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

Margin Analysis of the LVQ Algorithm. - Koby Crammer; Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (NIPS, 2002)

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

Query by committee, linear separation and random walks. - Shai Fine; Ran Gilad-Bachrach; Eli Shamir 001 (Theor. Comput. Sci., 2002)

An Information Theoretic Tradeoff between Complexity and Accuracy. - Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (COLT, 2003)

Bayes and Tukey Meet at the Center Point. - Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (COLT, 2004)

Margin based feature selection - theory and algorithms. - Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (ICML, 2004)

Query by Committee Made Real. - Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (NIPS, 2005)

Is Feature Selection Still Necessary? - Amir Navot; Ran Gilad-Bachrach; Yiftah Navot; Naftali Tishby (SLSFS, 2005)

Margin Based Feature Selection and Infogain with Standard Classifiers. - Ran Gilad-Bachrach; Amir Navot (Feature Extraction, 2006)

Large Margin Principles for Feature Selection. - Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (Feature Extraction, 2006)

To PAC and beyond (למידה מונחית ומעבר לה.; Probably approximately correct learning.). - Ran Gilad-Bachrach (2006)

Workstation capacity tuning using reinforcement learning. - Aharon Bar-Hillel; Amir Di-Nur; Liat Ein-Dor; Ran Gilad-Bachrach; Yossi Ittach (SC, 2007)

Efficient human computation: the distributed labeling problem. - Ran Gilad-Bachrach; Aharon Bar-Hillel; Liat Ein-Dor (KDD Workshop on Human Computation, 2009)

Efficient Human Computation - Ran Gilad-Bachrach; Aharon Bar-Hillel; Liat Ein-Dor (CoRR, 2009)

Robust Distributed Online Prediction - Ofer Dekel; Ran Gilad-Bachrach; Ohad Shamir; Lin Xiao (CoRR, 2010)

Optimal Distributed Online Prediction. - Ofer Dekel; Ran Gilad-Bachrach; Ohad Shamir; Lin Xiao (ICML, 2011)

On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. - Ashok Kumar Ponnuswami; Kumaresh Pattabiraman; Qiang Wu; Ran Gilad-Bachrach; Tapas Kanungo (WSDM, 2011)

Learning from mistakes: towards a correctable learning algorithm. - Karthik Raman 001; Krysta M. Svore; Ran Gilad-Bachrach; Christopher J. C. Burges (CIKM, 2012)

Latent fault detection in large scale services. - Moshe Gabel; Assaf Schuster; Ran Gilad-Bachrach; Nikolaj S. Bjørner (DSN, 2012)

Full body gait analysis with Kinect. - Moshe Gabel; Ran Gilad-Bachrach; Erin Renshaw; Assaf Schuster (EMBC, 2012)

Confined intra-arm communication for medical applications. - Trang Thai; Gerald DeJean; Ran Gilad-Bachrach (Wireless Health, 2012)

Optimal Distributed Online Prediction Using Mini-Batches. - Ofer Dekel; Ran Gilad-Bachrach; Ohad Shamir; Lin Xiao (J. Mach. Learn. Res., 2012)

The Median Hypothesis. - Ran Gilad-Bachrach; Christopher J. C. Burges (Empirical Inference, 2013)

Using multiple samples to learn mixture models. - Jason D. Lee; Ran Gilad-Bachrach; Rich Caruana (NIPS, 2013)

Classifier selection using the predicate depth. - Ran Gilad-Bachrach; Christopher J. C. Burges (J. Mach. Learn. Res., 2013)

PopTherapy: coping with stress through pop-culture. - Pablo Paredes; Ran Gilad-Bachrach; Mary Czerwinski; Asta Roseway; Kael Rowan; Javier Hernandez (PervasiveHealth, 2014)

Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces. - Yoram Bachrach; Yehuda Finkelstein; Ran Gilad-Bachrach; Liran Katzir 001; Noam Koenigstein; Nir Nice; Ulrich Paquet (RecSys, 2014)

Crypto-Nets: Neural Networks over Encrypted Data. - Pengtao Xie; Misha Bilenko; Tom Finley; Ran Gilad-Bachrach; Kristin E. Lauter; Michael Naehrig (CoRR, 2014)

DART: Dropouts meet Multiple Additive Regression Trees. - Korlakai Vinayak Rashmi; Ran Gilad-Bachrach (AISTATS, 2015)

