• Sep 2021 Journal Article Journal of Political Economy

    Feasible Joint Posterior Beliefs

    We study the set of possible joint posterior belief distributions of a group of agents who share a common prior regarding a binary state and who observe some information structure. For two agents, we introduce a quantitative version of Aumann’s agreement theorem and show that it is equivalent to a characterization of feasible distributions from a 1995 work by Dawid andshow more
  • 28 Aug 2021 Journal Article Annals of Mathematics and Artificial Intelligence

    Real-time solving of computationally hard problems using optimal algorithm portfolios

    Various hard real-time systems have a desired requirement which is impossible to fulfill: to solve a computationally hard optimization problem within a short and fixed amount of time T, e.g., T = 0.5 seconds. For such a task, the exact, exponential algorithms, as well as various Polynomial-Time Approximation Schemes, are irrelevant because they can exceed T. What isshow more
  • 26 Aug 2021 Journal Article Acta Psychologica

    The effect of aging on facial attractiveness: An empirical and computational investigation

    Dexian He , Clifford I Workman , Yoed N Kenett , Xianyou He , Anjan Chatterjee
    How does aging affect facial attractiveness? We tested the hypothesis that people find older faces less attractive than younger faces, and furthermore, that these aging effects are modulated by the age and sex of the perceiver and by the specific kind of attractiveness judgment being made. Using empirical and computational network science methods, we confirmed that withshow more
  • 10 Aug 2021 Journal Article Operations Research Letters

    Robust learning in social networks via matrix scaling

    The influence vanishing property in social networks states that the influence of the most influential agent vanishes as society grows. Removing this assumption causes a failure of learning of boundedly rational dynamics. We suggest a boundedly rational methodology that leads to learning in almost all networks. The methodology adjusts the agent's weights based on theshow more
  • 6 Aug 2021 Journal Article Journal of Economic Theory

    Virtually additive learning

    We introduce the class of virtually additive non-Bayesian learning heuristics to aggregating beliefs in social networks. A virtually additive heuristic is characterized by a single function that maps a belief to a real number that represents the virtual belief. To aggregate beliefs, an agent simply sums up all the virtual beliefs of his neighbors to obtain hisshow more
  • Aug 2021 Conference Paper The 34th Annual Conference on Learning Theory (COLT 2021)

    Frank-Wolfe with Nearest Extreme Point Oracle

    Dan Garber , Noam Wolf
    We consider variants of the classical Frank-Wolfe algorithm for constrained smooth convex minimization, that instead of access to the standard oracle for minimizing a linear function over the feasible set, have access to an oracle that can find an extreme point of the feasible set that is closest in Euclidean distance to a given vector. We first show that for manyshow more
  • Aug 2021 Conference Paper Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing

    A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters

    Mengjie Zhao , Yi Zhu , Ehsan Shareghi , Ivan Vuli , Roi Reichart , Anna Korhonen , Hinrich Schütze
    Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the modelshow more
  • Aug 2021 Conference Paper Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing

    Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions

    Daniel Rosenberg , Itai Gat , Amir Feder , Roi Reichart
    Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentationsshow more
  • Aug 2021 Journal Article IEEE Transactions on Knowledge and Data Engineering

    Learning to Rerank Schema Matches

    Avigdor Gal , Haggai Roitman , Roee Shraga
    Schema matching is at the heart of integrating structured and semi-structured data with applications in data warehousing, data analysis recommendations, Web table matching, etc. Schema matching is known as an uncertain process and a common method to overcome this uncertainty introduces a human expert with a ranked list of possible schema matches to choose from, knownshow more
  • 29 Jul 2021 Preprint bioRxiv

    Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure

    Marcela Ovando Tellez , Yoed N Kenett , Mathias Benedek , Matthieu Bernard , Joan Belo , Benoit Beranger , Theophile Bieth , Emmanuelle Volle
    Creative cognition relies on the ability to form remote associations between concepts, which allows to generate novel ideas or solve new problems. Such an ability is related to the organisation of semantic memory; yet whether real-life creative behaviour relies on semantic memory organisation and its neural substrates remains unclear. Therefore, this study exploredshow more
  • 29 Jul 2021 Conference Paper Proceedings of the Annual Meeting of the Cognitive Science Society

