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ORCIDAmit Sharma 0007
Person information
- affiliation: Microsoft Research, Bangalore, India
- affiliation (PhD 2015): Cornell University, Ithaca, NY, USA
- affiliation (former): IIT Kharagpur, India
Other persons with the same name
- Amit Sharma (aka: Amit Kumar Sharma) — disambiguation page
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2020 – today
- 2024
[i45]Pragya Srivastava, Satvik Golechha, Amit Deshpande, Amit Sharma:
NICE: To Optimize In-Context Examples or Not? CoRR abs/2402.06733 (2024)- 2023
- [j4]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. Trans. Mach. Learn. Res. 2023 (2023) - [c49]Parikshit Bansal, Amit Sharma:
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers. ACL (1) 2023: 2271-2287 - [c48]Chacha Chen
, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. FAccT 2023: 1 - [c47]Jivat Neet Kaur, Emre Kiciman, Amit Sharma:
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ICLR 2023 - [c46]Abhinav Kumar, Amit Deshpande, Amit Sharma:
Causal Effect Regularization: Automated Detection and Removal of Spurious Correlations. NeurIPS 2023 - [i44]Emre Kiciman, Robert Ness, Amit Sharma, Chenhao Tan:
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality. CoRR abs/2305.00050 (2023) - [i43]Parikshit Bansal, Amit Sharma:
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers. CoRR abs/2305.16863 (2023) - [i42]Abbavaram Gowtham Reddy, Saketh Bachu, Saloni Dash, Charchit Sharma, Amit Sharma, Vineeth N. Balasubramanian:
Rethinking Counterfactual Data Augmentation Under Confounding. CoRR abs/2305.18183 (2023) - [i41]Abhinav Kumar, Amit Deshpande, Amit Sharma:
Causal Effect Regularization: Automated Detection and Removal of Spurious Attributes. CoRR abs/2306.11072 (2023) - [i40]Parikshit Bansal, Amit Sharma:
Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost. CoRR abs/2306.15766 (2023) - [i39]Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, Amit Sharma:
Causal Inference Using LLM-Guided Discovery. CoRR abs/2310.15117 (2023) - [i38]Jake M. Hofman, Angelos Chatzimparmpas, Amit Sharma, Duncan J. Watts, Jessica Hullman:
Pre-registration for Predictive Modeling. CoRR abs/2311.18807 (2023) - 2022
- [c45]Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian, Amit Sharma:
Matching Learned Causal Effects of Neural Networks with Domain Priors. ICML 2022: 10676-10696 - [c44]Abhinav Kumar, Chenhao Tan, Amit Sharma:
Probing Classifiers are Unreliable for Concept Removal and Detection. NeurIPS 2022 - [c43]Saloni Dash, Vineeth N. Balasubramanian, Amit Sharma:
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals. WACV 2022: 3879-3888 - [e2]Engineer Bainomugisha, Waylon Brunette, Nicola Dell, Amit Sharma:
COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, Seattle, WA, USA, 29 June 2022 - 1 July 2022. ACM 2022, ISBN 978-1-4503-9347-8 [contents] - [i37]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. CoRR abs/2202.02195 (2022) - [i36]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. CoRR abs/2202.04092 (2022) - [i35]Jivat Neet Kaur, Emre Kiciman, Amit Sharma:
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. CoRR abs/2206.07837 (2022) - [i34]Abhinav Kumar, Chenhao Tan, Amit Sharma:
Probing Classifiers are Unreliable for Concept Removal and Detection. CoRR abs/2207.04153 (2022) - [i33]Parikshit Bansal, Yashoteja Prabhu, Emre Kiciman, Amit Sharma:
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems. CoRR abs/2210.10636 (2022) - [i32]Abbavaram Gowtham Reddy, Saloni Dash, Amit Sharma, Vineeth N. Balasubramanian:
Counterfactual Generation Under Confounding. CoRR abs/2210.12368 (2022) - 2021
- [c42]Naman Goel, Alfonso Amayuelas, Amit Deshpande, Amit Sharma:
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective. AAAI 2021: 7564-7573 - [c41]Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma:
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End. AIES 2021: 652-663 - [c40]Sachin R. Pendse, Amit Sharma, Aditya Vashistha, Munmun De Choudhury, Neha Kumar:
