Search Results for author: Katherine M. Collins

Found 16 papers, 6 papers with code

Estimation of Concept Explanations Should be Uncertainty Aware

1 code implementation13 Dec 2023 Vihari Piratla, Juyeon Heo, Katherine M. Collins, Sukriti Singh, Adrian Weller

We believe the improved quality of uncertainty-aware concept explanations make them a strong candidate for more reliable model interpretation.

AI for Mathematics: A Cognitive Science Perspective

no code implementations19 Oct 2023 Cedegao E. Zhang, Katherine M. Collins, Adrian Weller, Joshua B. Tenenbaum

Mathematics is one of the most powerful conceptual systems developed and used by the human species.

Learning to Receive Help: Intervention-Aware Concept Embedding Models

1 code implementation NeurIPS 2023 Mateo Espinosa Zarlenga, Katherine M. Collins, Krishnamurthy Dvijotham, Adrian Weller, Zohreh Shams, Mateja Jamnik

To address this, we propose Intervention-aware Concept Embedding models (IntCEMs), a novel CBM-based architecture and training paradigm that improves a model's receptiveness to test-time interventions.

Selective Concept Models: Permitting Stakeholder Customisation at Test-Time

no code implementations14 Jun 2023 Matthew Barker, Katherine M. Collins, Krishnamurthy Dvijotham, Adrian Weller, Umang Bhatt

Concept-based models perform prediction using a set of concepts that are interpretable to stakeholders.

Learning Personalized Decision Support Policies

no code implementations13 Apr 2023 Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar

In this work, we propose learning a decision support policy that, for a given input, chooses which form of support, if any, to provide.

Multi-Armed Bandits

Human Uncertainty in Concept-Based AI Systems

no code implementations22 Mar 2023 Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, Krishnamurthy Dvijotham

We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans.

Decision Making

Human-in-the-Loop Mixup

1 code implementation2 Nov 2022 Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller

We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration.

Eliciting and Learning with Soft Labels from Every Annotator

1 code implementation2 Jul 2022 Katherine M. Collins, Umang Bhatt, Adrian Weller

Our elicitation methodology therefore shows nuanced promise in enabling practitioners to enjoy the benefits of improved model performance and reliability with fewer annotators, and serves as a guide for future dataset curators on the benefits of leveraging richer information, such as categorical uncertainty, from individual annotators.

Learning Signal-Agnostic Manifolds of Neural Fields

no code implementations NeurIPS 2021 Yilun Du, Katherine M. Collins, Joshua B. Tenenbaum, Vincent Sitzmann

We leverage neural fields to capture the underlying structure in image, shape, audio and cross-modal audiovisual domains in a modality-independent manner.

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface

no code implementations ICLR 2022 Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum

We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.

Scene Understanding Time Series +1

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