Title
Topic
-
‘Disaster World: Decision-Theoretic Agents for Simulating Population Responses to Hurricanes’
“Artificial intelligence (AI) research provides a rich source of modeling languages capable of generating socially plausible simulations of human behavior, while also providing a transparent ground truth that can support validation of social-science methods applied to that simulation. In this work, we leverage two established AI representations: decision-theoretic planning and recursive modeling. … We used PsychSim, a multiagent social-simulation framework combining these two AI frameworks, to build a general parameterized model of human behavior during disaster response, grounding the model in social-psychological theories to ensure social plausibility.” Find the paper and the full list of authors at Computational and Mathematical Organization…
-
‘Effectiveness of Teamwork-Level Interventions Through Decision-Theoretic Reasoning in a Minecraft Search-and-Rescue Task’
“Autonomous agents offer the promise of improved human teamwork through automated assessment and assistance during task performance. Studies of human teamwork have identified various processes that underlie joint task performance, while abstracting away the specifics of the task.We present here an agent that focuses exclusively on teamwork-level variables in deciding what interventions to use in assisting a human team. Our agent … relies on input from analytic components (ACs) (developed by other research teams) that process environmental information and output only teamwork-relevant measures.” Find the paper and authors list in the 2023 International Conference on Autonomous Agents and Multiagent Systems…
-
‘Agent-Based Modeling of Human Decision-Makers Under Uncertain Information During Supply Chain Shortages’
“In recent years, product shortages caused by supply chain disruptions have generated problems for consumers worldwide. … Understanding how humans learn to interpret information from others and how it influences their decision-making is key to alleviating supply chain shortages. In this work, we investigated how downstream supply chain echelons, health centers in pharmaceutical supply chains, interpret and use manufacturers’ estimated resupply date (ERD) information during drug shortages.” Find the paper and the full list of authors in the 2023 International Conference on Autonomous Agents and Multiagent Systems proceedings.
-
‘Scaling Up and Stabilizing Differentiable Planning with Implicit Differentiation’
“Differentiable planning promises end-to-end differentiability and adaptivity. However, an issue prevents it from scaling up to larger-scale problems: they need to differentiate through forward iteration layers to compute gradients, which couples forward computation and backpropagation and needs to balance forward planner performance and computational cost of the backward pass. … We propose to differentiate through the Bellman fixed-point equation to decouple forward and backward passes for Value Iteration Network and its variants, which enables constant backward cost (in planning horizon) and flexible forward budget and helps scale up to large tasks.” Find the paper and full list of authors at Open…
-
‘When Fair Classification Meets Noisy Protected Attributes’
“The operationalization of algorithmic fairness comes with several practical challenges, … [including] the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments may prevent the collection and use of demographic data, making it difficult to ensure algorithmic fairness. … recent proposals aim to achieve algorithmic fairness in classification by incorporating noisiness in protected attributes or not using protected attributes at all. … Our study reveals that attribute-blind and noise-tolerant fair classifiers can potentially achieve similar level of performance as attribute-reliant algorithms, even when protected attributes are noisy.” Find the paper and full list of…
-
‘Malicious Selling Strategies in Livestream E-Commerce: A Case Study of Alibaba’s Taobao and ByteDance’s TikTok’
“We sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao and TikTok. We identified 16 malicious selling strategies that were used to deceive, coerce, or manipulate viewers and found that platform designs enhanced nine of the malicious selling strategies. Second, through an interview study with 13 viewers, we report three challenges of overcoming malicious selling in relation to imbalanced power between viewers, streamers, and the platforms.” Find the paper and full list of authors at ACM Transactions on Computer-Human Interactions.
-
‘Towards Unbiased Exploration in Partial Label Learning’
“We consider learning a probabilistic classifier from partially-labelled supervision (inputs denoted with multiple possibilities) using standard neural architectures with a softmax as the final layer. We identify a bias phenomenon that can arise from the softmax layer in even simple architectures that prevents proper exploration of alternative options, making the dynamics of gradient descent overly sensitive to initialisation. We introduce a novel loss function that allows for unbiased exploration within the space of alternative outputs.” Find the paper and the full list of authors at ArXiv.
-
‘Improved Learning-Augmented Algorithms for k-Means and k-Medians Clustering’
“We consider the problem of clustering in the learning-augmented setting. We are given a data set in d-dimensional Euclidean space, and a label for each data point given by a predictor indicating what subsets of points should be clustered together. … For a dataset of size m, we propose a deterministic k-means algorithm that produces centers with aimproved bound on the clustering cost compared to the previous randomized state-of-the-art algorithm while preserving the O(dm log m) runtime.” Find the paper and the full list of authors at Open Review.
-
‘Scheduling Under Non-Uniform Job and Machine Delays’
“We study the problem of scheduling precedence-constrained jobs on heterogenous machines in the presence of non-uniform job and machine communication delays. We are given a set of n unit size precedence-ordered jobs, and a set of m related machines each with size m_i (machine i can execute at most m_i jobs at any time). … The objective is to construct a schedule that minimizes makespan, … the maximum completion time over all jobs. We consider schedules which allow duplication of jobs as well as schedules which do not.” Find the paper and full list of authors at Dagstuhl Research Online…
-
‘ThreadLock: Native Principal Isolation Through Memory Protection Keys’
“Inter-process isolation has been deployed in operating systems for decades, but secure intra-process isolation remains an active research topic. Achieving secure intra-process isolation within an operating system process is notoriously difficult. However, viable solutions that securely consolidate workloads into the same process have the potential to be extremely valuable. In this work, we present native principal isolation, a technique to restrict threads’ access to process memory by enforcing intra-process security policies defined over a program’s application binary interface (ABI).” Find the paper and full list of authors in the 2023 ACM Asia Conference on Computer and Communications Security proceedings.
-
‘Flourishing in the Everyday: Moving Beyond Damage-Centered Design in HCI for BIPOC Communities’
“Research and design in human-computer interaction centers problem-solving, causing a downstream effect of framing work with and for marginalized communities predominantly from the lens of deficit and damage. … However, we observe an additional need to center positive aspects of humanity, such as joy, pleasure, rest, and cultural heritage, particularly for Black, Indigenous, and People of Color. In this paper, we present three case studies of existing technologies that center BIPOC flourishing to provide an alternative path for HCI.” Find the paper and the full list of authors in the 2023 ACM Designing Interactive Systems Conference proceedings.
-
‘That’s a Tough Call: Studying the Challenges of Call Graph Construction for WebAssembly’
“WebAssembly is a low-level bytecode format that powers applications and libraries running in browsers, on the server side, and in standalone runtimes. Call graphs are at the core of many interprocedural static analysis and optimization techniques. However, WebAssembly poses some unique challenges for static call graph construction. … This paper presents the first systematic study of WebAssembly-specific challenges for static call graph construction and of the state-of-the-art in call graph analysis.” Find the paper and the full list of authors in the Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis.
-
‘Systematic Comparisons Between Lyme Disease and Post-Treatment Lyme Disease Syndrome in the U.S. With Administrative Claims Data’
“Post-treatment Lyme disease syndrome (PTLDS) is used to describe Lyme disease patients who have the infection cleared by antibiotic but then experienced persisting symptoms of pain, fatigue, or cognitive impairment. Currently, little is known about the cause or epidemiology of PTLDS. … We conducted a data-driven study with a large nationwide administrative dataset, which consists of more than 98 billion billing and 1.4 billion prescription records between 2008 and 2016, to identify unique aspects of PTLDS that could have diagnostic and etiologic values.” Find the paper and the full list of authors at EBioMedicine.
-
‘New Sampling Lower Bounds via the Separator’
“Suppose that a target distribution can be approximately sampled by a low-depth decision tree, or more generally by an efficient cell-probe algorithm. It is shown to be possible to restrict the input to the sampler so that its output distribution is still not too far from the target distribution, and at the same time many output coordinates are almost pairwise independent. This new tool is then used to obtain several new sampling lower bounds and separations, including a separation between AC0 and low-depth decision trees, and a hierarchy theorem for sampling.”
-
‘On Correlation Bounds Against Polynomials’
“We study the fundamental challenge of exhibiting explicit functions that have small correlation with low-degree polynomials over 𝔽₂. Our main contributions include: …2) We propose a new approach for proving correlation bounds with the central ‘mod functions.’ …3) We prove our conjecture for quadratic polynomials. … We express correlation in terms of directional derivatives and analyze it by slowly restricting the direction.4) We make partial progress on the conjecture for cubic polynomials, in particular proving tight correlation bounds for cubic polynomials whose degree-3 part is symmetric.” Find the paper and full list of authors at the Dagstuhl Research Online Publication Server.
-
‘Integrating Symmetry into Differentiable Planning with Steerable Convolutions’
“In this paper, we study a principled approach on incorporating group symmetry into end-to-end differentiable planning algorithms and explore the benefits of symmetry in planning. To achieve this, we draw inspiration from equivariant convolution networks and model the path planning problem as a set of signals over grids.” Find the paper and the full list of authors at Open Review.
