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ORCIDKira Radinsky
Person information
- affiliation: Technion - Israel Institute of Technology, Department of Computer Science, Haifa, Israel
- affiliation: eBay Research, Netanya, Israel
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2020 – today
- 2026
[j14]Dan Kalifa
, Eric Horvitz, Kira Radinsky:
Learning protein representations with conformational dynamics. Bioinform. 42(5) (2026)- [j13]Noam Koren, Kira Radinsky, Daniel Freedman:
Neural Fourier Transform for Multiple Time Series Prediction. Trans. Mach. Learn. Res. 2026 (2026) - [j12]Sally Turutov, Kira Radinsky:
Mechanism-Aware Prediction of Tissue-Specific Drug Activity via Multi-Modal Biological Graphs. Trans. Mach. Learn. Res. 2026 (2026) - [j11]Yakir Yehuda, Kira Radinsky:
ODE-Constrained Generative Modeling of Cardiac Dynamics for 12-Lead ECG Synthesis. Trans. Mach. Learn. Res. 2026 (2026) - [c57]Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun, Kira Radinsky, Daniel Freedman:
SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels. AAAI 2026: 525-533 - [c56]Yakir Yehuda, Kira Radinsky:
PDE-Driven Spatiotemporal Generative Modeling for Multilead ECG Synthesis. AAAI 2026: 1453-1461 - [c55]Ofir Shabat, Ido Guy, Kira Radinsky:
Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War. ACL (Findings) 2026: 11484-11501 - [i25]Noam Koren, Rafael Moschopoulos, Kira Radinsky, Elad Hazan:
SFO: Learning PDE Operators via Spectral Filtering. CoRR abs/2601.17090 (2026) - 2025
- [j10]Dan Kalifa, Kira Radinsky, Eric Horvitz:
Beyond the leaderboard: leveraging predictive modeling for protein-ligand insights and discovery. Bioinform. 41(8) (2025) - [j9]Shweta Sharma, Natan Kaminsky, Kira Radinsky, Lilac Amirav:
Predicting Oxidation Potentials with DFT-Driven Machine Learning. J. Chem. Inf. Model. 65(11): 5345-5351 (2025) - [j8]Dan Kalifa, Uriel Singer, Kira Radinsky:
FusionProt: Fusing Sequence and Structural Information for Unified Protein Representation Learning. Trans. Mach. Learn. Res. 2025 (2025) - [c54]Amitay Sicherman, Kira Radinsky:
Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration. CIKM 2025: 2696-2706 - [c53]Dan Kalifa, Uriel Singer, Kira Radinsky:
GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning. CIKM 2025: 5763-5771 - [c52]Sally Turutov, Kira Radinsky:
Cross-Species Insights: Transforming Drug Efficacy from Rats to Humans Using Tissue-Specific Generative Models. KDD (1) 2025: 2618-2627 - [d1]Dan Kalifa, Eric Horvitz, Kira Radinsky:
Learning Protein Representations with Conformational Dynamics - Bioinformatics Submission. Zenodo, 2025
- [i24]Amitay Sicherman, Kira Radinsky:
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction Networks. CoRR abs/2501.18278 (2025) - [i23]Amitay Sicherman, Kira Radinsky:
Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration. CoRR abs/2502.01461 (2025) - [i22]Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun, Kira Radinsky, Daniel Freedman:
SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels. CoRR abs/2511.10025 (2025) - 2024
- [c51]Sally Turutov, Kira Radinsky:
Molecular Optimization Model with Patentability Constraint. AAAI 2024: 257-264 - [c50]Shadi Iskander, Kira Radinsky, Yonatan Belinkov:
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information. NAACL (Short Papers) 2024: 379-390 - [c49]Mousa Arraf, Kira Radinsky:
CIQA: A Coding Inspired Question Answering Model. SIGIR 2024: 1973-1983 - [i21]Shadi Iskander, Kira Radinsky, Yonatan Belinkov:
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information. CoRR abs/2403.09516 (2024) - [i20]Noam Koren, Kira Radinsky:
Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform. CoRR abs/2405.13812 (2024) - [i19]Dan Kalifa, Uriel Singer, Ido Guy, Guy D. Rosin, Kira Radinsky:
Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly. CoRR abs/2405.13995 (2024) - [i18]Dan Kalifa, Uriel Singer, Kira Radinsky:
GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning. CoRR abs/2408.00057 (2024) - [i17]Yakir Yehuda, Kira Radinsky:
Ordinary Differential Equations for Enhanced 12-Lead ECG Generation. CoRR abs/2409.17833 (2024) - 2023
- [c48]Shadi Iskander, Kira Radinsky, Yonatan Belinkov:
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection. ACL (Findings) 2023: 5961-5977 - [c47]Moran Beladev, Gilad Katz, Lior Rokach, Uriel Singer, Kira Radinsky:
GraphERT- Transformers-based Temporal Dynamic Graph Embedding. CIKM 2023: 68-77 - [c46]Natan Kaminsky, Uriel Singer, Kira Radinsky:
CFOM: Lead Optimization For Drug Discovery With Limited Data. CIKM 2023: 1056-1066 - [c45]Sally Turutov, Kira Radinsky:
Generating Optimized Molecules without Patent Infringement. CIKM 2023: 4850-4856 - [c44]Dave Makhervaks, Plia Gillis, Kira Radinsky:
Clinical Contradiction Detection. EMNLP 2023: 1248-1263 - [c43]Yakir Yehuda, Daniel Freedman, Kira Radinsky:
Self-supervised Classification of Clinical Multivariate Time Series using Time Series Dynamics. KDD 2023: 5416-5427 - [c42]Gal Peretz, Mousa Arraf, Kira Radinsky:
What If: Generating Code to Answer Simulation Questions in Chemistry Texts. SIGIR 2023: 1335-1344 - [i16]Shadi Iskander, Kira Radinsky, Yonatan Belinkov:
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection. CoRR abs/2305.10204 (2023) - 2022
- [j7]Shunit Agmon, Plia Gillis, Eric Horvitz, Kira Radinsky:
Gender-sensitive word embeddings for healthcare. J. Am. Medical Informatics Assoc. 29(3): 415-423 (2022) - [c41]Uriel Singer, Kira Radinsky:
EqGNN: Equalized Node Opportunity in Graphs. AAAI 2022: 8333-8341 - [c40]Galia Nordon, Aviram Magen, Ido Guy, Kira Radinsky:
Learning to Rank Articles for Molecular Queries. AAAI 2022: 12594-12600 - [c39]Roy Benjamin, Uriel Singer, Kira Radinsky:
Graph Neural Networks Pretraining Through Inherent Supervision for Molecular Property Prediction. CIKM 2022: 2903-2912 - [c38]Guy D. Rosin, Kira Radinsky:
Temporal Attention for Language Models. NAACL-HLT (Findings) 2022: 1498-1508 - [c37]Dan Kalifa, Uriel Singer, Ido Guy, Guy D. Rosin, Kira Radinsky:
Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly. WSDM 2022: 430-438 - [c36]Guy D. Rosin, Ido Guy, Kira Radinsky:
Time Masking for Temporal Language Models. WSDM 2022: 833-841 - [i15]Guy D. Rosin, Kira Radinsky:
Temporal Attention for Language Models. CoRR abs/2202.02093 (2022) - [i14]Gal Peretz, Kira Radinsky:
What If: Generating Code to Answer Simulation Questions. CoRR abs/2204.07835 (2022) - [i13]Uriel Singer, Haggai Roitman, Ido Guy, Kira Radinsky:
tBDFS: Temporal Graph Neural Network Leveraging DFS. CoRR abs/2206.05692 (2022) - 2021
- [j6]Uriel Singer, Kira Radinsky, Eric Horvitz:
On biases of attention in scientific discovery. Bioinform. 36(22-23): 5269-5274 (2021) - [c35]Tomer Golany, Daniel Freedman, Kira Radinsky:
ECG ODE-GAN: Learning Ordinary Differential Equations of ECG Dynamics via Generative Adversarial Learning. AAAI 2021: 134-141 - [c34]Guy Barshatski, Galia Nordon, Kira Radinsky:
Multi-Property Molecular Optimization using an Integrated Poly-Cycle Architecture. CIKM 2021: 3727-3736 - [c33]Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha:
12-Lead ECG Reconstruction via Koopman Operators. ICML 2021: 3745-3754 - [c32]Guy Barshatski, Kira Radinsky:
