V Varshaneya

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All published works

Action	Title	Year	Authors
+ PDF Chat	Towards Learning and Explaining Indirect Causal Effects in Neural Networks	2024	Abbavaram Gowtham Reddy Saketh Bachu Harsharaj Pathak Benin Godfrey L V Varshaneya Vineeth N Balasubramanian Satyanarayan Kar
+	Towards Learning and Explaining Indirect Causal Effects in Neural Networks	2023	Abbaavaram Gowtham Reddy Saketh Bachu Harsharaj Pathak Benin L Godfrey Vineeth N Balasubramanian V Varshaneya Satya Narayanan Kar

Common Coauthors

Coauthor	Papers Together
Vineeth N Balasubramanian	2
Saketh Bachu	2
Harsharaj Pathak	2
Satya Narayanan Kar	1
Abbavaram Gowtham Reddy	1
Benin Godfrey L	1
Satyanarayan Kar	1
Abbaavaram Gowtham Reddy	1
Benin L Godfrey	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Direct and Indirect Effects	2001	Judea Pearl	1
+	Explaining nonlinear classification decisions with deep Taylor decomposition	2016	Grégoire Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus‐Robert Müller	1
+	“Why Should I Trust You?”: Explaining the Predictions of Any Classifier	2016	Marco Ribeiro Sameer Singh Carlos Guestrin	1
+	Axiomatic Attribution for Deep Networks	2017	Mukund Sundararajan Ankur Taly Qiqi Yan	1
+ PDF Chat	Grounding Visual Explanations	2018	Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata	1
+	Explaining Classifiers with Causal Concept Effect (CaCE)	2019	Yash Goyal Amir Feder Uri Shalit Been Kim	1
+	Estimating individual treatment effect: generalization bounds and algorithms	2016	Uri Shalit Fredrik Johansson David Sontag	1
+	Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps	2013	Karen Simonyan Andrea Vedaldi Andrew Zisserman	1
+ PDF Chat	Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization	2017	Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra	1
+ PDF Chat	Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR	2017	Sandra Wachter Brent Mittelstadt Chris Russell	1
+	Counterfactual Visual Explanations	2019	Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee	1
+	Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)	2017	Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler Fernanda Viégas Rory Sayres	1
+	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations	2017	Andrew Slavin Ross Michael C. Hughes Finale Doshi‐Velez	1
+	Feature relevance quantification in explainable AI: A causal problem	2019	Dominik Janzing Lenon Minorics Patrick Blöbaum	1
+	Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers	2019	Divyat Mahajan Chenhao Tan Amit Sharma	1
+ PDF Chat	Learning Counterfactual Representations for Estimating Individual Dose-Response Curves	2020	Patrick Schwab Lorenz Linhardt Stefan Bauer Joachim M. Buhmann Walter Karlen	1
+	Deep Structural Causal Models for Tractable Counterfactual Inference	2020	Nick Pawlowski Daniel C. Castro Ben Glocker	1
+	Concept Bottleneck Models	2020	Pang Wei Koh Thao Nguyen Yew Siang Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang	1
+	CASTLE: Regularization via Auxiliary Causal Graph Discovery	2020	Trent Kyono Yao Zhang Mihaela van der Schaar	1
+	Multi-Objective Counterfactual Explanations	2020	Susanne Dandl Christoph Molnar Martin Binder Bernd Bischl	1
+	Recurrent Independent Mechanisms	2019	Anirudh Goyal Alex Lamb Jordan Hoffmann Shagun Sodhani Sergey Levine Yoshua Bengio Bernhard Schölkopf	1
+ PDF Chat	Interpretable machine learning: Fundamental principles and 10 grand challenges	2022	Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong	1
+ PDF Chat	Treatment Effect Estimation with Disentangled Latent Factors	2021	Weijia Zhang Lin Liu Jiuyong Li	1
+ PDF Chat	Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End	2021	Ramaravind Kommiya Mothilal Divyat Mahajan Chenhao Tan Amit Sharma	1
+ PDF Chat	Interpretable Counterfactual Explanations Guided by Prototypes	2021	Arnaud Van Looveren Janis Klaise	1
+ PDF Chat	Comprehensible Convolutional Neural Networks via Guided Concept Learning	2021	Sandareka Wickramanayake Wynne Hsu Mong Li Lee	1
+	The Causal-Neural Connection: Expressiveness, Learnability, and Inference	2021	Kevin Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim	1
+	Generative causal explanations of black-box classifiers	2020	Matthew O’Shaughnessy Gregory Canal Marissa Connor Mark Davenport Christopher J. Rozell	1
+	CXPlain: Causal Explanations for Model Interpretation under Uncertainty	2019	Patrick Schwab Walter Karlen	1
+	Neural Network Attributions: A Causal Perspective	2019	Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar Vineeth N Balasubramanian	1
+	Language Models are Few-Shot Learners	2020	T. B. Brown Benjamin F. Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell	1
+	SmoothGrad: removing noise by adding noise	2017	Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viégas Martin Wattenberg	1
+	CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training	2017	Murat Kocaoğlu Chris Snyder Alexandros G. Dimakis Sriram Vishwanath	1
+	Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models	2020	Tom Heskes Evi Sijben Ioan Gabriel Bucur Tom Claassen	1
+	Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off	2022	Mateo Espinosa Zarlenga Pietro Barbiero Gabriele Ciravegna Giuseppe Marra Francesco Giannini Michelangelo Diligenti Zohreh Shams Fŕed́eric Precioso Stefano Melacci Adrian Weller	1
+	Causal Regularization	2019	Dominik Janzing	1
+	A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution	2020	Aria Khademi Vasant Honavar	1
+	Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review	2020	Sahil Verma Varich Boonsanong Minh Hoang Keegan Hines John P. Dickerson Chirag Shah	1
+	Matching Learned Causal Effects of Neural Networks with Domain Priors	2021	Sai Srinivas Kancheti Abbavaram Gowtham Reddy Vineeth N Balasubramanian Amit Sharma	1
+ PDF Chat	Contrastive-ACE: Domain Generalization Through Alignment of Causal Mechanisms	2022	Yunqi Wang Furui Liu Zhitang Chen Yik‐Chung Wu Jianye Hao Guangyong Chen Pheng‐Ann Heng	1
+	LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models	2022	Mor Geva Avi Caciularu Guy Dar Paul Roit Shoval Sadde Micah Shlain Bar Tamir Yoav Goldberg	1
+	Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction	2015	Guido W. Imbens Donald B. Rubin	1