    Creative Foraging: Examining Relations Between Foraging Styles, Semantic Memory Structure, and Creative Thinking

    Yoed N Kenett , Brendan S Baker , Thomas T Hills , Yuval Hart , Roger E Beaty
    Creativity has been separately related to differences in foraging search styles and semantic memory structure. Here, we converge computational methods to examine the relation of creative foraging styles, semantic memory structure, and creative thinking. A large sample of participants was divided into groups based on their exploration and exploitation strategies in ashow more
  • 28 Jul 2021 Preprint arXiv

    From Monopoly to Competition: Optimal Contests Prevail

    Xiaotie Deng , Yotam Gafni , Ron Lavi , Tao Lin , Hongyi Ling
    We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) formshow more
  • 27 Jul 2021 Journal Article Trends in Cognitive Sciences

    To predict human choice, consider the context

    Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitiveshow more
  • 27 Jul 2021 Conference Paper UAI 2021 : 37th Conference on Uncertainty in Artificial Intelligence

    Bandits with Partially Observable Confounded Data

    We study linear contextual bandits with access to a large, confounded, offline dataset that was sampled from some fixed policy. We show that this problem is closely related to a variant of the bandit problem with side information. We construct a linear bandit algorithm that takes advantage of the projected information, and prove regret bounds. Our results demonstrateshow more
  • 27 Jul 2021 Conference Paper UAI 2021 : 37th Conference on Uncertainty in Artificial Intelligence

    Approximate Implication with d-Separation

    The graphical structure of Probabilistic Graphical Models (PGMs) encodes the conditional independence (CI) relations that hold in the modeled distribution. Graph algorithms, such as d-separation, use this structure to infer additional conditional independencies, and to query whether a specific CI holds in the distribution. The premise of all current systems-of-inferenceshow more
  • 21 Jul 2021 Preprint arXiv

    A Thin Self-Stabilizing Asynchronous Unison Algorithm with Applications to Fault Tolerant Biological Networks

    Yuval Emek , Eyal Keren
    Introduced by Emek and Wattenhofer (PODC 2013), the \emph{stone age (SA)} model provides an abstraction for network algorithms distributed over randomized finite state machines. This model, designed to resemble the dynamics of biological processes in cellular networks, assumes a weak communication scheme that is built upon the nodes ability to sense their vicinity inshow more
  • 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning, ICML

    Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization

    Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generative models, by introducing a randomized predictor thatshow more
  • 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning, ICML

    Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

    Nadav Hallak , Panayotis Mertikopoulos , Volkan Cevher
    This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and there are no gradient-based algorithmic frameworks capable of providing a solution. On that account, we propose ashow more
  • 21 Jul 2021 Preprint arXiv

    Efficient Deterministic Leader Election for Programmable Matter

    Fabien Dufoulon , Shay Kutten , William K Moses
    It was suggested that a programmable matter system (composed of multiple computationally weak mobile particles) should remain connected at all times since otherwise, reconnection is difficult and may be impossible. At the same time, it was not clear that allowing the system to disconnect carried a significant advantage in terms of time complexity. We demonstrate for ashow more
  • 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning

    Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

    Andrew Jesson , Sören Mindermann , Yarin Gal , Uri Shalit
    We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders. Unobserved confounders introduce ignorance -- a level of unidentifiability -- about an individual's response to treatment by inducing bias in CATE estimates. We present a new parametric interval estimator suited forshow more
  • 21 Jul 2021 Conference Paper International Conference on Machine Learning

    Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

    Junhyung Park , Uri Shalit , Bernhard Schölkopf , Krikamol Muandet
    We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment's distributional aspects beyond the mean. We first introduce a formal definition of the CoDiTE associated with a distance function between probability measures. Then we discussshow more
  • 18 Jul 2021 Conference Paper EC'21: Proceedings of the 22nd ACM Conference on Economics and Computation

    On Social Networks that Support Learning

    Bayes-rational agents reside on a social network. They take binary actions sequentially and irrevocably, and the right action depends on an unobservable state. Each agent receives a bounded private signal about the realized state and observes the actions taken by the neighbors who acted before. How does the network topology affect the ability of agents to aggregate theshow more
  • 18 Jul 2021 Conference Paper EC'21: Proceedings of the 22nd ACM Conference on Economics and Computation

