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Dhanya Sridhar
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2020 – today
- 2024
- [i16]Sophie Xhonneux, David Dobre, Jian Tang, Gauthier Gidel, Dhanya Sridhar
:
In-Context Learning Can Re-learn Forbidden Tasks. CoRR abs/2402.05723 (2024) - [i15]Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar:
Evaluating Interventional Reasoning Capabilities of Large Language Models. CoRR abs/2404.05545 (2024) - [i14]Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello:
Demystifying amortized causal discovery with transformers. CoRR abs/2405.16924 (2024) - [i13]Sarthak Mittal, Eric Elmoznino, Léo Gagnon, Sangnie Bhardwaj, Dhanya Sridhar, Guillaume Lajoie:
Does learning the right latent variables necessarily improve in-context learning? CoRR abs/2405.19162 (2024) - [i12]Elliot Layne, Jason S. Hartford, Sébastien Lachapelle, Mathieu Blanchette, Dhanya Sridhar:
Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations. CoRR abs/2405.20482 (2024) - 2023
- [j5]Yixin Wang, Dhanya Sridhar, David M. Blei:
Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j4]Dhanya Sridhar, Hal Daumé III, David M. Blei:
Heterogeneous Supervised Topic Models. Trans. Assoc. Comput. Linguistics 10: 732-745 (2022) - [j3]Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. Trans. Assoc. Comput. Linguistics 10: 1138-1158 (2022) - [j2]Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei:
Identifiable Deep Generative Models via Sparse Decoding. Trans. Mach. Learn. Res. 2022 (2022) - [c11]Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei:
On the Assumptions of Synthetic Control Methods. AISTATS 2022: 7163-7175 - [c10]Dhanya Sridhar, Caterina De Bacco, David M. Blei:
Estimating Social Influence from Observational Data. CLeaR 2022: 712-733 - [i11]Dhanya Sridhar
Estimating Social Influence from Observational Data. CoRR abs/2204.01633 (2022) - 2021
- [c9]Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown:
Valid Causal Inference with (Some) Invalid Instruments. ICML 2021: 4096-4106 - [c8]Reid Pryzant, Dallas Card
Causal Effects of Linguistic Properties. NAACL-HLT 2021: 4095-4109 - [c7]Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green:
Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. WWW 2021: 2025-2036 - [i10]Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang:
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. CoRR abs/2109.00725 (2021) - [i9]Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei:
Identifiable Variational Autoencoders via Sparse Decoding. CoRR abs/2110.10804 (2021) - 2020
- [c6]Victor Veitch, Dhanya Sridhar, David M. Blei:
Adapting Text Embeddings for Causal Inference. UAI 2020: 919-928 - [i8]Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown:
Valid Causal Inference with (Some) Invalid Instruments. CoRR abs/2006.11386 (2020) - [i7]Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar:
Causal Effects of Linguistic Properties. CoRR abs/2010.12919 (2020)
2010 – 2019
- 2019
- [c5]Dhanya Sridhar, Lise Getoor:
Estimating Causal Effects of Tone in Online Debates. IJCAI 2019: 1872-1878 - [i6]Yixin Wang, Dhanya Sridhar, David M. Blei:
Equal Opportunity and Affirmative Action via Counterfactual Predictions. CoRR abs/1905.10870 (2019) - [i5]Victor Veitch, Dhanya Sridhar, David M. Blei:
Using Text Embeddings for Causal Inference. CoRR abs/1905.12741 (2019) - [i4]Dhanya Sridhar, Lise Getoor:
Estimating Causal Effects of Tone in Online Debates. CoRR abs/1906.04177 (2019) - 2018
- [b1]Dhanya Sridhar:
Learning Structured and Causal Probabilistic Models for Computational Science. University of California, Santa Cruz, USA, 2018 - [c4]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Scalable Probabilistic Causal Structure Discovery. IJCAI 2018: 5112-5118 - [c3]Yue Zhang, Arti Ramesh, Jennifer Golbeck, Dhanya Sridhar
A Structured Approach to Understanding Recovery and Relapse in AA. WWW 2018: 1205-1214 - [i3]Varun Embar, Dhanya Sridhar, Golnoosh Farnadi, Lise Getoor:
Scalable Structure Learning for Probabilistic Soft Logic. CoRR abs/1807.00973 (2018) - 2017
- [c2]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. AKBC@NIPS 2017 - [i2]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. CoRR abs/1711.05900 (2017) - 2016
- [j1]Dhanya Sridhar
A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinform. 32(20): 3175-3182 (2016) - [i1]Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor:
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. CoRR abs/1607.00474 (2016) - 2015
- [c1]Dhanya Sridhar
Joint Models of Disagreement and Stance in Online Debate. ACL (1) 2015: 116-125
Coauthor Index
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