Search Results for author: Frederic Sala

Found 25 papers, 15 papers with code

Zero-Shot Robustification of Zero-Shot Models With Foundation Models

1 code implementation8 Sep 2023 Dyah Adila, Changho Shin, Linrong Cai, Frederic Sala

These insights are embedded and used to remove harmful and boost useful components in embeddings -- without any supervision.

Mitigating Source Bias for Fairer Weak Supervision

1 code implementation NeurIPS 2023 Changho Shin, Sonia Cromp, Dyah Adila, Frederic Sala

Weak supervision enables efficient development of training sets by reducing the need for ground truth labels.


Domain Generalization via Nuclear Norm Regularization

1 code implementation13 Mar 2023 Zhenmei Shi, Yifei Ming, Ying Fan, Frederic Sala, YIngyu Liang

In this paper, we propose a simple and effective regularization method based on the nuclear norm of the learned features for domain generalization.

Domain Generalization

Resonant Anomaly Detection with Multiple Reference Datasets

no code implementations20 Dec 2022 Mayee F. Chen, Benjamin Nachman, Frederic Sala

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal.

Anomaly Detection

Lifting Weak Supervision To Structured Prediction

1 code implementation24 Nov 2022 Harit Vishwakarma, Nicholas Roberts, Frederic Sala

Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from a variety of sources.

Binary Classification Structured Prediction

Good Data from Bad Models : Foundations of Threshold-based Auto-labeling

no code implementations22 Nov 2022 Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak

Given the long shelf-life and diverse usage of the resulting datasets, understanding when the data obtained by such auto-labeling systems can be relied on is crucial.

AutoML for Climate Change: A Call to Action

1 code implementation7 Oct 2022 Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White

The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications.


Ask Me Anything: A simple strategy for prompting language models

3 code implementations5 Oct 2022 Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré

Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task.

Coreference Resolution Natural Language Inference +2

AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

no code implementations30 Aug 2022 Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala

While it has been used successfully in many domains, weak supervision's application scope is limited by the difficulty of constructing labeling functions for domains with complex or high-dimensional features.


Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision

1 code implementation24 Mar 2022 Mayee F. Chen, Daniel Y. Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré

Despite the black-box nature of foundation models, we prove results characterizing how our approach improves performance and show that lift scales with the smoothness of label distributions in embedding space.

Generative Modeling Helps Weak Supervision (and Vice Versa)

1 code implementation22 Mar 2022 Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski

The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples.

Data Augmentation Image Classification

Universalizing Weak Supervision

no code implementations ICLR 2022 Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, Frederic Sala

We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space.

Learning-To-Rank regression

Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation

1 code implementation3 Mar 2021 Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré

We apply our decomposition framework to three scenarios -- well-specified, misspecified, and corrected models -- to 1) choose between labeled and unlabeled data and 2) learn from their combination.

Cut out the annotator, keep the cutout: better segmentation with weak supervision

no code implementations ICLR 2021 Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re

We propose a framework that fuses limited label learning and weak supervision for segmentation tasks, enabling users to train high-performing segmentation CNNs with very few hand-labeled training points.

Data Augmentation Few-Shot Learning +4

Ivy: Instrumental Variable Synthesis for Causal Inference

no code implementations11 Apr 2020 Zhaobin Kuang, Frederic Sala, Nimit Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Ré

To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner.

Causal Inference Epidemiology +1

Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

1 code implementation ICML 2020 Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré

In this work, we show that, for a class of latent variable models highly applicable to weak supervision, we can find a closed-form solution to model parameters, obviating the need for iterative solutions like stochastic gradient descent (SGD).

Multi-Resolution Weak Supervision for Sequential Data

no code implementations NeurIPS 2019 Frederic Sala, Paroma Varma, Jason Fries, Daniel Y. Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré

Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence.

Learning Mixed-Curvature Representations in Product Spaces

no code implementations ICLR 2019 Albert Gu, Frederic Sala, Beliz Gunel, Christopher Ré

The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.

Riemannian optimization Word Embeddings

Representation Tradeoffs for Hyperbolic Embeddings

2 code implementations ICML 2018 Christopher De Sa, Albert Gu, Christopher Ré, Frederic Sala

Given a tree, we give a combinatorial construction that embeds the tree in hyperbolic space with arbitrarily low distortion without using optimization.

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