Srinivas, Suraj

22 publications

ICML 2025 How Much Can We Forget About Data Contamination? Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike Von Luxburg

ICMLW 2024 All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas, Himabindu Lakkaraju

ICMLW 2024 All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas, Himabindu Lakkaraju

ICMLW 2024 All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas, Himabindu Lakkaraju

UAI 2024 Characterizing Data Point Vulnerability as Average-Case Robustness Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

NeurIPS 2024 Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE) Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flavio P. Calmon, Himabindu Lakkaraju

ICMLW 2023 Consistent Explanations in the Face of Model Indeterminacy via Ensembling Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju

NeurIPS 2023 Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

ICMLW 2023 Efficient Estimation of Local Robustness of Machine Learning Models Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

UAI 2023 On Minimizing the Impact of Dataset Shifts on Actionable Explanations Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju

ICMLW 2023 Verifiable Feature Attributions: A Bridge Between Post Hoc Explainability and Inherent Interpretability Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

NeurIPS 2023 Which Models Have Perceptually-Aligned Gradients? an Explanation via Off-Manifold Robustness Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju

ICMLW 2023 Which Models Have Perceptually-Aligned Gradients? an Explanation via Off-Manifold Robustness Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju

ICMLW 2023 Word-Level Explanations for Analyzing Bias in Text-to-Image Models Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju

CVPRW 2022 Cyclical Pruning for Sparse Neural Networks Suraj Srinivas, Andrey Kuzmin, Markus Nagel, Mart van Baalen, Andrii Skliar, Tijmen Blankevoort

NeurIPS 2022 Data-Efficient Structured Pruning via Submodular Optimization Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien

NeurIPS 2022 Efficient Training of Low-Curvature Neural Networks Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

NeurIPS 2022 Which Explanation Should I Choose? a Function Approximation Perspective to Characterizing Post Hoc Explanations Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

ICLR 2021 Rethinking the Role of Gradient-Based Attribution Methods for Model Interpretability Suraj Srinivas, Francois Fleuret

NeurIPS 2019 Full-Gradient Representation for Neural Network Visualization Suraj Srinivas, François Fleuret

ICML 2018 Knowledge Transfer with Jacobian Matching Suraj Srinivas, Francois Fleuret

CVPRW 2017 Training Sparse Neural Networks Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu