Title
Topic
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‘Ecosystem-Level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes’
“Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models. In practice, the societal impact of machine learning is determined by the surrounding context of machine learning deployments. To capture this, we introduce ecosystem-level analysis: rather than analyzing a single model, we consider the collection of models that are deployed in a given context.” Find the paper and full list of authors at ArXiv.
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‘Random Oracle Combiners: Breaking the Concatenation Barrier for Collision-Resistance’
“Suppose two parties have hash functions h1 and h2 respectively, but each only trusts the security of their own. We wish to build a hash combiner Cʰ¹,ʰ² which is secure so long as either one of the underlying hash functions is. … In this case, concatenating the two hash outputs clearly works. Unfortunately, a long series of works … showed no (noticeably) shorter combiner for collision resistance is possible. … We argue the right formulation of the “hash combiner” is what we call random oracle (RO) combiners.” Find the paper and full list of authors at Cryptology ePrint Archive.
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‘Security With Functional Re-Encryption From CPA
“The notion of functional re-encryption security (funcCPA) for public-key encryption schemes was recently introduced by Akavia et al. (TCC’22), in the context of homomorphic encryption. This notion lies in between CPA security and CCA security: we give the attacker a functional re-encryption oracle instead of the decryption oracle of CCA security. … In this work we observe that funcCPA security may have applications beyond homomorphic encryption and set out to study its properties.” Find the paper and full list of authors at Cryptology ePrint Archive.
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‘BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation’
“We address the task of supervised action segmentation which aims to partition a video into non-overlapping segments, each representing a different action. Recent works apply transformers to perform temporal modeling at the frame-level, which suffer from high computational cost and cannot well capture action dependencies over long temporal horizons. To address these issues, we propose an efficient BI-level Temporal modeling (BIT) framework that learns explicit action tokens to represent action segments, in parallel performs temporal modeling on frame and action levels, while maintaining a low computational cost.” Find the paper and full list of authors at ArXiv.
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‘Real-Time Neural Light Field on Mobile Devices’
“Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow. … Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. … In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering.” Find the paper and full list of authors at ArXiv.
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‘Latent Graph Inference With Limited Supervision’
“Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node representations from data features. However, existing LGI methods commonly suffer from the issue of supervision starvation, where massive edge weights are learned without semantic supervision. … In this paper, we observe that this issue is actually caused by the graph sparsification operation, which severely destroys the important connections established between pivotal nodes and labeled ones. To address this, we propose to restore the corrupted affinities and replenish the missed supervision for better LGI.” Find the paper and authors list at ArXiv.
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‘A Tutorial on Visual Representations of Relational Queries’
“Query formulation is increasingly performed by systems that need to guess a user’s intent. … But how can a user know that the computational agent is returning answers to the “right” query? More generally, given that relational queries can become pretty complicated, how can we help users understand existing relational queries, whether human-generated or automatically generated? Now seems the right moment to revisit a topic that predates the birth of the relational model: developing visual metaphors that help users understand relational queries. This lecture-style tutorial surveys the key visual metaphors developed for visual representations of relational expressions.”
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‘Pixel-Aligned Recurrent Queries for Multi-View 3D Object Detection’
“We present PARQ – a multi-view 3D object detector with transformer and pixel-aligned recurrent queries. Unlike previous works that use learnable features or only encode 3D point positions as queries in the decoder, PARQ leverages appearance-enhanced queries initialized from reference points in 3D space and updates their 3D location with recurrent cross-attention operations. Incorporating pixel-aligned features and cross attention enables the model to encode the necessary 3D-to-2D correspondences and capture global contextual information of the input images.” Find the paper and full list of authors at ArXiv.
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‘SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation’
“Human-centric video frame interpolation has great potential for improving people’s entertainment experiences and finding commercial applications in the sports analysis industry. … Although there are multiple benchmark datasets available in the community, none of them is dedicated for human-centric scenarios. To bridge this gap, we introduce SportsSloMo, a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution (≥720p) slow-motion sports videos crawled from YouTube. We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets.” Find the paper and full list of authors at ArXiv.
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‘Social Functions of Machine Emotional Expressions’
“Virtual humans and social robots frequently generate behaviors that human observers naturally see as expressing emotion. In this review article, we highlight that these expressions can have important benefits for human–machine interaction. We first summarize the psychological findings on how emotional expressions achieve important social functions in human relationships and highlight that artificial emotional expressions can serve analogous functions in human–machine interaction. We then review computational methods for determining what expressions make sense to generate within the context of interaction and how to realize those expressions across multiple modalities.” Find the paper and full list of authors in IEEE Proceedings.
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‘Large Language Models in Textual Analysis for Gesture Selection’
“Gestures perform a variety of communicative functions that powerfully influence human face-to-face interaction. … Approaches to automatic gesture generation vary not only in the degree to which they rely on data-driven techniques but also the degree to which they can produce context and speaker specific gestures. However, these approaches face two major challenges. … Here, we approach these challenges by using large language models (LLMs) to show that these powerful models of large amounts of data can be adapted for gesture analysis and generation.” Find the paper and full list of authors in the 25th International Conference on Multimodal Interaction…
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‘Blend: A Unified Data Discovery System’
“Data discovery is an iterative and incremental process that necessitates the execution of multiple data discovery queries to identify the desired tables from large and diverse data lakes. Current methodologies concentrate on single discovery tasks such as join, correlation, or union discovery. However, in practice, a series of these approaches and their corresponding index structures are necessary. … This paper presents BLEND, a comprehensive data discovery system that empowers users to develop ad-hoc discovery tasks without the need to develop new algorithms or build a new index structure.” Find the paper and full list of authors at ArXiv.
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‘Detecting Receptivity for mHealth Interventions’
“Just-In-Time Adaptive Interventions (JITAI) have the potential to provide effective support for health behavior by delivering the right type and amount of intervention at the right time. … Previous research has explored the association of context and user-specific traits on receptivity and built machine-learning models to detect receptivity after the study was completed. However, for effective intervention delivery, JITAI systems need to make in-the-moment decisions about a user’s receptivity. In this study, we deployed machinelearning models in a chatbot-based digital coach to predict receptivity for physical-activity interventions.” Find the paper and full list of authors in SIGMOBILE.
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‘Modeling Self-Propagating Malware With Epidemiological Models’
“Self-propagating malware (SPM) is responsible for large financial losses and major data breaches with devastating social impacts that cannot be understated. Well-known campaigns such as WannaCry and Colonial Pipeline have been able to propagate rapidly on the Internet and cause widespread service disruptions. To date, the propagation behavior of SPM is still not well understood. … Here, we address this gap by performing a comprehensive analysis of a newly proposed epidemiological-inspired model for SPM propagation, the Susceptible-Infected-Infected Dormant-Recovered (SIIDR) model.” Find the paper and full list of authors at Applied Network Science.
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‘Latent Space Symmetry Discovery’
“Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data. However, existing symmetry discovery methods are limited to linear symmetries in their search space and cannot handle the complexity of symmetries in real-world, often high-dimensional data. We propose a novel generative model, Latent LieGAN (LaLiGAN), which can discover nonlinear symmetries from data. It learns a mapping from data to a latent space where the symmetries become linear and simultaneously discovers symmetries in the latent space.” Find the paper and list of authors at ArXiv.
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‘ICML 2023 Topological Deep Learning Challenge : Design and Results’
“This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings.” Find the paper and full list of authors at ArXiv.
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‘Chameleon: Increasing Label-Only Membership Leakage With Adaptive Poisoning’
“The integration of machine learning (ML) in numerous critical applications introduces a range of privacy concerns for individuals who provide their datasets for model training. One such privacy risk is Membership Inference (MI), in which an attacker seeks to determine whether a particular data sample was included in the training dataset. … MI attacks capitalize on access to the model’s predicted confidence scores to successfully perform membership inference, and employ data poisoning to further enhance their effectiveness. … We show that existing label-only MI attacks are ineffective at inferring membership.” Find the paper and full list of authors at ArXiv.
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Panel discussion for CSCW ’23: ‘Getting Data for CSCW Research’
“This panel will bring together a group of scholars from diverse methodological backgrounds to discuss critical aspects of data collection for CSCW research. This discussion will consider the rapidly evolving ethical, practical, and data access challenges, examine the solutions our community is currently deploying and envision how to ensure vibrant CSCW research going forward.” Find the full list of panelists in the Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing.
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‘Lower Bounds on Anonymous Whistleblowing’
“Anonymous transfer … allows a sender to leak a message anonymously by participating in a public non-anonymous discussion where everyone knows who said what. … The work of [ACM22] presented a lower bound on anonymous transfer, ruling out constructions with strong anonymity guarantees. … They also provided a (heuristic) upper bound, giving a scheme with weak anonymity guarantees. … In this work, we present improved lower bounds on anonymous transfer, that rule out both of the above possibilities.” Find the paper and full list of authors at the Cryptology ePrint Archive.
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‘Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems’
“Deep equivariant models use symmetries to improve sample efficiency and generalization. However, the assumption of perfect symmetry in many of these models can sometimes be restrictive, especially when the data does not perfectly align with such symmetries. Thus, we introduce relaxed octahedral group convolution for modeling 3D physical systems in this paper. This flexible convolution technique provably allows the model to both maintain the highest level of equivariance that is consistent with data and discover the subtle symmetry-breaking factors in the physical systems.” Find the paper and full list of authors at ArXiv.
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‘An Example of (Too Much) Hyper-Parameter Tuning in Suicide Ideation Detection’
“This work starts with the TWISCO baseline, a benchmark of suicide-related content from Twitter. We find that hyper-parameter tuning can improve this baseline by 9%. We examined 576 combinations of hyper-parameters: learning rate, batch size, epochs and date range of training data. Reasonable settings of learning rate and batch size produce better results than poor settings.” Find the paper and full list of authors in the Proceedings of the International AAAI Conference on Web and Social Media.
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‘Unified Concept Editing in Diffusion Models’
“Text-to-image models suffer from various safety issues that may limit their suitability for deployment. Previous methods have separately addressed individual issues of bias, copyright and offensive content in text-to-image models. However, in the real world, all of these issues appear simultaneously in the same model. We present a method that tackles all issues with a single approach. Our method, Unified Concept Editing (UCE), edits the model without training using a closed-form solution and scales seamlessly to concurrent edits on text-conditional diffusion models.” Find the paper and full list of authors at ArXiv.
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‘A Function Interpretation Benchmark for Evaluating Interpretability Methods’
“Labeling neural network submodules with human-legible descriptions is useful for many downstream tasks: such descriptions can surface failures, guide interventions, and perhaps even explain important model behaviors. To date, most mechanistic descriptions of trained networks have involved small models, narrowly delimited phenomena, and large amounts of human labor. Labeling all human-interpretable sub-computations in models of increasing size and complexity will almost certainly require tools that can generate and validate descriptions automatically. … This paper introduces FIND (Function INterpretation and Description), a benchmark suite for evaluating the building blocks of automated interpretability methods.” Find the paper and authors list at ArXiv.
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‘The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing and Coloring in Bar Charts’
“Data visualizations present a massive number of potential messages to an observer. … The message that a viewer tends to notice — the message that a visualization ‘affords’ — is strongly affected by how values are arranged in a chart, e.g., how the values are colored or positioned. … We present a set of empirical evaluations of how different messages … are afforded by variations in ordering, partitioning, spacing and coloring of values, within the ubiquitous case study of bar graphs. In doing so, we introduce a quantitative method that is easily scalable, reviewable and replicable.” Find the paper and…
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‘Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules’
“Automated game design (AGD), the study of automatically generating game rules, has a long history in technical games research. AGD approaches generally rely on approximations of human play, either objective functions or AI agents. Despite this, the majority of these approximators are static, meaning they do not reflect human player’s ability to learn and improve in a game. In this paper, we investigate the application of Reinforcement Learning (RL) as an approximator for human play for rule generation.” Find the paper and full list of authors at ArXiv.
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‘E(2)-Equivariant Graph Planning for Navigation’
“Learning for robot navigation presents a critical and challenging task. The scarcity and costliness of real-world datasets necessitate efficient learning approaches. In this letter, we exploit Euclidean symmetry in planning for 2D navigation, which originates from Euclidean transformations between reference frames and enables parameter sharing. To address the challenges of unstructured environments, we formulate the navigation problem as planning on a geometric graph and develop an equivariant message passing network to perform value iteration. Furthermore, to handle multi-camera input, we propose a learnable equivariant layer to lift features to a desired space.” Find the paper and authors list at ArXiv.
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‘GME: GPU-based Microarchitectural Extensions To Accelerate Homomorphic Encryption’
“Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. … Despite its promise of strong data privacy and security guarantees, FHE introduces a slowdown of up to five orders of magnitude as compared to the same computation using plaintext data. This overhead is presently a major barrier to the commercial adoption of FHE. In this work, we leverage GPUs to accelerate FHE, capitalizing on a well-established GPU ecosystem available in the cloud.” Find the paper and full list of authors at ArXiv.
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‘Dropout Attacks’
“Dropout is a common operator in deep learning, aiming to prevent overfitting by randomly dropping neurons during training. This paper introduces a new family of poisoning attacks against neural networks named DROPOUTATTACK. DROPOUTATTACK attacks the dropout operator by manipulating the selection of neurons to drop instead of selecting them uniformly at random. We design, implement, and evaluate four DROPOUTATTACK variants that cover a broad range of scenarios. These attacks can slow or stop training, destroy prediction accuracy of target classes, and sabotage either precision or recall of a target class.” Find the paper and full list of authors at ArXiv.
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‘O(k) -Equivariant Dimensionality Reduction on Stiefel Manifolds’
“Many real-world datasets live on high-dimensional Stiefel and Grassmannian manifolds, Vk(ℝN) and Gr(k,ℝN) respectively, and benefit from projection onto lower-dimensional Stiefel (respectively, Grassmannian) manifolds. In this work, we propose an algorithm called Principal Stiefel Coordinates (PSC) to reduce data dimensionality from Vk(ℝN) to Vk(ℝn) in an O(k)-equivariant manner (k≤n≪N).” Find the paper and full list of authors at ArXiv.