Ran Gilad-Bachrach

78 papers A* 14A 8B 2Journal 39Unranked 12

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers. CoRR Gilad Yehudai, Clayton Sanford, Maya Bechler-Speicher, Orr Fischer, Ran Gilad-Bachrach, Amir Globerson
2025	J	`jnl`	Graph Mixing Additive Networks. CoRR Maya Bechler-Speicher, Andrea Zerio, Maor Huri, Marie Vibeke Vestergaard, Ran Gilad-Bachrach, Tine Jess, Samir Bhatt, Aleksejs Sazonovs
2025	J	`jnl`	Interpretable Graph Learning Over Sets of Temporally-Sparse Data. CoRR Andrea Zerio, Maya Bechler-Speicher, Maor Huri, Marie Vibeke Vestergaard, Ran Gilad-Bachrach, Tine Jess, Samir Bhatt, Aleksejs Sazonovs
2025	J	`jnl`	Medical Data Pecking: A Context-Aware Approach for Automated Quality Evaluation of Structured Medical Data. CoRR Irena Girshovitz, Atai Ambus, Moni Shahar, Ran Gilad-Bachrach
2025	J	`jnl`	Towards Invariance to Node Identifiers in Graph Neural Networks. CoRR Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb, Ran Gilad-Bachrach, Amir Globerson
2024	J	`jnl`	Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model. CoRR Dafna Schwartz, Lori Quinn, Nora E. Fritz, Lisa M. Muratori, Jeffrey M. Hausdorff, Ran Gilad-Bachrach
2024	A*	`conf`	Graph Neural Networks Use Graphs When They Shouldn't. ICML Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson
2024	J	`jnl`	Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study. CoRR Chaya Ben Yehuda, Ran Gilad-Bachrach, Yarin Udi
2024	J	`jnl`	On the Utilization of Unique Node Identifiers in Graph Neural Networks. CoRR Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb, Ran Gilad-Bachrach, Amir Globerson
2024	A*	`conf`	TREE-G: Decision Trees Contesting Graph Neural Networks. AAAI Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
2024	A*	`conf`	The Intelligible and Effective Graph Neural Additive Network. NeurIPS Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
2024	J	`jnl`	The Intelligible and Effective Graph Neural Additive Networks. CoRR Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
2024	J	`jnl`	Tighter Bounds on the Information Bottleneck with Application to Deep Learning. CoRR Nir Weingarten, Zohar Yakhini, Moshe Butman, Ran Gilad-Bachrach
2023	J	`jnl`	Graph Neural Networks Use Graphs When They Shouldn't. CoRR Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson
2023	J	`jnl`	The Case Against Explainability. CoRR Hofit Wasserman Rozen, Niva Elkin-Koren, Ran Gilad-Bachrach
2022	A	`conf`	A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. AISTATS Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach
2022	J	`jnl`	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 Yonatan E. Brand, Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach, Jeffrey M. Hausdorff
2022	J	`jnl`	Graph Trees with Attention. CoRR Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
2022	J	`jnl`	Inherent Inconsistencies of Feature Importance. CoRR Nimrod Harel, Ran Gilad-Bachrach, Uri Obolski
2021	J	`jnl`	Algorithmic copywriting: automated generation of health-related advertisements to improve their performance. Inf. Retr. J. Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
2021	J	`jnl`	Break your Bandit Routine with LSD Rewards: a Last Switch Dependent Analysis of Satiation and Seasonality. CoRR Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach
2021	A*	`conf`	Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. ICML Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss-Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
2021	J	`jnl`	PyBryt: auto-assessment and auto-grading for computational thinking. CoRR Christopher Pyles, François van Schalkwyk, Gerard J. Gorman, Marijan Beg, Lee Stott, Nir Levy, Ran Gilad-Bachrach
2021	A*	`conf`	Robust Model Compression Using Deep Hypotheses. AAAI Omri Armstrong, Ran Gilad-Bachrach