    Sequential Naive Learning

    Itai Arieli , Yakov Babichenko , Manuel Mueller-Frank
    We analyze boundedly rational updating from aggregate statistics in a model with binary actions and binary states. Agents each take an irreversible action in sequence after observing the unordered set of previous actions. Each agent first forms her prior based on the aggregate statistic, then incorporates her signal with the prior based on Bayes rule, and finally appliesshow more
  • 18 Jul 2021 Preprint arXiv

    Incomplete Information VCG Contracts for Common Agency

    We study contract design for welfare maximization in the well known "common agency" model of [Bernheim and Whinston, 1986]. This model combines the challenges of coordinating multiple principals with the fundamental challenge of contract design: that principals have incomplete information of the agent's choice of action. Motivated by the significant social inefficiencyshow more
  • 18 Jul 2021 Preprint arXiv

    Regret-Minimizing Bayesian Persuasion

    We study a Bayesian persuasion setting with binary actions (adopt and reject) for Receiver. We examine the following question - how well can Sender perform, in terms of persuading Receiver to adopt, when ignorant of Receiver's utility? We take a robust (adversarial) approach to study this problem; that is, our goal is to design signaling schemes for Sender that performshow more
  • 16 Jul 2021 Journal Article ACM SIGecom Exchanges

    Feasible joint posterior beliefs (through examples)

    Through a sequence of examples, we survey the main results of "Feasible Joint Posterior Beliefs" [Arieli, Babichenko, Sandomirskiy, Tamuz 2021]. A group of agents share a common prior distribution regarding a binary state, and observe some information structure. What are the possible joint distributions of their posteriors? We discuss feasibility of product distributionsshow more
  • 14 Jul 2021 Journal Article Scientific Reports

    Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks

    Orr Levy , Yoed N Kenett , Orr Oxenberg , Nichol Castro , Simon De Deyne , Michael S Vitevitch , Shlomo Havlin
    An essential aspect of human communication is the ability to access and retrieve information from ones’ ‘mental lexicon’. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scaleshow more
  • 13 Jul 2021 Journal Article Journal of Artificial Intelligence Research

    Representative Committees of Peers

    A population of voters must elect representatives among themselves to decide on a sequence of possibly unforeseen binary issues. Voters care only about the final decision, not the elected representatives. The disutility of a voter is proportional to the fraction of issues, where his preferences disagree with the decision. While an issue-by-issue vote by all voters wouldshow more
  • 6 Jul 2021 Journal Article Journal of Medical Internet Research

    Unique Internet Search Strategies of Individuals With Self-Stated Autism: Quantitative Analysis of Search Engine Users' Investigative Behaviors

    Background: Although autism is often characterized in literature by the presence of repetitive behavior, in structured decision tasks, individuals with autism spectrum disorder (ASD) have been found to examine more options in a given time period than controls. Objective: We aimed to examine whether this investigative tendency emerges in information searchesshow more
  • 6 Jul 2021 Conference Paper Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations

    Automatic Speech-Based Checklist for Medical Simulations

    Sapir Gershov , Yaniv Ringel , Erez Dvir , Tzvia Tsirilman , Elad Ben Zvi , Sandra Braun , Aeyal Raz , Shlomi Laufer
    Medical simulators provide a controlled environment for training and assessing clinical skills. However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist. This makes the assessment process inefficient and expensive. Furthermore, this evaluation method doesshow more
  • 24 Jun 2021 Conference Paper International Conference on Advanced Information Systems Engineering (CAiSE)

    ADAMAP: Automatic Alignment of Relational Data Sources Using Mapping Patterns

    Diego Calvanese , Avigdor Gal , Naor Haba , Davide Lanti , Marco Montali , Alessandro Mosca , Roee Shraga
    We propose a method for automatically extracting semantics from data sources. The availability of multiple data sources on the one hand and the lack of proper semantic documentation of such data sources on the other hand call for new strategies in integrating data sources by extracting semantics from the data source itself rather than from its documentation. In thisshow more
  • 23 Jun 2021 Journal Article Clinical Psychological Science

    The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

    Yaakov Ophir , Refael Tikochinski , Anat Brunstein Klomek , Roi Reichart
    Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. In this article, we introduce a comprehensive outlook on the emerging movement to integrate computational linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art deep neural network models, in this “travel guide” article, we describe, in ashow more
  • 22 Jun 2021 Conference Paper IEEE 37th International Conference on Data Engineering (ICDE)

    Learning to Characterize Matching Experts

    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-automatic manner, with correspondences being generated by matching algorithms and outcomes subsequently validated by human experts. Human-in-the-loop data integration has been recently challengedshow more
  • 20 Jun 2021 Conference Paper PODS'21: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

    A Dichotomy for the Generalized Model Counting Problem for Unions of Conjunctive Queries

    Batya Kenig , Dan Suciu
    We study the \em generalized model counting problem, defined as follows: given a database, and a set of deterministic tuples, count the number of subsets of the database that include all deterministic tuples and satisfy the query. This problem is computationally equivalent to the evaluation of the query over a tuple-independent probabilistic database where all tuplesshow more
  • 15 Jun 2021 Journal Article Frontiers in Nutrition

    Promoting Healthy Eating Behaviors by Incentivizing Exploration of Healthy Alternatives

    Y Shavit , Yefim Roth , Kinneret Teodorescu
    Incentive-based intervention programs aimed at promoting healthy eating behaviors usually focus on incentivizing repeating the desired behavior. Unfortunately, even when effective, these interventions are often short-lived and do not lead to a lasting behavioral change. We present a new type of intervention program focused on incentivizing exploration of new healthyshow more
  • 15 Jun 2021 Conference Paper STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing

    Settling the complexity of Nash equilibrium in congestion games

    Yakov Babichenko , Aviad Rubinstein
    We consider (i) the problem of finding a (possibly mixed) Nash equilibrium in congestion games, and (ii) the problem of finding an (exponential precision) fixed point of the gradient descent dynamics of a smooth function f:[0,1]n → ℝ. We prove that these problems are equivalent. Our result holds for various explicit descriptions of f, ranging from (almost general)show more
  • 10 Jun 2021 Edited Volume Proceedings of the Third Workshop on Computational Typology and Multilingual NLP (SIGTYP)

    Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

    Ekaterina Vylomova , Elizabeth Salesky , Sabrina Mielke , Gabriella Lapesa , Ritesh Kumar , Harald Hammarström , Ivan Vulić , Anna Korhonen , Roi Reichart , Edoardo Maria Ponti , Ryan Cotterell
    SIGTYP 2021 is the third edition of the workshop for typology-related research and its integration into multilingual Natural Language Processing (NLP). The workshop is co-located with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021), which takes place virtually this year. Our workshop includes a sharedshow more
  • 9 Jun 2021 Journal Article Journal of Alzheimer's Disease

    Linking the Clinical Dementia Rating Scale-Sum of Boxes, the Clinician’s Interview-Based Impression Plus Caregiver Input, and the Clinical Global Impression Scale: Evidence based on Individual Participant Data from Five Randomized Clinical Trials of Donepezil

    Myrto Samara , Stephen Z Levine , Kazufumi Yoshida , Yair Goldberg , Andrea Cipriani , Orestis Efthimiou , Takeshi Iwatsubo , Stefan Leucht , Toshiaki A Furakawa
    Background: In patients with Alzheimer’s disease, global assessment scales, such as the Clinical Dementia Rating-Sum of Boxes (CDR-SB), the Clinician’s Interview-Based Impression Plus Caregiver Input (CIBI plus), and the Clinical Global Impression (CGI) are commonly used. Objective: To clinically understand and interpret the associations between these scales, we examinedshow more
  • 9 Jun 2021 Preprint arXiv

    Proportional Participatory Budgeting with Substitute Projects

    Roy Fairstein , Reshef Meir , Kobi Gal
    Participatory budgeting is a democratic process for allocating funds to projects based on the votes of members of the community. However, most input methods of voters' preferences prevent the voters from expressing complex relationships among projects, leading to outcomes that do not reflect their preferences well enough. In this paper, we propose an input method thatshow more
  • 8 Jun 2021 Journal Article Operations Research Letters

    Complexity, algorithms and applications of the integer network flow with fractional supplies problem

    Dorit S Hochbaum , Asaf Levin , Xu Rao
    We consider here the integer minimum cost network flow when some of the supplies are fractional. In the presence of fractional supplies it is impossible to satisfy the flow balance constraints, creating an imbalance. We present here a polynomial time algorithm for minimizing the total cost of flow and imbalance penalty. We also show that in the presence of ashow more
  • 7 Jun 2021 Journal Article Clinical Microbiology and Infection

    Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study

    Gil Caspi , Avshalom Dayan , Yael Eshal , Sigal Liverant-Taub , Gilad Twig , Uri Shalit , Yair Lewis , Avi Shina , Oren Caspi
    Objective To analyze the correlation between COVID-19 vaccination percentage and socioeconomic status (SES). Methods A nationwide ecologic study based on open-sourced, anonymized, aggregated data provided by the Israel Ministry of Health. The correlations between municipal SES, vaccination percentage, and active COVID-19 cases during the vaccination campaign wereshow more
  • 1 Jun 2021 Journal Article Journal of the American Statistical Association

    Filtering the Rejection Set While Preserving False Discovery Rate Control

    Eugene Katsevich , Chiara Sabatti , Marina Bogomolov
    Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the international classification of diseases (ICD), the directed acyclic graph structure of the gene ontology (GO), or the spatial structure in genome-wide association studies. In the context of multiple testing, the resulting relationships among hypothesesshow more
  • 30 May 2021 Preprint arXiv

    Improving Efficiency of Tests for Composite Null Hypotheses

    The goal of mediation analysis is to study the effect of exposure on an outcome interceded by a mediator. Two simple hypotheses are tested: the effect of the exposure on the mediator, and the effect of the mediator on the outcome. When either of these hypotheses is true, a predetermined significance level can be assured. When both nulls are true, the same test becomesshow more
  • 24 May 2021 Journal Article Thinking Skills and Creativity

    Forward flow and creative thought: Assessing associative cognition and its role in divergent thinking

    Roger E Beaty , Daniel C Zeitlen , Brendan S Baker , Yoed N Kenett
    Creative thinking is thought to be supported by both spontaneous associative and controlled executive processes. Recently, a new measure of associative cognition has been developed—forward flow—which uses computational semantic models (e.g., latent semantic analysis; LSA) to capture “how far” people travel in semantic space during a chained free association task. Theshow more
  • 24 May 2021 Preprint Social Science Research Network

    Hospitalization versus Home Care: Balancing Mortality and Infection Risks for Hematology Patients

    Mor Armony , Galit B Yom-Tov
    Problem definition: Previous research has shown that early discharge of patients may hurt their medical outcomes. However, in many cases the “optimal” length of stay (LOS) and the best location for treatment of the patient are not obvious. A case in point is hematology patients, for whom these are critical decisions. Patients with hematological malignancies are susceptibleshow more
  • 20 May 2021 Preprint arXiv

    A Fully Adaptive Self-Stabilizing Transformer for LCL Problems

    Shimon Bitton , Yuval Emek , Taisuke Izumi , Shay Kutten
    The first generic self-stabilizing transformer for local problems in a constrained bandwidth model is introduced. This transformer can be applied to a wide class of locally checkable labeling (LCL) problems, converting a given fault free synchronous algorithm that satisfies certain conditions into a self-stabilizing synchronous algorithm for the same problem. Theshow more
  • 19 May 2021 Preprint arXiv

    Knowledge-driven Data Ecosystems Towards Data Transparency

    Sandra Geisler , Maria-Esther Vidal , Cinzia Cappiello , Bernadette Farias Lóscio , Avigdor Gal , Matthias Jarke , Maurizio Lenzerini , Paolo Missier , Boris Otto , Elda Paja , Barbara Pernici , Jakob Rehof
    A Data Ecosystem offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In thisshow more
  • 19 May 2021 Preprint arXiv

    Testing partial conjunction hypotheses under dependency, with applications to meta-analysis

    In many statistical problems the hypotheses are naturally divided into groups, and the investigators are interested to perform group-level inference, possibly along with inference on individual hypotheses. We consider the goal of discovering groups containing u or more signals with group-level false discovery rate (FDR) control. This goal can be addressed byshow more
  • 18 May 2021 Edited Volume Robotics and Computer-integrated Manufacturing