-
‘Symmetries, Flat Minima and the Conserved Quantities of Gradient Flow’
“Empirical studies of the loss landscape of deep networks have revealed that many local minima are connected through low-loss valleys. Yet, little is known about the theoretical origin of such valleys. We present a general framework for finding continuous symmetries in the parameter space, which carve out low-loss valleys. Our framework uses equivariances of the activation functions and can be applied to different layer architectures.” Find the paper and the full list of authors at Open Review.
-
‘The Surprising Effectiveness of Equivariant Models in Domains With Latent Symmetry’
“Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture. These applications typically assume that the domain symmetry is fully described by explicit transformations of the model inputs and outputs. However, many real-life applications contain only latent or partial symmetries which cannot be easily described by simple transformations of the input. … We discover, surprisingly, that imposing equivariance constraints that do not exactly match the domain symmetry is very helpful in learning the true symmetry.” Find the paper and full list of authors at Open Review.
-
‘Equivariant Single View Pose Prediction via Induced and Restricted Representations’
“Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would leverage the fact that objects can be rotated and translated in three dimensions to make predictions about novel images. However, imposing SO(3)-equivariance on two-dimensional inputs is difficult because the group of three-dimensional rotations does not have a natural action on the two-dimensional plane. … We show that an algorithm that learns a three-dimensional representation of the world from two dimensional images must satisfy certain geometric consistency properties.” Find the paper and full list of authors at ArXiv.
-
‘Location-Independent GNSS Relay Attacks: A Lazy Attacker’s Guide to Bypassing Navigation Message Authentication’
“In this work, we demonstrate the possibility of spoofing a GNSS receiver to arbitrary locations without modifying the navigation messages. … Prior work required an adversary to record the GNSS signals at the intended spoofed location and relay them to the victim receiver. Our attack demonstrates the ability of an adversary to receive signals close to the victim receiver and in real-time generate spoofing signals for an arbitrary location without modifying the navigation message contents.” Find the paper and full list of authors in the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks proceedings.
-
‘UE Security Reloaded: Developing a 5G Standalone User-Side Security Testing Framework’
“Security flaws and vulnerabilities in cellular networks lead to severe security threats given the data-plane services that are involved, from calls to messaging and Internet access. While the 5G Standalone (SA) system is currently being deployed worldwide, practical security testing of User Equipment (UE) has only been conducted and reported publicly for 4G/LTE and earlier network generations. In this paper, we develop and present the first open-source based security testing framework for 5G SA User Equipment.” Find the paper and the full list of authors in the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks proceedings.
-
‘Encrypted Databases Made Secure Yet Maintainable’
“State-of-the-art encrypted databases (EDBs) can be divided into two types: one that protects the whole DBMS engine in a trusted domain, and one that protects only operators that support queries over encrypted data. Both types have limitations when dealing with malicious database administrators (DBAs). The first type either exposes the data to DBAs or makes maintenance operations difficult if the DBA role is eliminated. The second type is vulnerable to abuse of the operator interfaces; … we devise a smuggle attack that enables DBAs to secretly and effectively access data.” Find the paper and full list of authors at USENIX.
-
‘Boosting Multitask Learning on Graphs Through Higher-Order Task Affinities’
“Predicting node labels on a given graph is a widely studied problem with many applications. … This paper considers predicting multiple node labeling functions on graphs simultaneously and revisits this problem from a multitask learning perspective. … Due to complex overlapping patterns, we find that negative transfer is prevalent when we apply naive multitask learning to multiple community detection, as task relationships are highly nonlinear across different node labeling. To address the challenge, we develop an algorithm to cluster tasks into groups based on a higher-order task affinity measure.” Find the paper and full list of authors at ArXiv.
-
‘Toward Computationally-Supported Roleplaying for Perspective-Taking’
“Designing and studying computationally-supported roleplaying for changing social perspectives of players is a complex and challenging problem. As indispensable components of roleplaying games (RPGs), narratives have the potential to promote successful perspective-taking. … We first present the design of a visual novel style RPG scenario addressing xenophobia and bullying, using an interactive narrative powered by a computational narrative engine. We then report on a usability evaluation of our interactive narrative system and an empirical evaluation of the RPG’s effectiveness in promoting successful perspective-taking through a crowdsourced online experiment.” Find the full list of authors in the International Conference on Human-Computer…
-
‘Summarizing, Simplifying and Synthesizing Medical Evidence Using GPT-3 (With Varying Success)’
“Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings. However, it is unclear if such models are similarly capable in more specialized, high-stakes domains such as biomedicine. In this paper, we enlist domain experts (individuals with medical training) to evaluate summaries of biomedical articles generated by GPT-3, given zero supervision. … We design an annotation scheme for evaluating model outputs, with an emphasis on assessing the factual accuracy of generated summaries.” Find the paper and the full list of authors at ArXiv.