2021	J	`jnl`	Robust Model Compression Using Deep Hypotheses. CoRR Omri Armstrong, Ran Gilad-Bachrach
2021	A*	`conf`	Trees with Attention for Set Prediction Tasks. ICML Roy Hirsch, Ran Gilad-Bachrach
2020	J	`jnl`	Marginal Contribution Feature Importance - an Axiomatic Approach for The Natural Case. CoRR Amnon Catav, Boyang Fu, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
2020	A*	`conf`	The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance. WWW Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon
2019	J	`jnl`	E-Gotsky: Sequencing Content using the Zone of Proximal Development. CoRR Oded Vainas, Ori Bar-Ilan, Yossi Ben David, Ran Gilad-Bachrach, Galit Lukin, Meitar Ronen, Roi Shillo, Daniel Sitton
2019	A*	`conf`	Low Latency Privacy Preserving Inference. ICML Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha
2019	—	`conf`	Secure Data Exchange: A Marketplace in the Cloud. CCSW Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal, Mike Rosulek
2019	B	`conf`	Staying in the Zone: Sequencing Content in Classrooms Based on the Zone of Proximal Development. EDM Oded Vainas, Yossi Ben David, Ran Gilad-Bachrach, Meitar Ronen, Ori Bar-Ilan, Roi Shillo, Galit Lukin, Daniel Sitton
2019	J	`jnl`	The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance. CoRR Brit Youngmann, Ran Gilad-Bachrach, Danny Karmon, Elad Yom-Tov
2018	J	`jnl`	Logistic regression over encrypted data from fully homomorphic encryption. IACR Cryptol. ePrint Arch. Hao Chen, Ran Gilad-Bachrach, Kyoohyung Han, Zhicong Huang, Amir Jalali, Kim Laine, Kristin E. Lauter
2018	J	`jnl`	Low Latency Privacy Preserving Inference. CoRR Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach
2018	J	`jnl`	Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. CoRR John Moore, Joel Pfeiffer, Kai Wei, Rishabh K. Iyer, Denis Charles, Ran Gilad-Bachrach, Levi Boyles, Eren Manavoglu
2018	B	`conf`	Smooth Sensitivity Based Approach for Differentially Private PCA. ALT Alon Gonem, Ran Gilad-Bachrach
2018	J	`jnl`	Turning Lemons into Peaches using Secure Computation. CoRR Stav Buchsbaum, Ran Gilad-Bachrach, Yehuda Lindell
2017	J	`jnl`	Manual for Using Homomorphic Encryption for Bioinformatics. Proc. IEEE Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing
2017	J	`jnl`	Private Collaborative Neural Network Learning. IACR Cryptol. ePrint Arch. Melissa Chase, Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal
2017	J	`jnl`	Smooth Sensitivity Based Approach for Differentially Private Principal Component Analysis. CoRR Alon Gonen, Ran Gilad-Bachrach
2016	—	`conf`	Challenges for designing notifications for affective computing systems. UbiComp Adjunct Mary Czerwinski, Ran Gilad-Bachrach, Shamsi T. Iqbal, Gloria Mark
2016	A*	`conf`	CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. ICML Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing
2016	—	`conf`	Predicting "About-to-Eat" Moments for Just-in-Time Eating Intervention. Digital Health Tauhidur Rahman, Mary Czerwinski, Ran Gilad-Bachrach, Paul Johns
2016	A	`conf`	Scaffolding the scaffolding: Supporting children¿s social-emotional learning at home. CSCW Petr Slovák, Christopher Frauenberger, Ran Gilad-Bachrach, Mia Doces, Brian Smith, Rachel Kamb, Kael Rowan, Geraldine Fitzpatrick
2016	J	`jnl`	Secure Data Exchange: A Marketplace in the Cloud. IACR Cryptol. ePrint Arch. Ran Gilad-Bachrach, Kim Laine, Kristin E. Lauter, Peter Rindal, Mike Rosulek
2015	A	`conf`	DART: Dropouts meet Multiple Additive Regression Trees. AISTATS Korlakai Vinayak Rashmi, Ran Gilad-Bachrach
2015	J	`jnl`	DART: Dropouts meet Multiple Additive Regression Trees. CoRR K. V. Rashmi, Ran Gilad-Bachrach
2015	A*	`conf`	Designing Social and Emotional Skills Training: The Challenges and Opportunities for Technology Support. CHI Petr Slovák, Ran Gilad-Bachrach, Geraldine Fitzpatrick