    Agile robotics for industrial applications: Editorial

    Craig Schlenoff , Zeid Kootbally , Erez Karpas
    Advances in automation have provided for sustained productivity increases and manufacturing growth over the past decade. Sustaining this growth will require automation to become more agile and flexible, enabling the automation of tasks that require a high degree of human dexterity and the ability to react to unforeseen circumstances. Applying robots is one promisingshow more
  • 18 May 2021 Conference Paper ICAPS 2021 Workshop on Heuristics and Search for Domain-independent Planning

    A Compilation Based Approach to Finding Centroids and Minimum Covering States in Planning

    In some scenarios, an agent may want to prepare for achieving one of several possible goals, by reaching some state which is close (according to some metric) to all possible goals. Recently, this task was formulated as the finding centroids (which minimize the average distance to the goals) or minimum covering states (which minimize the maximum distance). In this papershow more
  • 18 May 2021 Conference Paper Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling

    Learning-based Synthesis of Social Laws in STRIPS

    In a multi-agent environment, each agent must take into account not only the actions it must perform to achieve its goals, but also the behavior of other agents in the system, which usually requires some sort of coordination between the agents. One way to avoid the complexity of centralized planning and online negotiation between agents is to design an artificial socialshow more
  • 11 May 2021 Preprint arXiv

    Designing an Automatic Agent for Repeated Language based Persuasion Games

    Persuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized messages that do not consist of rich human language. In this paper we consider a repeated sender (expert) -- receiver (decision maker) game, where the sender is fully informed about the state ofshow more
  • 10 May 2021 Conference Paper International Conference on Computer Communications (IEEE INFOCOM)

    Multicast Communications with Varying Bandwidth Constraints

    To find a maximum number of communication requests that can be satisfied concurrently, is a fundamental network scheduling problem. In this work, we investigate the problem of finding a maximum number of multicast requests that can be scheduled simultaneously in a tree network in which the edges and links have heterogeneous bandwidth limitations. This problem generalizesshow more
  • 9 May 2021 Preprint arXiv

    CausaLM: Causal Model Explanation Through Counterfactual Language Models

    Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all ML-based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might beshow more
  • 7 May 2021 Journal Article Production and Operations Management

    Two‐Phase Newsvendor with Optimally Timed Additional Replenishment: Model, Algorithm, Case Study

    Dina Smirnov , Yale T Herer , Assaf Avrahami
    Recent advancements in Information Technology have provided an opportunity to significantly improve the effectiveness of inventory systems. The use of in-cycle demand information enables faster reaction to demand fluctuations. In particular, for the newsvendor (NV) system, we exploit the newly available data to perform an additional review (AR) of inventory at anshow more
  • 6 May 2021 Conference Paper CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

    The Effects of Warmth and Competence Perceptions on Users' Choice of an AI System

    People increasingly rely on Artificial Intelligence (AI) based systems to aid decision-making in various domains and often face a choice between alternative systems. We explored the effects of users' perception of AI systems' warmth (perceived intent) and competence (perceived ability) on their choices. In a series of studies, we manipulated AI systems' warmth andshow more
  • 1 May 2021 Journal Article Evidence-Based Mental Health

    Linking the Mini-Mental State Examination, the Alzheimer’s Disease Assessment Scale-Cognitive Subscale and the Severe Impairment Battery: evidence from individual participant …

    Stephen Z Levine , Kazufumi Yoshida , Yair Goldberg , Myrto Samara , Andrea Cipriani , Orestis Efthimiou , Takeshi Iwatsubo , Stefan Leucht , Toshi A Furukawa
    Background The Mini-Mental State Examination (MMSE), the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) and the Severe Impairment Battery (SIB) are widely used rating scales to assess cognition in Alzheimer’s disease. Objective To understand the correspondence between these rating scales, we aimed to examine the linkage of MMSE withshow more
  • 1 May 2021 Journal Article Communications in Mathematical Physics

    The Speed of a Random Front for Stochastic Reaction–Diffusion Equations with Strong Noise

    Carl Mueller , Leonid Mytnik , Lenya Ryzhik
    We study the asymptotic speed of a random front for solutions \(u_t(x)\) to stochastic reaction-diffusion equations of the form
    $$\begin{aligned} \partial _tu=\frac{1}{2}\partial _x^2u+f(u)+\sigma \sqrt{u(1-u)}{\dot{W}}(t,x),~t\ge 0,~x\in {\mathbb {R}}, \end{aligned}$$
    arising in population genetics. Here, f is a continuous function with \(f(0)=f(1)=0\)
    show more
  • 29 Apr 2021 Journal Article Transactions of the Association for Computational Linguistics

    Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages

    Edoardo Maria Ponti , Ivan Vulić , Ryan Cotterell , Marinela Parovic , Roi Reichart , Anna Korhonen
    Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task–language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of neural parameters. Weshow more
  • 24 Apr 2021 Preprint medRxiv

    Protection of previous SARS-CoV-2 infection is similar to that of BNT162b2 vaccine protection: A three-month nationwide experience from Israel

    Yair Goldberg , Micha Mandel , Woodbridge Y , Ronen Fluss , Ilya Novikov , Rami Yaari , Arnona Ziv , Laurence S Freedman , Amit Huppert
    Worldwide shortage of vaccination against SARS-CoV-2 infection while the pandemic is still uncontrolled leads many states to the dilemma whether or not to vaccinate previously infected persons. Understanding the level of protection of previous infection compared to that of vaccination is critical for policy making. We analyze an updated individual-level database of theshow more
  • 22 Apr 2021 Journal Article Operations Research

    Technical Note—Two-Stage Sample Robust Optimization

    Dimitris Bertsimas , Shimrit Shtern , Bradley Sturt
    We investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-∞ Wasserstein ambiguity set. Our main result establishes that this approximation scheme is asymptotically optimal for two-stage stochastic linear optimization problems; that is, under mildshow more
  • 21 Apr 2021 Journal Article Dynamic Games and Applications

    Privacy, Patience, and Protection

    Ronen Gradwohl , Rann Smorodinsky
    We analyze repeated games in which players have private information about their levels of patience and in which they would like to maintain the privacy of this information vis-a-vis third parties. We show that privacy protection in the form of shielding players’ actions from outside observers is harmful, as it limits and sometimes eliminates the possibility of attainingshow more
  • 19 Apr 2021 Journal Article Nature Medicine

    COVID-19 dynamics after a national immunization program in Israel

    Hagai Rossman , Smadar Shilo , Tomer Meir , Malka Gorfine , Uri Shalit , Eran Segal
    Studies on the real-life effect of the BNT162b2 vaccine for Coronavirus Disease 2019 (COVID-19) prevention are urgently needed. In this study, we conducted a retrospective analysis of data from the Israeli Ministry of Health collected between 28 August 2020 and 24 February 2021. We studied the temporal dynamics of the number of new COVID-19 cases and hospitalizationsshow more
  • 26 Mar 2021 Journal Article Nature Communications

    Hospital load and increased COVID-19 related mortality in Israel

    Hagai Rossman , Tomer Meir , Jonathan Somer , Smadar Shilo , Rom Gutman , Asaf Ben Arie , Eran Segal , Uri Shalit , Malka Gorfine
    The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases. Here, we study the ramifications of hospital load due to COVID-19 morbidity on in-hospital mortality of patients with COVID-19 by analyzing records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-Januaryshow more
  • 22 Mar 2021 Preprint Social Science Research Network

    Information Aggregation in Large Collective Purchases

    We study a monopolist that uses the following scheme to gauge market traction for its common-value, excludible product. The monopolist offers its product at a given price, and each potential consumer decides whether to buy it. The contributions are collected. The product is supplied only if the total demand exceeds some threshold set by the monopolist, as is common inshow more
  • 18 Mar 2021 Journal Article Metacognition and Learning

    Metacognitive control processes in question answering: help seeking and withholding answers

    Monika Undorf , Iris Livneh , Rakefet Ackerman
    When responding to knowledge questions, people monitor their confidence in the knowledge they retrieve from memory and strategically regulate their responses so as to provide answers that are both correct and informative. The current study investigated the association between subjective confidence and the use of two response strategies: seeking help and withholdingshow more
  • 18 Mar 2021 Conference Paper International Conference on Artificial Intelligence and Statistics (AISTATS)

    Revisiting Projection-free Online Learning: the Strongly Convex Case

    Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent years due to their ability to handle convex constraints that are popular in many applications, but for which computing projections is often computationally impractical in high-dimensional settingsshow more
  • 18 Mar 2021 Preprint arXiv

