe Butman, Ran Gilad-Bachrach:
Tighter Bounds on the Information Bottleneck with Application to Deep Learning. CoRR abs/2402.07639 (2024) - [i25]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach:
The Intelligible and Effective Graph Neural Additive Networks. CoRR abs/2406.01317 (2024) - [i24]Chaya Ben Yehuda, Ran Gilad-Bachrach, Yarin Udi:
Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study. CoRR abs/2407.11612 (2024) - 2023
- [i23]Hofit Wasserman Rozen, Niva Elkin-Koren, Ran Gilad-Bachrach:
The Case Against Explainability. CoRR abs/2305.12167 (2023) - [i22]Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson:
Graph Neural Networks Use Graphs When They Shouldn't. CoRR abs/2309.04332 (2023) - 2022
- [j7]Yonatan E. Brand, Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach, Jeffrey M. Hausdorff:
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. Sensors 22(18): 7094 (2022) - [c35]Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach:
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. AISTATS 2022: 971-990 - [i21]Nimrod Harel, Ran Gilad-Bachrach, Uri Obolski:
Inherent Inconsistencies of Feature Importance. CoRR abs/2206.08204 (2022) - [i20]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach:
Graph Trees with Attention. CoRR abs/2207.02760 (2022) - 2021
- [j6]Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon:
Algorithmic copywriting: automated generation of health-related advertisements to improve their performance. Inf. Retr. J. 24(3): 205-239 (2021) - [c34]Omri Armstrong, Ran Gilad-Bachrach:
Robust Model Compression Using Deep Hypotheses. AAAI 2021: 6688-6695 - [c33]Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss-Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach:
Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. ICML 2021: 1324-1335 - [c32]Roy Hirsch, Ran Gilad-Bachrach:
Trees with Attention for Set Prediction Tasks. ICML 2021: 4250-4261 - [i19]Omri Armstrong, Ran Gilad-Bachrach:
Robust Model Compression Using Deep Hypotheses. CoRR abs/2103.07668 (2021) - [i18]Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach:
Break your Bandit Routine with LSD Rewards: a Last Switch Dependent Analysis of Satiation and Seasonality. CoRR abs/2110.11819 (2021) - [i17]Christopher Pyles, François van Schalkwyk, Gerard J. Gorman, Marijan Beg, Lee Stott, Nir Levy, Ran Gilad-Bachrach:
PyBryt: auto-assessment and auto-grading for computational thinking. CoRR abs/2112.02144 (2021) - 2020
- [c31]Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon:
The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance. WWW 2020: 1366-1377 - [i16]Amnon Catav, Boyang Fu, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach:
Marginal Contribution Feature Importance - an Axiomatic Approach for The Natural Case. CoRR abs/2010.07910 (2020)
2010 – 2019
- 2019
- [c30]Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal, Mike Rosulek:
Secure Data Exchange: A Marketplace in the Cloud. CCSW 2019: 117-128 - [c29]Oded Vainas, Yossi Ben David, Ran Gilad-Bachrach, Meitar Ronen, Ori Bar-Ilan, Roi Shillo, Galit Lukin, Daniel Sitton:
Staying in the Zone: Sequencing Content in Classrooms Based on the Zone of Proximal Development. EDM 2019 - [c28]Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha:
Low Latency Privacy Preserving Inference. ICML 2019: 812-821 - [i15]Oded Vainas, Ori Bar-Ilan, Yossi Ben David, Ran Gilad-Bachrach, Galit Lukin, Meitar Ronen, Roi Shillo, Daniel Sitton:
E-Gotsky: Sequencing Content using the Zone of Proximal Development. CoRR abs/1904.12268 (2019) - [i14]Brit Youngmann, Ran Gilad-Bachrach, Danny Karmon, Elad Yom-Tov:
The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance. CoRR abs/1910.12274 (2019) - 2018
- [c27]Alon Gonem, Ran Gilad-Bachrach:
Smooth Sensitivity Based Approach for Differentially Private PCA. ALT 2018: 438-450 - [i13]John Moore, Joel Pfeiffer, Kai Wei, Rishabh K. Iyer, Denis Charles, Ran Gilad-Bachrach, Levi Boyles, Eren Manavoglu:
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. CoRR abs/1804.06909 (2018) - [i12]Stav Buchsbaum, Ran Gilad-Bachrach, Yehuda Lindell:
Turning Lemons into Peaches using Secure Computation. CoRR abs/1810.02066 (2018) - [i11]Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach:
Low Latency Privacy Preserving Inference. CoRR abs/1812.10659 (2018) - [i10]Hao Chen, Ran Gilad-Bachrach, Kyoohyung Han, Zhicong Huang, Amir Jalali, Kim Laine, Kristin E. Lauter:
Logistic regression over encrypted data from fully homomorphic encryption. IACR Cryptol. ePrint Arch. 2018: 462 (2018) - 2017
- [j5]Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing:
Manual for Using Homomorphic Encryption for Bioinformatics. Proc. IEEE 105(3): 552-567 (2017) - [i9]Alon Gonen, Ran Gilad-Bachrach:
Smooth Sensitivity Based Approach for Differentially Private Principal Component Analysis. CoRR abs/1710.10556 (2017) - [i8]Melissa Chase, Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal:
Private Collaborative Neural Network Learning. IACR Cryptol. ePrint Arch. 2017: 762 (2017) - 2016
- [c26]Petr Slovák, Christopher Frauenberger, Ran Gilad-Bachrach, Mia Doces, Brian Smith, Rachel Kamb, Kael Rowan, Geraldine Fitzpatrick:
Scaffolding the scaffolding: Supporting children¿s social-emotional learning at home. CSCW 2016: 1749-1763 - [c25]Tauhidur Rahman, Mary Czerwinski, Ran Gilad-Bachrach, Paul Johns:
Predicting "About-to-Eat" Moments for Just-in-Time Eating Intervention. Digital Health 2016: 141-150 - [c24]Mary Czerwinski, Ran Gilad-Bachrach, Shamsi T. Iqbal, Gloria Mark:
Challenges for designing notifications for affective computing systems. UbiComp Adjunct 2016: 1554-1559 - [c23]Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing:
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. ICML 2016: 201-210 - [i7]Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal, Mike Rosulek:
Secure Data Exchange: A Marketplace in the Cloud. IACR Cryptol. ePrint Arch. 2016: 620 (2016) - 2015
- [c22]Korlakai Vinayak Rashmi, Ran Gilad-Bachrach:
DART: Dropouts meet Multiple Additive Regression Trees. AISTATS 2015 - [c21]Petr Slovák, Ran Gilad-Bachrach, Geraldine Fitzpatrick:
Designing Social and Emotional Skills Training: The Challenges and Opportunities for Technology Support. CHI 2015: 2797-2800 - [i6]K. V. Rashmi, Ran Gilad-Bachrach:
DART: Dropouts meet Multiple Additive Regression Trees. CoRR abs/1505.01866 (2015) - 2014
- [c20]Pablo Paredes, Ran Gilad-Bachrach, Mary Czerwinski, Asta Roseway, Kael Rowan, Javier Hernandez:
PopTherapy: coping with stress through pop-culture. PervasiveHealth 2014: 109-117 - [c19]Yoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach, Liran Katzir, Noam Koenigstein, Nir Nice, Ulrich Paquet:
Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces. RecSys 2014: 257-264 - [i5]Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin E. Lauter, Michael Naehrig:
Crypto-Nets: Neural Networks over Encrypted Data. CoRR abs/1412.6181 (2014) - 2013
- [j4]Ran Gilad-Bachrach, Christopher J. C. Burges:
Classifier selection using the predicate depth. J. Mach. Learn. Res. 14(1): 3591-3618 (2013) - [c18]Ran Gilad-Bachrach, Christopher J. C. Burges:
The Median Hypothesis. Empirical Inference 2013: 161-175 - [c17]Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana:
Using multiple samples to learn mixture models. NIPS 2013: 324-332 - [i4]Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana:
Using Multiple Samples to Learn Mixture Models. CoRR abs/1311.7184 (2013) - 2012
- [j3]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction Using Mini-Batches. J. Mach. Learn. Res. 13: 165-202 (2012) - [c16]Karthik Raman, Krysta M. Svore, Ran Gilad-Bachrach, Christopher J. C. Burges:
Learning from mistakes: towards a correctable learning algorithm. CIKM 2012: 1930-1934 - [c15]Moshe Gabel, Assaf Schuster, Ran Gilad-Bachrach, Nikolaj S. Bjørner:
Latent fault detection in large scale services. DSN 2012: 1-12 - [c14]Moshe Gabel, Ran Gilad-Bachrach, Erin Renshaw, Assaf Schuster:
Full body gait analysis with Kinect. EMBC 2012: 1964-1967 - [c13]Trang Thai, Gerald DeJean, Ran Gilad-Bachrach:
Confined intra-arm communication for medical applications. Wireless Health 2012: 18:1-18:2 - 2011
- [c12]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction. ICML 2011: 713-720 - [c11]Ashok Kumar Ponnuswami, Kumaresh Pattabiraman, Qiang Wu, Ran Gilad-Bachrach, Tapas Kanungo:
On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. WSDM 2011: 715-724 - 2010
- [i3]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction using Mini-Batches. CoRR abs/1012.1367 (2010) - [i2]Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Robust Distributed Online Prediction. CoRR abs/1012.1370 (2010)
2000 – 2009
- 2009
- [c10]Ran Gilad-Bachrach, Aharon Bar-Hillel, Liat Ein-Dor:
Efficient human computation: the distributed labeling problem. KDD Workshop on Human Computation 2009: 70-76 - [i1]Ran Gilad-Bachrach, Aharon Bar-Hillel, Liat Ein-Dor:
Efficient Human Computation. CoRR abs/0903.1125 (2009) - 2007
- [c9]Aharon Bar-Hillel, Amir Di-Nur, Liat Ein-Dor, Ran Gilad-Bachrach, Yossi Ittach:
Workstation capacity tuning using reinforcement learning. SC 2007: 32 - 2006
- [b1]Ran Gilad-Bachrach:
To PAC and beyond (למידה מונחית ומעבר לה.; Probably approximately correct learning.). Hebrew University of Jerusalem, Israel, 2006 - [p2]Ran Gilad-Bachrach, Amir Navot:
Margin Based Feature Selection and Infogain with Standard Classifiers. Feature Extraction 2006: 395-401 - [p1]Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Large Margin Principles for Feature Selection. Feature Extraction 2006: 585-606 - 2005
- [c8]Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Query by Committee Made Real. NIPS 2005: 443-450 - [c7]Amir Navot, Ran Gilad-Bachrach, Yiftah Navot, Naftali Tishby:
Is Feature Selection Still Necessary? SLSFS 2005: 127-138 - 2004
- [c6]Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Bayes and Tukey Meet at the Center Point. COLT 2004: 549-563 - [c5]Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Margin based feature selection - theory and algorithms. ICML 2004 - 2003
- [c4]Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
An Information Theoretic Tradeoff between Complexity and Accuracy. COLT 2003: 595-609 - 2002
- [j2]Ran Bachrach, Ran El-Yaniv, M. Reinstadtler:
On the Competitive Theory and Practice of Online List Accessing Algorithms. Algorithmica 32(2): 201-245 (2002) - [j1]Shai Fine, Ran Gilad-Bachrach, Eli Shamir:
Query by committee, linear separation and random walks. Theor. Comput. Sci. 284(1): 25-51 (2002) - [c3]Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Margin Analysis of the LVQ Algorithm. NIPS 2002: 462-469
1990 – 1999
- 1999
- [c2]Ran Bachrach, Shai Fine, Eli Shamir:
Query by Committee, Linear Separation and Random Walks. EuroCOLT 1999: 34-49 - 1997
- [c1]Ran Bachrach, Ran El-Yaniv:
Online List Accessing Algorithms and Their Applications: Recent Empirical Evidence. SODA 1997: 53-62
Coauthor Index
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