2014	J	`jnl`	Crypto-Nets: Neural Networks over Encrypted Data. CoRR Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin E. Lauter, Michael Naehrig
2014	—	`conf`	PopTherapy: coping with stress through pop-culture. PervasiveHealth Pablo Paredes, Ran Gilad-Bachrach, Mary Czerwinski, Asta Roseway, Kael Rowan, Javier Hernandez
2014	A	`conf`	Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces. RecSys Yoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach, Liran Katzir, Noam Koenigstein, Nir Nice, Ulrich Paquet
2013	J	`jnl`	Classifier selection using the predicate depth. J. Mach. Learn. Res. Ran Gilad-Bachrach, Christopher J. C. Burges
2013	—	`conf`	The Median Hypothesis. Empirical Inference Ran Gilad-Bachrach, Christopher J. C. Burges
2013	J	`jnl`	Using Multiple Samples to Learn Mixture Models. CoRR Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
2013	—	`conf`	Using multiple samples to learn mixture models. NIPS Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
2012	—	`conf`	Confined intra-arm communication for medical applications. Wireless Health Trang Thai, Gerald DeJean, Ran Gilad-Bachrach
2012	—	`conf`	Full body gait analysis with Kinect. EMBC Moshe Gabel, Ran Gilad-Bachrach, Erin Renshaw, Assaf Schuster
2012	A	`conf`	Latent fault detection in large scale services. DSN Moshe Gabel, Assaf Schuster, Ran Gilad-Bachrach, Nikolaj S. Bjørner
2012	A	`conf`	Learning from mistakes: towards a correctable learning algorithm. CIKM Karthik Raman, Krysta M. Svore, Ran Gilad-Bachrach, Christopher J. C. Burges
2012	J	`jnl`	Optimal Distributed Online Prediction Using Mini-Batches. J. Mach. Learn. Res. Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao
2011	A	`conf`	On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. WSDM Ashok Kumar Ponnuswami, Kumaresh Pattabiraman, Qiang Wu, Ran Gilad-Bachrach, Tapas Kanungo
2011	A*	`conf`	Optimal Distributed Online Prediction. ICML Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao
2010	J	`jnl`	Optimal Distributed Online Prediction using Mini-Batches CoRR Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao
2010	J	`jnl`	Robust Distributed Online Prediction CoRR Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao
2009	J	`jnl`	Efficient Human Computation CoRR Ran Gilad-Bachrach, Aharon Bar-Hillel, Liat Ein-Dor
2009	—	`conf`	Efficient human computation: the distributed labeling problem. KDD Workshop on Human Computation Ran Gilad-Bachrach, Aharon Bar-Hillel, Liat Ein-Dor
2007	A	`conf`	Workstation capacity tuning using reinforcement learning. SC Aharon Bar-Hillel, Amir Di-Nur, Liat Ein-Dor, Ran Gilad-Bachrach, Yossi Ittach
2006	—	`ch.`	Large Margin Principles for Feature Selection. Feature Extraction Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
2006	—	`ch.`	Margin Based Feature Selection and Infogain with Standard Classifiers. Feature Extraction Ran Gilad-Bachrach, Amir Navot
2006	—	`—`	To PAC and beyond (למידה מונחית ומעבר לה.; Probably approximately correct learning.). Ran Gilad-Bachrach
2005	—	`conf`	Is Feature Selection Still Necessary? SLSFS Amir Navot, Ran Gilad-Bachrach, Yiftah Navot, Naftali Tishby
2005	—	`conf`	Query by Committee Made Real. NIPS Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
2004	A*	`conf`	Bayes and Tukey Meet at the Center Point. COLT Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
2004	A*	`conf`	Margin based feature selection - theory and algorithms. ICML Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
2003	A*	`conf`	An Information Theoretic Tradeoff between Complexity and Accuracy. COLT Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
2002	—	`conf`	Margin Analysis of the LVQ Algorithm. NIPS Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
2002	J	`jnl`	Query by committee, linear separation and random walks. Theor. Comput. Sci. Shai Fine, Ran Gilad-Bachrach, Eli Shamir