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2020 – today
- 2024
[c28]Ching-An Cheng, Allen Nie, Adith Swaminathan:
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs. NeurIPS 2024- [c27]Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng:
How to Solve Contextual Goal-Oriented Problems with Offline Datasets? NeurIPS 2024 - [i25]Jiacen Xu, Jack W. Stokes, Geoff McDonald, Xuesong Bai, David Marshall, Siyue Wang, Adith Swaminathan, Zhou Li:
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks. CoRR abs/2403.01038 (2024) - [i24]Allen Nie, Ching-An Cheng, Andrey Kolobov, Adith Swaminathan:
The Importance of Directional Feedback for LLM-based Optimizers. CoRR abs/2405.16434 (2024) - [i23]Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan:
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots. CoRR abs/2406.01633 (2024) - [i22]Ching-An Cheng, Allen Nie, Adith Swaminathan:
Trace is the New AutoDiff - Unlocking Efficient Optimization of Computational Workflows. CoRR abs/2406.16218 (2024) - [i21]Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng:
How to Solve Contextual Goal-Oriented Problems with Offline Datasets? CoRR abs/2408.07753 (2024) - [i20]Kaushal Paneri, Michael Munje, Kailash Singh Maurya, Adith Swaminathan, Yifan Shi:
Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders. CoRR abs/2410.03697 (2024) - 2023
- [c26]Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo de Oliveira Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan:
Hindsight Learning for MDPs with Exogenous Inputs. ICML 2023: 31877-31914 - [i19]Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng:
Interactive Robot Learning from Verbal Correction. CoRR abs/2310.17555 (2023) - [i18]Ching-An Cheng, Andrey Kolobov, Dipendra Misra, Allen Nie, Adith Swaminathan:
LLF-Bench: Benchmark for Interactive Learning from Language Feedback. CoRR abs/2312.06853 (2023) - 2022
- [i17]Sean R. Sinclair, Felipe Frujeri, Ching-An Cheng, Adith Swaminathan:
Hindsight Learning for MDPs with Exogenous Inputs. CoRR abs/2207.06272 (2022) - 2021
- [j2]Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang:
Recommendations as Treatments. AI Mag. 42(3): 19-30 (2021) - [c25]Ching-An Cheng, Andrey Kolobov, Adith Swaminathan:
Heuristic-Guided Reinforcement Learning. NeurIPS 2021: 13550-13563 - [i16]Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan:
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL. CoRR abs/2106.00589 (2021) - [i15]Ching-An Cheng, Andrey Kolobov, Adith Swaminathan:
Heuristic-Guided Reinforcement Learning. CoRR abs/2106.02757 (2021) - 2020
- [c24]Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz:
Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations. AAAI 2020: 5207-5215 - [c23]Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew J. Hausknecht:
Working Memory Graphs. ICML 2020: 6404-6414 - [c22]Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew J. Hausknecht:
Learning Calibratable Policies using Programmatic Style-Consistency. ICML 2020: 11001-11011 - [c21]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. NeurIPS 2020 - [c20]Thorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan:
REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. RecSys 2020: 628-629 - [c19]Lin Ma, Bailu Ding, Sudipto Das
, Adith Swaminathan:
Active Learning for ML Enhanced Database Systems. SIGMOD Conference 2020: 175-191 - [i14]J. Edward Hu, Adith Swaminathan, Hadi Salman, Greg Yang:
Improved Image Wasserstein Attacks and Defenses. CoRR abs/2004.12478 (2020) - [i13]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Reinforcement Learning Without Great Exploration. CoRR abs/2007.08202 (2020)
2010 – 2019
- 2019
- [c18]Maggie Makar, Adith Swaminathan, Emre Kiciman:
A Distillation Approach to Data Efficient Individual Treatment Effect Estimation. AAAI 2019: 4544-4551 - [c17]Thorsten Joachims, Maria Dimakopoulou, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile:
REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation. RecSys 2019: 568-569 - [c16]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with Stationary Distribution Correction. UAI 2019: 1180-1190 - [i12]Matthew J. Hausknecht, Ricky Loynd, Greg Yang, Adith Swaminathan, Jason D. Williams:
NAIL: A General Interactive Fiction Agent. CoRR abs/1902.04259 (2019) - [i11]Ishan Durugkar, Matthew J. Hausknecht, Adith Swaminathan, Patrick MacAlpine:
Multi-Preference Actor Critic. CoRR abs/1904.03295 (2019) - [i10]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with State Distribution Correction. CoRR abs/1904.08473 (2019) - [i9]Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz:
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations. CoRR abs/1905.05179 (2019) - [i8]Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew J. Hausknecht:
Learning Calibratable Policies using Programmatic Style-Consistency. CoRR abs/1910.01179 (2019) - [i7]Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew J. Hausknecht:
Working Memory Graphs. CoRR abs/1911.07141 (2019) - 2018
- [c15]Thorsten Joachims, Adith Swaminathan, Maarten de Rijke:
Deep Learning with Logged Bandit Feedback. ICLR (Poster) 2018 - [c14]Thorsten Joachims, Adith Swaminathan, Tobias Schnabel:
Unbiased Learning-to-Rank with Biased Feedback. IJCAI 2018: 5284-5288 - [c13]Thorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile:
REVEAL 2018: offline evaluation for recommender systems. RecSys 2018: 514-515 - 2017
- [b1]Adith Swaminathan:
Counterfactual evaluation and learning from logged user feedback. Cornell University, USA, 2017 - [c12]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. NIPS 2017: 3632-3642 - [c11]Thorsten Joachims, Adith Swaminathan, Tobias Schnabel:
Unbiased Learning-to-Rank with Biased Feedback. WSDM 2017: 781-789 - 2016
- [c10]Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims:
Recommendations as Treatments: Debiasing Learning and Evaluation. ICML 2016: 1670-1679 - [c9]Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims:
Unbiased Comparative Evaluation of Ranking Functions. ICTIR 2016: 109-118 - [c8]Thorsten Joachims, Adith Swaminathan:
Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. SIGIR 2016: 1199-1201 - [i6]Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims:
Recommendations as Treatments: Debiasing Learning and Evaluation. CoRR abs/1602.05352 (2016) - [i5]Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims:
Unbiased Comparative Evaluation of Ranking Functions. CoRR abs/1604.07209 (2016) - [i4]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. CoRR abs/1605.04812 (2016) - [i3]Thorsten Joachims, Adith Swaminathan, Tobias Schnabel:
Unbiased Learning-to-Rank with Biased Feedback. CoRR abs/1608.04468 (2016) - [i2]Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke:
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. CoRR abs/1612.00367 (2016) - 2015
- [j1]Adith Swaminathan, Thorsten Joachims:
Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16: 1731-1755 (2015) - [c7]Adith Swaminathan, Thorsten Joachims:
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. ICML 2015: 814-823 - [c6]Adith Swaminathan, Thorsten Joachims:
The Self-Normalized Estimator for Counterfactual Learning. NIPS 2015: 3231-3239 - [c5]Tobias Schnabel, Adith Swaminathan, Thorsten Joachims:
Unbiased Ranking Evaluation on a Budget. WWW (Companion Volume) 2015: 935-937 - [c4]Adith Swaminathan, Thorsten Joachims:
Counterfactual Risk Minimization. WWW (Companion Volume) 2015: 939-941 - [i1]Adith Swaminathan, Thorsten Joachims:
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. CoRR abs/1502.02362 (2015) - 2014
- [c3]Rakesh Agrawal, Maria Christoforaki, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi, Adith Swaminathan:
Mining Videos from the Web for Electronic Textbooks. ICFCA 2014: 219-234 - 2013
- [c2]Karthik Raman, Adith Swaminathan, Johannes Gehrke, Thorsten Joachims:
Beyond myopic inference in big data pipelines. KDD 2013: 86-94 - 2012
- [c1]Ruben Sipos, Adith Swaminathan, Pannaga Shivaswamy, Thorsten Joachims:
Temporal corpus summarization using submodular word coverage. CIKM 2012: 754-763
Coauthor Index
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