55 citations · 81 across the 18 of their papers we have counts for
19 papers
STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts
Zachary Bamberger, Till R. Saenger, Gilad Morad +3
Inference-Time-Compute (ITC) methods like Best-of- and Tree-of-Thoughts are meant to produce output candidates that are both high-quality and diverse, but their use of high-temp…
Assessing Policy Updates: Toward Trust-Preserving Intelligent User Interfaces
Matan Solomon, Ofra Amir, Omer Ben-Porat
Reinforcement learning agents are often updated with human feedback, yet such updates can be unreliable: reward misspecification, preference conflicts, or limited data may leave po…
Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance
Yotam Amitai, Reuth Mirsky, Ofra Amir
In human-agent teams, openly sharing goals is often assumed to enhance planning, collaboration, and effectiveness. However, direct communication of these goals is not always feasib…
"Trust me on this" Explaining Agent Behavior to a Human Terminator
Uri Menkes, Assaf Hallak, Ofra Amir
Consider a setting where a pre-trained agent is operating in an environment and a human operator can decide to temporarily terminate its operation and take-over for some duration o…
Interactive Explanations for Reinforcement-Learning Agents
Yotam Amitai, Ofra Amir, Guy Avni
As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL)…
From Actions to Words: Towards Abstractive-Textual Policy Summarization in RL
Sahar Admoni, Assaf Hallak, Yftah Ziser +2
Explaining reinforcement learning agents is challenging because policies emerge from complex reward structures and neural representations that are difficult for humans to interpret…
Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
Yotam Amitai, Yael Septon, Ofra Amir
Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, see…
Mitigating Skewed Bidding for Conference Paper Assignment
Inbal Rozencweig, Reshef Meir, Nick Mattei +1
The explosion of conference paper submissions in AI and related fields, has underscored the need to improve many aspects of the peer review process, especially the matching of pape…
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
Yotam Amitai, Guy Avni, Ofra Amir
As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL)…
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Yael Septon, Tobias Huber, Elisabeth André +1
Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and…
Stop the [Image] Steal: The Role and Dynamics of Visual Content in the 2020 U.S. Election Misinformation Campaign
Hana Matatov, Mor Naaman, Ofra Amir
Images are powerful. Visual information can attract attention, improve persuasion, trigger stronger emotions, and is easy to share and spread. We examine the characteristics of the…
Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media
Hana Matatov, Mor Naaman, Ofra Amir
The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying a…
"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison
Yotam Amitai, Ofra Amir
With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires users to understand the agent…
VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter
Anton Abilov, Yiqing Hua, Hana Matatov +2
The wide spread of unfounded election fraud claims surrounding the U.S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U.S.…
Learning to Characterize Matching Experts
Roee Shraga, Ofra Amir, Avigdor Gal
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements. Matching problems were traditionally solved in a semi-aut…
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Tobias Huber, Katharina Weitz, Elisabeth André +1
With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of th…
Exploring Computational User Models for Agent Policy Summarization
Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez +1
AI agents are being developed to support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understan…
Frustratingly Easy Truth Discovery
Reshef Meir, Ofra Amir, Omer Ben-Porat +3
Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. F…
Efficient Crowdsourcing via Proxy Voting
Gal Cohensius, Omer Ben Porat, Reshef Meir +1
Crowdsourcing platforms offer a way to label data by aggregating answers of multiple unqualified workers. We introduce a \textit{simple} and \textit{budget efficient} crowdsourcing…