"Can I Not Be Suicidal on a Sunday?": Understanding Technology-Mediated Pathways to Mental Health Support. CHI 2021: 545:1-545:16 - [c39]Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Aleksandrs Slivkins, Amit Sharma:
Sayer: Using Implicit Feedback to Optimize System Policies. SoCC 2021: 273-288 - [c38]Prateek Chanda, Amogh Wagh, Jemimah A. Johnson, Swaraj Renghe, Vageesh Chandramouli, George Mathews, Sapna Behar, Poornima Bhola, Girish N. Rao, Paulomi Sudhir, T. K. Srikanth, Amit Sharma, Seema Mehrotra:
MINDNOTES : A Mobile Platform to enable users to break stigma around mental health and connect with therapists. CSCW Companion 2021: 213-217 - [c37]Divyat Mahajan, Shruti Tople, Amit Sharma:
Domain Generalization using Causal Matching. ICML 2021: 7313-7324 - [c36]Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman:
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions. WSDM 2021: 409-417 - [i31]Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr:
Technology Readiness Levels for Machine Learning Systems. CoRR abs/2101.03989 (2021) - [i30]Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople:
Causally Constrained Data Synthesis for Private Data Release. CoRR abs/2105.13144 (2021) - [i29]Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman:
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions. CoRR abs/2108.13518 (2021) - [i28]Divyat Mahajan, Shruti Tople, Amit Sharma:
The Connection between Out-of-Distribution Generalization and Privacy of ML Models. CoRR abs/2110.03369 (2021) - [i27]Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins:
Sayer: Using Implicit Feedback to Optimize System Policies. CoRR abs/2110.14874 (2021) - [i26]Abbavaram Gowtham Reddy, Sai Srinivas Kancheti, Vineeth N. Balasubramanian, Amit Sharma:
Causal Regularization Using Domain Priors. CoRR abs/2111.12490 (2021) - [i25]Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis:
Omitted Variable Bias in Machine Learned Causal Models. CoRR abs/2112.13398 (2021) - 2020
- [c35]Devansh Mehta, Alok Sharma, Ramaravind Kommiya Mothilal, Chiraag, Anurag Shukla, Vishnu Prasad, William Thies, Venkanna U, Colin Scott, Amit Sharma:
Facilitating Media Distribution with Monetary Incentives. CHI Extended Abstracts 2020: 1-7 - [c34]Sachin R. Pendse, Faisal M. Lalani, Munmun De Choudhury, Amit Sharma, Neha Kumar:
"Like Shock Absorbers": Understanding the Human Infrastructures of Technology-Mediated Mental Health Support. CHI 2020: 1-14 - [c33]Amit Sharma, Emre Kiciman:
Causal Inference and Counterfactual Reasoning. COMAD/CODS 2020: 369-370 - [c32]Devansh Mehta, Ramaravind Kommiya Mothilal, Alok Sharma, William Thies, Amit Sharma:
Using Mobile Airtime Credits to Incentivize Learning, Sharing and Survey Response: Experiences from the Field. COMPASS 2020: 254-264 - [c31]Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan:
Explaining machine learning classifiers through diverse counterfactual explanations. FAT* 2020: 607-617 - [c30]Shruti Tople, Amit Sharma, Aditya Nori:
Alleviating Privacy Attacks via Causal Learning. ICML 2020: 9537-9547 - [c29]Koustuv Saha, Amit Sharma:
Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities. ICWSM 2020: 590-601 - [c28]Ashish Sharma, Monojit Choudhury, Tim Althoff, Amit Sharma:
Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms. ICWSM 2020: 614-625 - [c27]Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma, Kalika Bali:
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi. LREC 2020: 2832-2838 - [c26]Taisa Kushner, Amit Sharma:
Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums. WWW 2020: 2906-2912 - [i24]Ashish Sharma, Monojit Choudhury, Tim Althoff, Amit Sharma:
Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms. CoRR abs/2004.04999 (2020) - [i23]Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma, Kalika Bali:
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi. CoRR abs/2004.10270 (2020) - [i22]Taisa Kushner, Amit Sharma:
Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums. CoRR abs/2004.10330 (2020) - [i21]Divyat Mahajan, Shruti Tople, Amit Sharma:
Domain Generalization using Causal Matching. CoRR abs/2006.07500 (2020) - [i20]Saloni Dash, Amit Sharma:
Counterfactual Generation and Fairness Evaluation Using Adversarially Learned Inference. CoRR abs/2009.08270 (2020) - [i19]Amit Sharma, Emre Kiciman:
DoWhy: An End-to-End Library for Causal Inference. CoRR abs/2011.04216 (2020) - [i18]Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma:
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End. CoRR abs/2011.04917 (2020) - [i17]Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman:
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions. CoRR abs/2011.05877 (2020) - [i16]Naman Goel, Alfonso Amayuelas, Amit Deshpande, Amit Sharma:
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective. CoRR abs/2012.11448 (2020)
2010 – 2019
- 2019
- [j3]Saiganesh Swaminathan, Indrani Medhi-Thies, Devansh Mehta, Edward Cutrell, Amit Sharma, William Thies:
Learn2Earn: Using Mobile Airtime Incentives to Bolster Public Awareness Campaigns. Proc. ACM Hum. Comput. Interact. 3(CSCW): 49:1-49:20 (2019) - [j2]Sachin R. Pendse, Kate Niederhoffer, Amit Sharma:
Cross-Cultural Differences in the Use of Online Mental Health Support Forums. Proc. ACM Hum. Comput. Interact. 3(CSCW): 67:1-67:29 (2019) - [c25]Yada Pruksachatkun, Sachin R. Pendse, Amit Sharma:
Moments of Change: Analyzing Peer-Based Cognitive Support in Online Mental Health Forums. CHI 2019: 64 - [c24]Ramaravind Kommiya Mothilal, Devansh Mehta, Alok Sharma, William Thies, Amit Sharma:
Learnings from an Ongoing Deployment of an IVR-based Platform for Voter Awareness. CSCW Companion 2019: 257-261 - [c23]Sachin R. Pendse, Naveena Karusala, Divya Siddarth, Pattie Gonsalves, Seema Mehrotra, John A. Naslund, Mamta Sood, Neha Kumar, Amit Sharma:
Mental health in the global south: challenges and opportunities in HCI for development. COMPASS 2019: 22-36 - [c22]Ramaravind Kommiya Mothilal, Amulya Yadav, Amit Sharma:
Optimizing peer referrals for public awareness using contextual bandits. COMPASS 2019: 74-85 - [c21]Karn Dubey, Palash Gupta, Rachna Shriwas, Gayatri Gulvady, Amit Sharma:
Learnings from deploying a voice-based social platform for people with disability. COMPASS 2019: 111-121 - [c20]Jackson A. Killian, Bryan Wilder, Amit Sharma, Vinod Choudhary, Bistra Dilkina, Milind Tambe:
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data. KDD 2019: 2430-2438 - [c19]Emre Kiciman, Amit Sharma:
Causal Inference and Counterfactual Reasoning (3hr Tutorial). WSDM 2019: 828-829 - [i15]Jackson A. Killian, Bryan Wilder, Amit Sharma, Vinod Choudhary, Bistra Dilkina, Milind Tambe:
Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data. CoRR abs/1902.01506 (2019) - [i14]Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan:
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. CoRR abs/1905.07697 (2019) - [i13]Arpita Biswas, Siddharth Barman, Amit Deshpande, Amit Sharma:
Quantifying Infra-Marginality and Its Trade-off with Group Fairness. CoRR abs/1909.00982 (2019) - [i12]Shruti Tople, Amit Sharma, Aditya Nori:
Alleviating Privacy Attacks via Causal Learning. CoRR abs/1909.12732 (2019) - [i11]Divyat Mahajan, Chenhao Tan, Amit Sharma:
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers. CoRR abs/1912.03277 (2019) - 2018
- [j1]Jisun An, Rumi Chunara, David J. Crandall, Darian Frajberg, Megan French, Bernard J. Jansen, Juhi Kulshrestha, Yelena Mejova, Daniel M. Romero, Joni Salminen, Amit Sharma, Amit P. Sheth, Chenhao Tan, Samuel Hardman Taylor, Sanjaya Wijeratne:
Reports of the Workshops Held at the 2018 International AAAI Conference on Web and Social Media. AI Mag. 39(4): 36-44 (2018) - 2017
- [c18]Mathias Lécuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins:
Harvesting Randomness to Optimize Distributed Systems. HotNets 2017: 178-184 - [c17]Michael D. Ekstrand, Amit Sharma:
FATREC Workshop on Responsible Recommendation. RecSys 2017: 382-383 - [c16]Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna M. Wallach, Emine Yilmaz:
Auditing Search Engines for Differential Satisfaction Across Demographics. WWW (Companion Volume) 2017: 626-633 - [i10]Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna M. Wallach, Emine Yilmaz:
Auditing Search Engines for Differential Satisfaction Across Demographics. CoRR abs/1705.10689 (2017) - 2016
- [c15]Amit Sharma, Dan Cosley:
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds. CSCW 2016: 1089-1101 - [c14]Benjamin Shulman, Amit Sharma, Dan Cosley:
Predictability of Popularity: Gaps between Prediction and Understanding. ICWSM 2016: 348-357 - [c13]Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts:
Exploring Limits to Prediction in Complex Social Systems. WWW 2016: 683-694 - [c12]Samuel Barbosa, Dan Cosley, Amit Sharma, Roberto M. Cesar Jr.:
Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior. WWW 2016: 829-841 - [e1]Ido Guy, Amit Sharma:
Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, USA, September 17, 2016. CEUR Workshop Proceedings 1688, CEUR-WS.org 2016 [contents] - [i9]Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts:
Exploring limits to prediction in complex social systems. CoRR abs/1602.01013 (2016) - [i8]Samuel Barbosa, Dan Cosley, Amit Sharma, Roberto M. Cesar Jr.:
Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior. CoRR abs/1603.07025 (2016) - [i7]Benjamin Shulman, Amit Sharma, Dan Cosley:
Predictability of Popularity: Gaps between Prediction and Understanding. CoRR abs/1603.09436 (2016) - [i6]Amit Sharma, Dan Cosley:
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds. CoRR abs/1604.01105 (2016) - [i5]Amit Sharma, Jake M. Hofman, Duncan J. Watts:
Split-door criterion for causal identification: Automatic search for natural experiments. CoRR abs/1611.09414 (2016) - 2015
- [b1]Amit Sharma:
The Interplay of Personal Preference and Social Influence in Sharing Networks. Cornell University, USA, 2015 - [c11]Amit Sharma, Dan Cosley:
Studying and Modeling the Connection between People's Preferences and Content Sharing. CSCW 2015: 1246-1257 - [c10]Amit Sharma, Jake M. Hofman, Duncan J. Watts:
Estimating the Causal Impact of Recommendation Systems from Observational Data. EC 2015: 453-470 - [i4]Amit Sharma, Jake M. Hofman, Duncan J. Watts:
Estimating the Causal Impact of Recommendation Systems from Observational Data. CoRR abs/1510.05569 (2015) - 2014
- [c9]Amit Sharma:
Modeling the effect of people's preferences and social forces on adopting and sharing items. RecSys 2014: 421-424 - [i3]Amit Sharma, Dan Cosley:
Studying and Modeling the Connection between People's Preferences and Content Sharing. CoRR abs/1412.1424 (2014) - 2013
- [c8]Amit Sharma:
PopCore: a system for network-centric recommendation. CSCW Companion 2013: 31-34 - [c7]Amit Sharma, Mevlana Gemici, Dan Cosley:
Friends, Strangers, and the Value of Ego Networks for Recommendation. ICWSM 2013 - [c6]Priyankar Ghosh, Amit Sharma, Partha Pratim Chakrabarti, Pallab Dasgupta:
Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures : Extended Abstract. IJCAI 2013: 3156-3160 - [c5]Amit Sharma:
A Research Platform for Recommendation within Social Networks. RSWeb@RecSys 2013 - [c4]Amit Sharma, Baoshi Yan:
Pairwise learning in recommendation: experiments with community recommendation on linkedin. RecSys 2013: 193-200 - [c3]Amit Sharma, Dan Cosley:
Do social explanations work?: studying and modeling the effects of social explanations in recommender systems. WWW 2013: 1133-1144 - [i2]Amit Sharma, Dan Cosley:
Do Social Explanations Work? Studying and Modeling the Effects of Social Explanations in Recommender Systems. CoRR abs/1304.3405 (2013) - [i1]Amit Sharma, Mevlana Gemici, Dan Cosley:
Friends, Strangers, and the Value of Ego Networks for Recommendation. CoRR abs/1304.4837 (2013) - 2011
- [c2]Vimmi Jaiswal, Amit Sharma, Akshat Verma:
ReComp: QoS-aware recursive service composition at minimum cost. Integrated Network Management 2011: 225-232 - [c1]Amit Sharma, Dan Cosley:
Network-Centric Recommendation: Personalization with and in Social Networks. SocialCom/PASSAT 2011: 282-289
Coauthor Index
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