Search Results for author: Lucas Rosenblatt

Found 9 papers, 4 papers with code

Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings

no code implementations27 May 2024 Robert Wolfe, Isaac Slaughter, Bin Han, Bingbing Wen, Yiwei Yang, Lucas Rosenblatt, Bernease Herman, Eva Brown, Zening Qu, Nic Weber, Bill Howe

The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guarded, closed-weight models in terms of performance, domain adaptation, cost, and generalization.

Domain Adaptation Hallucination

A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy

no code implementations18 Dec 2023 Lucas Rosenblatt, Julia Stoyanovich, Christopher Musco

Our theoretical results center on the private mean estimation problem, while our empirical results center on extensive experiments on private data synthesis to demonstrate the effectiveness of stratification on a variety of private mechanisms.

I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

1 code implementation12 Dec 2023 R. Teal Witter, Lucas Rosenblatt

In order to simulate the impact of opening streets, we first compare models for predicting vehicle collisions given network and temporal data.

Graph Neural Network Q-Learning +1

Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology

1 code implementation1 Oct 2023 Lucas Rosenblatt, Bin Han, Erin Posthumus, Theresa Crimmins, Bill Howe

An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States.

The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice

no code implementations13 Feb 2023 Andrew Bell, Lucius Bynum, Nazarii Drushchak, Tetiana Herasymova, Lucas Rosenblatt, Julia Stoyanovich

The ``impossibility theorem'' -- which is considered foundational in algorithmic fairness literature -- asserts that there must be trade-offs between common notions of fairness and performance when fitting statistical models, except in two special cases: when the prevalence of the outcome being predicted is equal across groups, or when a perfectly accurate predictor is used.


Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI

1 code implementation5 Jan 2023 Lorena Piedras, Lucas Rosenblatt, Julia Wilkins

Detecting "toxic" language in internet content is a pressing social and technical challenge.

Binary Classification

Counterfactual Fairness Is Basically Demographic Parity

no code implementations7 Aug 2022 Lucas Rosenblatt, R. Teal Witter

Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings.

counterfactual Fairness

Spending Privacy Budget Fairly and Wisely

no code implementations27 Apr 2022 Lucas Rosenblatt, Joshua Allen, Julia Stoyanovich

Our methods are based on the insights that feature importance can inform how privacy budget is allocated, and, further, that per-group feature importance and fairness-related performance objectives can be incorporated in the allocation.

Fairness Feature Importance +1

Differentially Private Synthetic Data: Applied Evaluations and Enhancements

1 code implementation11 Nov 2020 Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar, Eduardo de Leon, Anuj Desai, Joshua Allen

Differentially private data synthesis protects personal details from exposure, and allows for the training of differentially private machine learning models on privately generated datasets.

BIG-bench Machine Learning

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