Search Results for author:
Found 14 papers, 3 papers with code
BOTracle: A framework for Discriminating Bots and Humans
The first method operates on heuristics, allowing for rapid detection.
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages.
A/B testing under Interference with Partial Network Information
A/B tests are often required to be conducted on subjects that might have social connections.
Continuous Treatment Effects with Surrogate Outcomes
Incorporating surrogates, which are fully observed post-treatment variables related to the primary outcome, can improve estimation in this case.
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data.
SODA: Protecting Proprietary Information in On-Device Machine Learning Models
The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications.
A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models
A model trained on these losses balances the trade-off between the creativity and reality of the model.
VADER: Video Alignment Differencing and Retrieval
We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos.
Privacy Aware Experiments without Cookies
We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences.
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated).
Time-uniform central limit theory and asymptotic confidence sequences
This paper introduces time-uniform analogues of such asymptotic confidence intervals, adding to the literature on confidence sequences (CS) -- sequences of confidence intervals that are uniformly valid over time -- which provide valid inference at arbitrary stopping times and incur no penalties for "peeking" at the data, unlike classical confidence intervals which require the sample size to be fixed in advance.
Botcha: Detecting Malicious Non-Human Traffic in the Wild
Malicious bots make up about a quarter of all traffic on the web, and degrade the performance of personalization and recommendation algorithms that operate on e-commerce sites.
Forecasting Granular Audience Size for Online Advertising
Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e. g. {US, Chrome, Mobile}).
Saliency Prediction for Mobile User Interfaces
However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach.
