240 citations · 503 across the 31 of their papers we have counts for
33 papers
Harnessing Generative UI for Education: Tailored Learning Interactives
Alisa Kovshov, Anisha Choudhury, Anna Iurchenko +26
It is well established that students learn best through active engagement, particularly via interactive experiences tied to real-world concepts. Creating such experiences, however,…
Towards Scalable Measurement of Durable Skills
Amir Globerson, Amy Keeling, Anisha Choudhury +37
Durable skills, such as collaboration, creativity and critical thinking, are instrumental to success in the modern workforce. Yet, measuring these skills remains a persistent chall…
Towards an AI-Augmented Textbook
LearnLM Team, Google, : +34
Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduo…
Evaluating Gemini in an arena for learning
LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla +34
Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-t…
Do LLMs have Consistent Values?
Naama Rozen, Liat Bezalel, Gal Elidan +2
Large Language Models (LLM) technology is constantly improving towards human-like dialogue. Values are a basic driving force underlying human behavior, but little research has been…
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
Irina Jurenka, Markus Kunesch, Kevin R. McKee +71
A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about…
TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools
Avi Caciularu, Alon Jacovi, Eyal Ben-David +5
Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling…
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
Roni Rabin, Alexandre Djerbetian, Roee Engelberg +4
Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and t…
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Paul Roit, Johan Ferret, Lior Shani +16
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon i…
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Deborah Cohen, Moonkyung Ryu, Yinlam Chow +8
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carr…
Active Learning with Label Comparisons
Gal Yona, Shay Moran, Gal Elidan +1
Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annot…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
Solving Sokoban with forward-backward reinforcement learning
Yaron Shoham, Gal Elidan
Despite seminal advances in reinforcement learning in recent years, many domains where the rewards are sparse, e.g. given only at task completion, remain quite challenging. In such…
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom +8
Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize…
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Sella Nevo, Gal Elidan, Avinatan Hassidim +4
Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the worl…
HydroNets: Leveraging River Structure for Hydrologic Modeling
Zach Moshe, Asher Metzger, Gal Elidan +3
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the cl…
DNF-Net: A Neural Architecture for Tabular Data
Ami Abutbul, Gal Elidan, Liran Katzir +1
A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is…
Convex Nonparanormal Regression
Yonatan Woodbridge, Gal Elidan, Ami Wiesel
Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge in machine learning and statistics. We introduce C…
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks
Shai Rozenberg, Gal Elidan, Ran El-Yaniv
This paper is concerned with the defense of deep models against adversarial attacks. Inspired by the certificate defense approach, we propose a maximal adversarial distortion (MAD)…
Spectral Algorithm for Low-rank Multitask Regression
Yotam Gigi, Ami Wiesel, Sella Nevo +3
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learnin…
ML for Flood Forecasting at Scale
Sella Nevo, Vova Anisimov, Gal Elidan +11
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount o…
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Yotam Gigi, Gal Elidan, Avinatan Hassidim +5
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river disc…
Learning Rules-First Classifiers
Deborah Cohen, Amit Daniely, Amir Globerson +1
Complex classifiers may exhibit "embarassing" failures in cases where humans can easily provide a justified classification. Avoiding such failures is obviously of key importance. I…
On the Monotonicity of the Copula Entropy
Yaniv Tenzer, Gal Elidan
Understanding the way in which random entities interact is of key interest in numerous scientific fields. This can range from a full characterization of the joint distribution to s…