• Sep 2021 Journal Article Journal of Political Economy

## Feasible Joint Posterior Beliefs

Abstract
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 and
• 28 Aug 2021 Journal Article Annals of Mathematics and Artificial Intelligence

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

Abstract
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 is
• 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
Abstract
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 with
• 10 Aug 2021 Journal Article Operations Research Letters

## Robust learning in social networks via matrix scaling

Abstract
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 the
• 6 Aug 2021 Journal Article Journal of Economic Theory

Abstract
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 his
• Aug 2021 Conference Paper The 34th Annual Conference on Learning Theory (COLT 2021)

## Frank-Wolfe with Nearest Extreme Point Oracle

Dan Garber , Noam Wolf
Abstract
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 many
• 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
Abstract
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 model
• 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
Abstract
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 augmentations
• Aug 2021 Journal Article IEEE Transactions on Knowledge and Data Engineering

## Learning to Rerank Schema Matches

Avigdor Gal , Haggai Roitman , Roee Shraga
Abstract
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, known
• 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
Abstract
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 explored
• 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
Abstract
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 a
• 28 Jul 2021 Preprint arXiv

## From Monopoly to Competition: Optimal Contests Prevail

Xiaotie Deng , Yotam Gafni , Ron Lavi , Tao Lin , Hongyi Ling
Abstract
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) form
• 27 Jul 2021 Journal Article Trends in Cognitive Sciences

## To predict human choice, consider the context

Abstract
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 cognitive
• 27 Jul 2021 Conference Paper UAI 2021 : 37th Conference on Uncertainty in Artificial Intelligence

## Bandits with Partially Observable Confounded Data

Abstract
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 demonstrate
• 27 Jul 2021 Conference Paper UAI 2021 : 37th Conference on Uncertainty in Artificial Intelligence

## Approximate Implication with d-Separation

Abstract
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-inference
• 21 Jul 2021 Preprint arXiv

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

Yuval Emek , Eyal Keren
Abstract
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 in
• 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning, ICML

## Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization

Abstract
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 that
• 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
Abstract
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 a
• 21 Jul 2021 Preprint arXiv

## Efficient Deterministic Leader Election for Programmable Matter

Fabien Dufoulon , Shay Kutten , William K Moses
Abstract
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 a
• 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
Abstract
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 for
• 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
Abstract
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 discuss
• 18 Jul 2021 Conference Paper EC'21: Proceedings of the 22nd ACM Conference on Economics and Computation

## On Social Networks that Support Learning

Abstract
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 the
• 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
Abstract
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 applies
• 18 Jul 2021 Preprint arXiv

## Incomplete Information VCG Contracts for Common Agency

Abstract
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 inefficiency
• 18 Jul 2021 Preprint arXiv

## Regret-Minimizing Bayesian Persuasion

Abstract
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 perform
• 16 Jul 2021 Journal Article ACM SIGecom Exchanges

## Feasible joint posterior beliefs (through examples)

Abstract
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 distributions
• 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
Abstract
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-scale
• 13 Jul 2021 Journal Article Journal of Artificial Intelligence Research

## Representative Committees of Peers

Abstract
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 would
• 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

Abstract
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 searches
• 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
Abstract
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 does
• 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
Abstract
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 this
• 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
Abstract
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 a
• 22 Jun 2021 Conference Paper IEEE 37th International Conference on Data Engineering (ICDE)

## Learning to Characterize Matching Experts

Abstract
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 challenged
• 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
Abstract
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 tuples
• 15 Jun 2021 Journal Article Frontiers in Nutrition

## Promoting Healthy Eating Behaviors by Incentivizing Exploration of Healthy Alternatives

Y Shavit , Yefim Roth , Kinneret Teodorescu
Abstract
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 healthy
• 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

Abstract
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)
• 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
Abstract
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 shared
• 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
Abstract
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 examined
• 9 Jun 2021 Preprint arXiv

## Proportional Participatory Budgeting with Substitute Projects

Roy Fairstein , Reshef Meir , Kobi Gal
Abstract
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 that
• 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
Abstract
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 a
• 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
Abstract
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 were
• 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
Abstract
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 hypotheses
• 30 May 2021 Preprint arXiv

## Improving Efficiency of Tests for Composite Null Hypotheses

Abstract
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 becomes
• 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
Abstract
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. The
• 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
Abstract
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 susceptible
• 20 May 2021 Preprint arXiv

## A Fully Adaptive Self-Stabilizing Transformer for LCL Problems

Shimon Bitton , Yuval Emek , Taisuke Izumi , Shay Kutten
Abstract
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. The
• 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
Abstract
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 this
• 19 May 2021 Preprint arXiv

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

Abstract
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 by
• 18 May 2021 Edited Volume Robotics and Computer-integrated Manufacturing

## Agile robotics for industrial applications: Editorial

Craig Schlenoff , Zeid Kootbally , Erez Karpas
Abstract
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 promising
• 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

Abstract
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 paper
• 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

Abstract
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 social
• 11 May 2021 Preprint arXiv

## Designing an Automatic Agent for Repeated Language based Persuasion Games

Abstract
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 of
• 10 May 2021 Conference Paper International Conference on Computer Communications (IEEE INFOCOM)

## Multicast Communications with Varying Bandwidth Constraints

Abstract
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 generalizes
• 9 May 2021 Preprint arXiv

## CausaLM: Causal Model Explanation Through Counterfactual Language Models

Abstract
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 be
• 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
Abstract
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 an
• 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

Abstract
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 and
• 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
Abstract
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 with
• 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
Abstract
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 $$f(0)=f(1)=0$$
• 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
Abstract
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. We
• 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
Abstract
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 the
• 22 Apr 2021 Journal Article Operations Research

## Technical Note—Two-Stage Sample Robust Optimization

Dimitris Bertsimas , Shimrit Shtern , Bradley Sturt
Abstract
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 mild
• 21 Apr 2021 Journal Article Dynamic Games and Applications

## Privacy, Patience, and Protection

Abstract
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 attaining
• 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
Abstract
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 hospitalizations
• 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
Abstract
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-January
• 22 Mar 2021 Preprint Social Science Research Network

## Information Aggregation in Large Collective Purchases

Abstract
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 in
• 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
Abstract
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 withholding
• 18 Mar 2021 Conference Paper International Conference on Artificial Intelligence and Statistics (AISTATS)

## Revisiting Projection-free Online Learning: the Strongly Convex Case

Abstract
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 settings
• 18 Mar 2021 Preprint arXiv

## Exploiting Isomorphic Subgraphs in SAT

Alexander Ivrii , Ofer Strichman
Abstract
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 the
• 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
Abstract
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 study
• 15 Mar 2021 Preprint arXiv

## Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy

BR Chernyak , B Raj , Tamir Hazan , Joseph Keshet
Abstract
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 similar
• 5 Mar 2021 Journal Article Applied Sciences

## Improving Conceptual Modeling with Object-Process Methodology Stereotypes

Abstract
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 understandable
• 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
Abstract
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 opportunity
• 1 Mar 2021 Journal Article IEEE Systems Journal

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

Abstract
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. Although
• 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
Abstract
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 combine
• 22 Jan 2021 Journal Article Mathematics of Operations Research

## A General Analysis of Sequential Social Learning

Itai Arieli , Manuel Mueller-Frank
Abstract
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, we
• 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
Abstract
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 associated
• 1 Jan 2021 Conference Paper National Conference on Artificial Intelligence

## Automatic Generation of Flexible Plans via Diverse Temporal Planning

Abstract
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 completely