Designing Social and Emotional Skills Training: The Challenges and Opportunities for Technology Support. - Petr Slovák; Ran Gilad-Bachrach; Geraldine Fitzpatrick (CHI, 2015)

Scaffolding the scaffolding: Supporting children¿s social-emotional learning at home. - Petr Slovák; Christopher Frauenberger; Ran Gilad-Bachrach; Mia Doces; Brian Smith; Rachel Kamb; Kael Rowan; Geraldine Fitzpatrick (CSCW, 2016)

Predicting "About-to-Eat" Moments for Just-in-Time Eating Intervention. - Tauhidur Rahman; Mary Czerwinski; Ran Gilad-Bachrach; Paul Johns (Digital Health, 2016)

Challenges for designing notifications for affective computing systems. - Mary Czerwinski; Ran Gilad-Bachrach; Shamsi T. Iqbal; Gloria Mark (UbiComp Adjunct, 2016)

CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. - Ran Gilad-Bachrach; Nathan Dowlin; Kim Laine; Kristin E. Lauter; Michael Naehrig; John Wernsing (ICML, 2016)

Secure Data Exchange: A Marketplace in the Cloud. - Ran Gilad-Bachrach; Kim Laine; Kristin E. Lauter; Peter Rindal; Mike Rosulek (IACR Cryptol. ePrint Arch., 2016)

Smooth Sensitivity Based Approach for Differentially Private Principal Component Analysis. - Alon Gonen; Ran Gilad-Bachrach (CoRR, 2017)

Private Collaborative Neural Network Learning. - Melissa Chase; Ran Gilad-Bachrach; Kim Laine; Kristin E. Lauter; Peter Rindal (IACR Cryptol. ePrint Arch., 2017)

Manual for Using Homomorphic Encryption for Bioinformatics. - Nathan Dowlin; Ran Gilad-Bachrach; Kim Laine; Kristin E. Lauter; Michael Naehrig; John Wernsing (Proc. IEEE, 2017)

Smooth Sensitivity Based Approach for Differentially Private PCA. - Alon Gonem; Ran Gilad-Bachrach (ALT, 2018)

Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. - John Moore; Joel Pfeiffer; Kai Wei; Rishabh K. Iyer; Denis Charles; Ran Gilad-Bachrach; Levi Boyles; Eren Manavoglu (CoRR, 2018)

Turning Lemons into Peaches using Secure Computation. - Stav Buchsbaum; Ran Gilad-Bachrach; Yehuda Lindell (CoRR, 2018)

Logistic regression over encrypted data from fully homomorphic encryption. - Hao Chen 030; Ran Gilad-Bachrach; Kyoohyung Han; Zhicong Huang; Amir Jalali; Kim Laine; Kristin E. Lauter (IACR Cryptol. ePrint Arch., 2018)

Staying in the Zone: Sequencing Content in Classrooms Based on the Zone of Proximal Development. - Oded Vainas; Yossi Ben David; Ran Gilad-Bachrach; Meitar Ronen; Ori Bar-Ilan; Roi Shillo; Galit Lukin; Daniel Sitton (EDM, 2019)

Low Latency Privacy Preserving Inference. - Alon Brutzkus; Ran Gilad-Bachrach; Oren Elisha (ICML, 2019)

E-Gotsky: Sequencing Content using the Zone of Proximal Development. - Oded Vainas; Ori Bar-Ilan; Yossi Ben David; Ran Gilad-Bachrach; Galit Lukin; Meitar Ronen; Roi Shillo; Daniel Sitton (CoRR, 2019)

The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance. - Brit Youngmann; Elad Yom-Tov; Ran Gilad-Bachrach; Danny Karmon (WWW, 2020)

Marginal Contribution Feature Importance - an Axiomatic Approach for The Natural Case. - Amnon Catav; Boyang Fu; Jason Ernst; Sriram Sankararaman; Ran Gilad-Bachrach (CoRR, 2020)

Robust Model Compression Using Deep Hypotheses. - Omri Armstrong; Ran Gilad-Bachrach (AAAI, 2021)

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. - Amnon Catav; Boyang Fu; Yazeed Zoabi; Ahuva Weiss-Meilik; Noam Shomron; Jason Ernst; Sriram Sankararaman; Ran Gilad-Bachrach (ICML, 2021)

Trees with Attention for Set Prediction Tasks. - Roy Hirsch; Ran Gilad-Bachrach (ICML, 2021)

Break your Bandit Routine with LSD Rewards: a Last Switch Dependent Analysis of Satiation and Seasonality. - Pierre Laforgue; Giulia Clerici; Nicolò Cesa-Bianchi; Ran Gilad-Bachrach (CoRR, 2021)

PyBryt: auto-assessment and auto-grading for computational thinking. - Christopher Pyles; François van Schalkwyk; Gerard J. Gorman; Marijan Beg; Lee Stott; Nir Levy; Ran Gilad-Bachrach (CoRR, 2021)

Algorithmic copywriting: automated generation of health-related advertisements to improve their performance. - Brit Youngmann; Elad Yom-Tov; Ran Gilad-Bachrach; Danny Karmon (Inf. Retr. J., 2021)

A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. - Pierre Laforgue; Giulia Clerici; Nicolò Cesa-Bianchi; Ran Gilad-Bachrach (AISTATS, 2022)

Inherent Inconsistencies of Feature Importance. - Nimrod Harel; Ran Gilad-Bachrach; Uri Obolski (CoRR, 2022)

Graph Trees with Attention. - Maya Bechler-Speicher; Amir Globerson; Ran Gilad-Bachrach (CoRR, 2022)

Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. - Yonatan E. Brand; Dafna Schwartz; Eran Gazit; Aron S. Buchman; Ran Gilad-Bachrach; Jeffrey M. Hausdorff (Sensors, 2022)

The Case Against Explainability. - Hofit Wasserman Rozen; Niva Elkin-Koren; Ran Gilad-Bachrach (CoRR, 2023)

TREE-G: Decision Trees Contesting Graph Neural Networks. - Maya Bechler-Speicher; Amir Globerson; Ran Gilad-Bachrach (AAAI, 2024)

Graph Neural Networks Use Graphs When They Shouldn't. - Maya Bechler-Speicher; Ido Amos; Ran Gilad-Bachrach; Amir Globerson (ICML, 2024)

The Intelligible and Effective Graph Neural Additive Network. - Maya Bechler-Speicher; Amir Globerson; Ran Gilad-Bachrach (NeurIPS, 2024)

Tighter Bounds on the Information Bottleneck with Application to Deep Learning. - Nir Weingarten; Zohar Yakhini; Moshe Butman; Ran Gilad-Bachrach (CoRR, 2024)

The Intelligible and Effective Graph Neural Additive Networks. - Maya Bechler-Speicher; Amir Globerson; Ran Gilad-Bachrach (CoRR, 2024)

Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study. - Chaya Ben Yehuda; Ran Gilad-Bachrach; Yarin Udi (CoRR, 2024)

On the Utilization of Unique Node Identifiers in Graph Neural Networks. - Maya Bechler-Speicher; Moshe Eliasof; Carola-Bibiane Schönlieb; Ran Gilad-Bachrach; Amir Globerson (CoRR, 2024)

Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model. - Dafna Schwartz; Lori Quinn; Nora E. Fritz; Lisa M. Muratori; Jeffrey M. Hausdorff; Ran Gilad-Bachrach (CoRR, 2024)

Towards Invariance to Node Identifiers in Graph Neural Networks. - Maya Bechler-Speicher; Moshe Eliasof; Carola-Bibiane Schönlieb; Ran Gilad-Bachrach; Amir Globerson (CoRR, 2025)

Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers. - Gilad Yehudai; Clayton Sanford; Maya Bechler-Speicher; Orr Fischer; Ran Gilad-Bachrach; Amir Globerson (CoRR, 2025)

Interpretable Graph Learning Over Sets of Temporally-Sparse Data. - Andrea Zerio; Maya Bechler-Speicher; Maor Huri; Marie Vibeke Vestergaard; Ran Gilad-Bachrach; Tine Jess; Samir Bhatt; Aleksejs Sazonovs (CoRR, 2025)

Medical Data Pecking: A Context-Aware Approach for Automated Quality Evaluation of Structured Medical Data. - Irena Girshovitz; Atai Ambus; Moni Shahar; Ran Gilad-Bachrach (CoRR, 2025)