Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10129 publications
Relational Affect in Dyadic Interactions
CHI Conference on Human Factors in Computing Systems (2024)
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Relational affect is the affective response (encompassing emotion, expression, feeling) that emerges from an interaction between two people. The case study presented here introduces the concept of relational affect through a human perceptual rating task. Forty-five raters watched short video clips of two people interacting and described their perceived emotion of the individuals and that of the overall interaction. Our qualitative analysis of the rater responses showed that raters used a variety of schemes to reason about emotion, including expressions, context, and perceived appraisal of the event. These reasoning schemes were notably different for perceived individual emotion and relational affect. Our findings show that the vocabulary use for relational affect is distinct from that of individual emotion and relational affect as a phenomenon deepens our understanding of social interactions and moves the field a step closer to realizing the goal of fluid interactions between people and technology.
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Storage Systems For Real-Time Personalized Recommendations
Jayasekhar Konduru
Aqsa Fulara
DZone (2024)
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This article explores the demands of real-time personalized recommendation systems, focusing on data storage challenges and solutions. We'll present common storage solutions suitable for such systems and outline best practices.
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LabelMaker: Automatic Semantic Label Generation from RGB-D Trajectories
Silvan Weder
Hermann Blum
Francis Engelmann
Marc Pollefeys
3DV (2024)
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Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire. This work introduces a fully automated 2D/3D labeling framework that, without any human intervention, can generate labels for RGB-D scans at equal (or better) level of accuracy than comparable manually annotated datasets such as ScanNet. Our approach is based on an ensemble of state-of-the-art segmentation models and 3D lifting through neural rendering. We demonstrate the effectiveness of our LabelMaker pipeline by generating significantly better labels for the ScanNet datasets and automatically labelling the previously unlabeled ARKitScenes dataset. Code and models are available at https://labelmaker.org/
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UGIF-DataSet: A New Dataset for Cross-lingual, Cross-modal Sequential actions on the UI
Findings of the Association for Computational Linguistics: NAACL 2024
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Help documents are supposed to aid smartphone users in resolving queries such as "How to block calls from unknown numbers?". However, given a query, identifying the right help document, understanding instructions from the document, and using them to resolve the issue at hand is challenging. The user experience may be enhanced by converting the instructions in the help document to a step-by-step tutorial overlaid on the phone UI. Successful execution of this task requires overcoming research challenges in retrieval, parsing, and grounding in the multilingual-multimodal setting. For example, user queries in one language may have to be matched against instructions in another language, which in turn needs to be grounded in a multimodal UI in yet another language. Moreover, there isn’t any relevant dataset for such a task. In order to bridge this gap, we introduce UGIF-DataSet, a multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone, containing 4,184 tasks across 8 languages. The instruction steps in UGIF-DataSet are available only in English, so the challenge involves operations in the cross-modal, cross-lingual setting. We compare the performance of different large language models for this task and find that the end-to-end task completion rate drops from 48% in English to 32% for other languages, demonstrating significant overall headroom for improvement. We are hopeful that UGIF-DataSet and our analysis will aid further research on the important problem of sequential task completion in the multilingual and multimodal setting.
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Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach
Anant Modwal
Govind Kaushal
Ramanan Balakrishnan
Shanay Shah
Monu Agrawal
Justin Lin
Prakash Hariramani
Priya Mandawat
Rutvik Karve
Naveen Madiraju
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Digital payment systems have revolutionized financial transactions, offering unparalleled convenience and accessibility to users worldwide. However, the increasing popularity of these platforms has also attracted malicious actors seeking to exploit their vulnerabilities for financial gain. To address this challenge, robust and adaptable scam detection mechanisms are crucial for maintaining the trust and safety of digital payment ecosystems. This paper presents a comprehensive approach to scam detection, focusing on the Unified Payments Interface (UPI) in India, Google Pay (GPay) as a specific use case. The approach leverages Large Language Models (LLMs) to enhance scam classification accuracy and designs a digital assistant to aid human reviewers in identifying and mitigating fraudulent activities. The results demonstrate the potential of LLMs in augmenting existing machine learning models and improving the efficiency, accuracy, quality, and consistency of scam reviews, ultimately contributing to a safer and more secure digital payment landscape. Our evaluation of the Gemini Ultra model on curated transaction data showed a 93.33% accuracy in scam classification. Furthermore, the model demonstrated 89% accuracy in generating reasoning for these classifications. A promising fact, the model identified 32% new accurate reasons for suspected scams that human reviewers had not included in the review notes.
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PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard
Proceedings of the 41st International Conference on Machine Learning (2024), pp. 21410-21429
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This work studies algorithms for learning from aggregate responses. We focus on the construction of aggregation sets (called \emph{bags} in the literature) for event-level loss functions. We prove for linear regression and generalized linear models (GLMs) that the optimal bagging problem reduces to one-dimensional size-constrained $k$-means clustering. Further, we theoretically quantify the advantage of using curated bags over random bags. We propose the \texttt{PriorBoost} algorithm, which iteratively forms increasingly homogenous bags with respect to (unseen) individual responses to improve model quality. We also explore label differential privacy for aggregate learning, and provide extensive experiments that demonstrate that \PriorBoost regularly achieves optimal quality, in contrast to non-adaptive algorithms for aggregate learning.
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Reinforcement Learning-Enhanced Cloud-Based Open Source Analog Circuit Generator for Standard and Cryogenic Temperatures in 130-nm and 180-nm OpenPDKs
Ali Hammoud
Anhang Li
Ayushman Tripathi
Wen Tian
Harsh Khandeparkar
Ryan Wans
Boris Murmann
Dennis Sylvester
Mehdi Saligane
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This work introduces an open-source, Process Technology-agnostic framework for hierarchical circuit netlist, layout, and Reinforcement Learning (RL) optimization. The layout, netlist, and optimization python API is fully modular and publicly installable via PyPI. It features a bottom-up hierarchical construction, which allows for complete design reuse across provided PDKs. The modular hierarchy also facilitates parallel circuit design iterations on cloud platforms. To illustrate its capabilities, a two-stage OpAmp with a 5T first-stage, commonsource second-stage, and miller compensation is implemented. We instantiate the OpAmp in two different open-source process design kits (OpenPDKs) using both room-temperature models and cryogenic (4K) models. With a human designed version as the baseline, we leveraged the parameterization capabilities of the framework and applied the RL optimizer to adapt to the power consumption limits suitable for cryogenic applications while maintaining gain and bandwidth performance. Using the modular RL optimization framework we achieve a 6x reduction in power consumption compared to manually designed circuits while maintaining gain to within 2%.
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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines
Aditya U Kale
Alastair Dennison
Alexander Martindale
An Wen Chan
Andrew Beam
Benjamin Ng
Cecilia S. Lee
Christopher Kelly
Christopher Yau
David Moher
Gary Collins
Lauren Oakden-Rayner
Lavinia Ferrante di Ruffano
Melanie Calvert
Melissa D McCradden
Pearse Keane
Robert Golub
Samantha Cruz Rivera
Victoria Ngai
Xiaoxuan Liu
Nature Communications (2024)
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The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
Alon Levkovitch
Roy Hirsch
Chulayuth Asawaroengchai
Ehud Rivlin
ICLR (2024)
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We present Spectron, a novel approach to adapting pre-trained large language models (LLMs) to perform spoken question answering (QA) and speech continuation. By endowing the LLM with a pre-trained speech encoder, our model becomes able to take speech inputs and generate speech outputs. The entire system is trained endto-end and operates directly on spectrograms, simplifying our architecture. Key to our approach is a training objective that jointly supervises speech recognition, text continuation, and speech synthesis using only paired speech-text pairs, enabling a ‘cross-modal’ chain-of-thought within a single decoding pass. Our method surpasses existing spoken language models in speaker preservation and semantic coherence. Furthermore, the proposed model improves upon direct initialization in retaining the knowledge of the original LLM as demonstrated through spoken QA datasets. We release our audio samples and spoken QA dataset via our website.
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Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions
Barbara Ikica
Hamidreza Alvari
Mehdi Hafezi Manshadi
(2024)
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The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data is scarce, highly private, or difficult to obtain. We investigate the feasibility of creating realistic, large-scale synthetic datasets of user-generated content, noting that such content is increasingly prevalent and a source of frequently sought information. Large language models (LLMs) offer a starting point for generating synthetic social media discussion threads, due to their ability to produce diverse responses that typify online interactions. However, as we demonstrate, straightforward application of LLMs yields limited success in capturing the complex structure of online discussions, and standard prompting mechanisms lack sufficient control. We therefore propose a multi-step generation process, predicated on the idea of creating compact representations of discussion threads, referred to as scaffolds. Our framework is generic yet adaptable to the unique characteristics of specific social media platforms. We demonstrate its feasibility using data from two distinct online discussion platforms. To address the fundamental challenge of ensuring the representativeness and realism of synthetic data, we propose a portfolio of evaluation measures to compare various instantiations of our framework.