    Exploiting Isomorphic Subgraphs in SAT

    Alexander Ivrii , Ofer Strichman
    While static symmetry breaking has been explored in the SAT community for decades, only as of 2010 research has focused on exploiting the same discovered symmetry dynamically, during the run of the SAT solver, by learning extra clauses. The two methods are distinct and not compatible. The former prunes solutions, whereas the latter does not--it only prunes areas of theshow more
  • 15 Mar 2021 Journal Article Creativity Research Journal

    Flexible Semantic Network Structure Supports the Production of Creative Metaphor

    Yangping Li , Yoed N Kenett , Weiping Hu , Roger E Beaty
    Metaphors are a common way to express creative language, yet the cognitive basis of figurative language production remains poorly understood. Previous studies found that higher creative individuals can better comprehend novel metaphors, potentially due to a more flexible semantic memory network structure conducive to remote conceptual combination. The present studyshow more
  • 15 Mar 2021 Preprint arXiv

    Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy

    BR Chernyak , B Raj , Tamir Hazan , Joseph Keshet
    This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models, with minimal loss of standard accuracy. We suggest creating a neighborhood around each training example, such that the label is kept constant for all inputs within that neighborhood. Unlike previous work that follows a similarshow more
  • 5 Mar 2021 Journal Article Applied Sciences

    Improving Conceptual Modeling with Object-Process Methodology Stereotypes

    As system complexity is on the rise, there is a growing need for standardized building blocks to increase the likelihood of systems’ success. Conceptual modeling is the primary activity required for engineering systems to be understood, designed, and managed. Modern modeling languages enable describing the requirements and design of systems in a formal yet understandableshow more
  • 3 Mar 2021 Conference Paper ACM Conference on Fairness, Accountability, and Transparency (FAcct)

    Corporate Social Responsibility via Multi-Armed Bandits

    Tom Ron , Omer Ben-Porat , Uri Shalit
    We propose a multi-armed bandit setting where each arm corresponds to a subpopulation, and pulling an arm is equivalent to granting an opportunity to this subpopulation. In this setting the decision-maker's fairness policy governs the number of opportunities each subpopulation should receive, which typically depends on the (unknown) reward from granting an opportunityshow more
  • 1 Mar 2021 Journal Article IEEE Systems Journal

    The Model Fidelity Hierarchy: From Text to Conceptual, Computational, and Executable Model

    Models have traditionally been mostly either prescriptive, expressing the function, structure and behavior of a system-to-be, or descriptive, specifying a system so it can be understood and analyzed. In this work, we offer a third kind—diagnostic models. We have built a model for assessing potential pediatric failure to thrive (FTT) during the perinatal stage. Althoughshow more
  • 26 Jan 2021 Preprint Conference of the European Chapter of the Association for Computational Linguistics

    Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification

    Yi Zhu , Ehsan Shareghi , Yingzhen Li , Roi Reichart , Anna Korhonen
    Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled data shortage. To bridge this gap, we combineshow more
  • 22 Jan 2021 Journal Article Mathematics of Operations Research

    A General Analysis of Sequential Social Learning

    Itai Arieli , Manuel Mueller-Frank
    This paper analyzes a sequential social learning game with a general utility function, state, and action space. We show that asymptotic learning holds for every utility function if and only if signals are totally unbounded, that is, the support of the private posterior probability of every event contains both zero and one. For the case of finitely many actions, weshow more
  • 1 Jan 2021 Conference Paper Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS)

    Situated Temporal Planning Using Deadline-aware Metareasoning

    Shahaf S Shperberg , Andrew Coles , Erez Karpas , Wheeler Ruml , Solomon Eyal Shimony
    We address the problem of situated temporal planning, in which an agent's plan can depend on scheduled exogenous events, and thus it becomes important to take the passage of time into account during the planning process. Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associatedshow more
  • 1 Jan 2021 Conference Paper National Conference on Artificial Intelligence

    Automatic Generation of Flexible Plans via Diverse Temporal Planning

    Robots operating in the real world must deal with uncertainty, be it due to working with humans who are unpredictable, or simply because they must operate in a dynamic environment. Ignoring the uncertainty is dangerous, while accounting for all possible outcomes is often computationally infeasible. One approach, which lies between ignoring the uncertainty completelyshow more