Unpaired Generative Molecule-to-Molecule Translation for Lead Optimization. KDD 2021: 2554-2564 - [c31]Guy D. Rosin, Ido Guy, Kira Radinsky:
Event-Driven Query Expansion. WSDM 2021: 391-399 - [i12]Uriel Singer, Kira Radinsky:
EqGNN: Equalized Node Opportunity in Graphs. CoRR abs/2108.08800 (2021) - [i11]Guy D. Rosin, Ido Guy, Kira Radinsky:
Time Masking for Temporal Language Models. CoRR abs/2110.06366 (2021) - 2020
- [j5]Slava Novgorodov, Ido Guy, Guy Elad, Kira Radinsky:
Descriptions from the Customers: Comparative Analysis of Review-based Product Description Generation Methods. ACM Trans. Internet Techn. 20(4): 44:1-44:31 (2020) - [c30]Tomer Golany, Gal Lavee, Shai Tejman-Yarden, Kira Radinsky:
Improving ECG Classification Using Generative Adversarial Networks. AAAI 2020: 13280-13285 - [c29]Galia Nordon, Levi Gottlieb, Kira Radinsky:
Chemical and Textual Embeddings for Drug Repurposing. AAAI 2020: 13338-13343 - [c28]Moran Beladev, Lior Rokach, Gilad Katz, Ido Guy, Kira Radinsky:
tdGraphEmbed: Temporal Dynamic Graph-Level Embedding. CIKM 2020: 55-64 - [c27]Tomer Golany, Kira Radinsky, Daniel Freedman:
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification. ICML 2020: 3597-3606 - [i10]Tomer Golany, Daniel Freedman, Kira Radinsky:
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification. CoRR abs/2006.15353 (2020) - [i9]Guy D. Rosin, Ido Guy, Kira Radinsky:
Event-Driven Query Expansion. CoRR abs/2012.12065 (2020)
2010 – 2019
- 2019
- [c26]Tomer Golany, Kira Radinsky:
PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification. AAAI 2019: 557-564 - [c25]Galia Nordon, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, Kira Radinsky:
Building Causal Graphs from Medical Literature and Electronic Medical Records. AAAI 2019: 1102-1109 - [c24]Galia Nordon, Gideon Koren, Varda Shalev, Eric Horvitz, Kira Radinsky:
Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications. AAAI 2019: 9565-9572 - [c23]Guy Elad, Ido Guy, Slava Novgorodov, Benny Kimelfeld, Kira Radinsky:
Learning to Generate Personalized Product Descriptions. CIKM 2019: 389-398 - [c22]Guy D. Rosin, Kira Radinsky:
Generating Timelines by Modeling Semantic Change. CoNLL 2019: 186-195 - [c21]Dor Ringel, Gal Lavee, Ido Guy, Kira Radinsky:
Cross-Cultural Transfer Learning for Text Classification. EMNLP/IJCNLP (1) 2019: 3871-3881 - [c20]Uriel Singer, Ido Guy, Kira Radinsky:
Node Embedding over Temporal Graphs. IJCAI 2019: 4605-4612 - [c19]Dean Zadok, Tom Hirshberg, Amir Biran, Kira Radinsky, Ashish Kapoor:
Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations. SIMULTECH 2019: 414-421 - [c18]Slava Novgorodov, Guy Elad, Ido Guy, Kira Radinsky:
Generating Product Descriptions from User Reviews. WWW 2019: 1354-1364 - [c17]Shahar Harel, Sefi Albo, Eugene Agichtein, Kira Radinsky:
Learning Novelty-Aware Ranking of Answers to Complex Questions. WWW 2019: 2799-2805 - [i8]Uriel Singer, Ido Guy, Kira Radinsky:
Node Embedding over Temporal Graphs. CoRR abs/1903.08889 (2019) - [i7]Dean Zadok, Tom Hirshberg, Amir Biran, Kira Radinsky, Ashish Kapoor:
Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations. CoRR abs/1905.05940 (2019) - [i6]Guy D. Rosin, Kira Radinsky:
Generating Timelines by Modeling Semantic Change. CoRR abs/1909.09907 (2019) - 2018
- [c16]Eylon Shoshan, Kira Radinsky:
Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text? CoNLL 2018: 200-210 - [c15]Shahar Harel, Kira Radinsky:
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks. KDD 2018: 331-339 - [c14]Evgeniy Gabrilovich, Kira Radinsky, Kuansan Wang:
The BIG Web Track Chairs' Welcome & Organization. WWW (Companion Volume) 2018: 629-630 - [i5]Shahar Harel, Kira Radinsky:
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks. CoRR abs/1804.02668 (2018) - [i4]Dana Sagi, Tzoof Avny, Kira Radinsky, Eugene Agichtein:
Learning to Focus when Ranking Answers. CoRR abs/1808.02724 (2018) - 2017
- [c13]Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, Omer Levy:
Named Entity Disambiguation for Noisy Text. CoNLL 2017: 58-68 - [c12]Guy D. Rosin, Eytan Adar, Kira Radinsky:
Learning Word Relatedness over Time. EMNLP 2017: 1168-1178 - [c11]Ido Guy, Kira Radinsky:
Structuring the Unstructured: From Startup to Making Sense of eBay's Huge eCommerce Inventory. SIGIR 2017: 1351 - [i3]Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, Omer Levy:
Named Entity Disambiguation for Noisy Text. CoRR abs/1706.09147 (2017) - [i2]Guy D. Rosin, Eytan Adar, Kira Radinsky:
Learning Word Relatedness over Time. CoRR abs/1707.08081 (2017) - 2014
- [e1]Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann:
Advances in Information Retrieval - 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings. Lecture Notes in Computer Science 8416, Springer 2014, ISBN 978-3-319-06027-9 [contents] - [i1]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Learning to Predict from Textual Data. CoRR abs/1402.0574 (2014) - 2013
- [j4]Kira Radinsky, Krysta M. Svore, Susan T. Dumais, Milad Shokouhi, Jaime Teevan, Alex Bocharov, Eric Horvitz:
Behavioral dynamics on the web: Learning, modeling, and prediction. ACM Trans. Inf. Syst. 31(3): 16 (2013) - [c10]Fernando Diaz, Susan T. Dumais, Miles Efron, Kira Radinsky, Maarten de Rijke, Milad Shokouhi:
SIGIR 2013 workshop on time aware information access (#TAIA2013). SIGIR 2013: 1137 - [c9]Kira Radinsky, Eric Horvitz:
Mining the web to predict future events. WSDM 2013: 255-264 - [c8]Kira Radinsky, Paul N. Bennett:
Predicting content change on the web. WSDM 2013: 415-424 - [c7]Kira Radinsky, Fernando Diaz, Susan T. Dumais, Milad Shokouhi, Anlei Dong, Yi Chang:
Temporal web dynamics and its application to information retrieval. WSDM 2013: 781-782 - 2012
- [b1]Kira Radinsky:
Learning to predict the future using Web knowledge and dynamics. Technion - Israel Institute of Technology, Israel, 2012 - [j3]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Learning to Predict from Textual Data. J. Artif. Intell. Res. 45: 641-684 (2012) - [j2]Fernando Diaz, Susan T. Dumais, Kira Radinsky, Maarten de Rijke, Milad Shokouhi:
#TAIA2012. SIGIR Forum 46(2): 102-106 (2012) - [j1]Kira Radinsky:
Learning to Predict the Future using Web Knowledge and Dynamics. SIGIR Forum 46(2): 114-115 (2012) - [c6]Milad Shokouhi, Kira Radinsky:
Time-sensitive query auto-completion. SIGIR 2012: 601-610 - [c5]Kira Radinsky, Krysta M. Svore, Susan T. Dumais, Jaime Teevan, Alex Bocharov, Eric Horvitz:
Modeling and predicting behavioral dynamics on the web. WWW 2012: 599-608 - [c4]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Learning causality for news events prediction. WWW 2012: 909-918 - 2011
- [c3]Kira Radinsky, Nir Ailon:
Ranking from pairs and triplets: information quality, evaluation methods and query complexity. WSDM 2011: 105-114 - [c2]Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, Shaul Markovitch:
A word at a time: computing word relatedness using temporal semantic analysis. WWW 2011: 337-346
2000 – 2009
- 2008
- [c1]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Predicting theNews of Tomorrow Using Patterns in Web Search Queries. Web Intelligence 2008: 363-367
Coauthor Index
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last updated on 2026-07-31 23:53 CEST by the dblp team
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