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First Passage Percolation with Queried Hints
Kritkorn Karntikoon
Aaron Schild
Yiheng Shen
Ali Sinop
AISTATS (2024)
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Optimization problems are ubiquitous throughout the modern world. In many of these applications, the input is inherently noisy and it is expensive to probe all of the noise in the input before solving the relevant optimization problem. In this work, we study how much of that noise needs to be queried in order to obtain an approximately optimal solution to the relevant problem. We focus on the shortest path problem in graphs, where one may think of the noise as coming from real-time traffic. We consider the following model: start with a weighted base graph $G$ and multiply each edge weight by an independently chosen, uniformly random number in $[1,2]$ to obtain a random graph $G'$. This model is called \emph{first passage percolation}. Mathematicians have studied this model extensively when $G$ is a $d$-dimensional grid graph, but the behavior of shortest paths in this model is still poorly understood in general graphs. We make progress in this direction for a class of graphs that resembles real-world road networks. Specifically, we prove that if the geometric realization of $G$ has constant doubling dimension, then for a given $s-t$ pair, we only need to probe the weights on $((\log n) / \epsilon)^{O(1)}$ edges in $G'$ in order to obtain a $(1 + \epsilon)$-approximation to the $s-t$ distance in $G'$. We also demonstrate experimentally that this result is pessimistic -- one can even obtain a short path in $G'$ with a small number of probes to $G'$.
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Building Recommendation Systems using Lambda Architecture
Vipul Bharat Marlecha
Sreyashi Das
International Research Journal of Engineering and Technology (IRJET), Volume: 11 Issue: 05 | May 2024 (2024)
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This paper studies the recommendation systems that are typical to content discovery and personalized services like Netflix and Amazon. The study includes typical components of recommendation systems, what data and inputs are required to serve depending on the machine learning models used. We share how the recommendations leverage a mix of batch processing and streaming databases, and end with trends and potential future developments for recommendation systems
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Rust is a general-purpose programming language designed for performance and safety. Unrecoverable errors (e.g., Divide by Zero) in Rust programs are critical, as they signal bad program states and terminate programs abruptly. Previous work has contributed to utilizing KLEE, a dynamic symbolic test engine, to verify the program would not panic. However, it is difficult for engineers who lack domain expertise to write test code correctly. Besides, the effectiveness of KLEE in finding panics in production Rust code has not been evaluated. We created an approach, called PanicCheck, to hide the complexity of verifying Rust programs with KLEE. Using PanicCheck, engineers only need to annotate the function-to-verify with #[panic_check]. The annotation guides PanicCheck to generate test code, compile the function together with tests, and execute KLEE for verification. After applying PanicCheck to 21 open-source and 2 closed-source projects, we found 61 test inputs that triggered panics; 60 of the 61 panics have been addressed by developers so far. Our research shows promising verification results by KLEE, while revealing technical challenges in using KLEE. Our experience will shed light on future practice and research in program verification.
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Browser fingerprinting is often associated with cross-site user tracking, a practice that many browsers (e.g., Safari, Brave, Edge, Firefox and Chrome) want to block. However, less is publicly known about its uses to enhance online safety, where it can provide an additional security layer against service abuses (e.g., in combination with CAPTCHAs) or during user authentication. To the best of our knowledge, no fingerprinting defenses deployed thus far consider this important distinction when blocking fingerprinting attempts, so they might negatively affect website functionality and security.
To address this issue we make three main contributions. First, we propose and evaluate a novel machine learning-based method to automatically identify authentication pages (i.e. sign-in and sign-up pages). Our algorithm -- which relies on a hybrid unsupervised/supervised approach -- achieves 96-98% precision and recall on a large, manually-labelled dataset of 10,000 popular sites. Second, we compare our algorithm with other methods from prior works on the same dataset, showing that it significantly outperforms all of them (+83% F1-score). Third, we quantify the prevalence of fingerprinting scripts across sign-in and sign-up pages (9.2%) versus those executed on other pages (8.9%); while the rates of fingerprinting are similar, home pages and authentication pages differ in the third-party scripts they include and how often these scripts are labeled as tracking. We also highlight the substantial differences in fingerprinting behavior on login and sign-up pages.
Our work sheds light on the complicated reality that fingerprinting is used to both protect user security and invade user privacy, and that this dual nature must be considered by fingerprinting mitigations.
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“They only care to show us the wheelchair”: disability representation in text-to-image AI models
Avery Mack
Rida Qadri
CHI Conference on Human-Computer Interaction (2024)
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This paper reports on disability representation in images output from text-to-image (T2I) generative AI systems. Through eight focus groups with 25 people
with disabilities, we found that models repeatedly presented reductive archetypes for different disabilities. Often these representations reflected broader
societal stereotypes and biases, which our participants were concerned to see reproduced through T2I. Our participants discussed further challenges with
using these models including the current reliance on prompt engineering to reach satisfactorily diverse results. Finally, they offered suggestions for
how to improve disability representation with solutions like showing multiple, heterogeneous images for a single prompt and including the prompt with images
generated. Our discussion reflects on tensions and tradeoffs we found among the diverse perspectives shared to inform future research on representation-oriented
generative AI system evaluation metrics and development processes.
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