Temporal corpus summarization using submodular word coverage. - Ruben Sipos; Adith Swaminathan; Pannaga Shivaswamy; Thorsten Joachims (CIKM, 2012)

Beyond myopic inference in big data pipelines. - Karthik Raman 001; Adith Swaminathan; Johannes Gehrke; Thorsten Joachims (KDD, 2013)

Mining Videos from the Web for Electronic Textbooks. - Rakesh Agrawal 001; Maria Christoforaki; Sreenivas Gollapudi; Anitha Kannan; Krishnaram Kenthapadi; Adith Swaminathan (ICFCA, 2014)

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. - Adith Swaminathan; Thorsten Joachims (ICML, 2015)

The Self-Normalized Estimator for Counterfactual Learning. - Adith Swaminathan; Thorsten Joachims (NIPS, 2015)

Unbiased Ranking Evaluation on a Budget. - Tobias Schnabel; Adith Swaminathan; Thorsten Joachims (WWW (Companion Volume), 2015)

Counterfactual Risk Minimization. - Adith Swaminathan; Thorsten Joachims (WWW (Companion Volume), 2015)

Batch learning from logged bandit feedback through counterfactual risk minimization. - Adith Swaminathan; Thorsten Joachims (J. Mach. Learn. Res., 2015)

Recommendations as Treatments: Debiasing Learning and Evaluation. - Tobias Schnabel; Adith Swaminathan; Ashudeep Singh; Navin Chandak; Thorsten Joachims (ICML, 2016)

Unbiased Comparative Evaluation of Ranking Functions. - Tobias Schnabel; Adith Swaminathan; Peter I. Frazier; Thorsten Joachims (ICTIR, 2016)

Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. - Thorsten Joachims; Adith Swaminathan (SIGIR, 2016)

Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. - Damien Lefortier; Adith Swaminathan; Xiaotao Gu; Thorsten Joachims; Maarten de Rijke (CoRR, 2016)

Off-policy evaluation for slate recommendation. - Adith Swaminathan; Akshay Krishnamurthy; Alekh Agarwal; Miroslav Dudík; John Langford 001; Damien Jose; Imed Zitouni (NIPS, 2017)

Counterfactual evaluation and learning from logged user feedback. - Adith Swaminathan (2017)

Deep Learning with Logged Bandit Feedback. - Thorsten Joachims; Adith Swaminathan; Maarten de Rijke (ICLR (Poster), 2018)

Unbiased Learning-to-Rank with Biased Feedback. - Thorsten Joachims; Adith Swaminathan; Tobias Schnabel (IJCAI, 2018)

REVEAL 2018: offline evaluation for recommender systems. - Thorsten Joachims; Adith Swaminathan; Yves Raimond; Olivier Koch; Flavian Vasile (RecSys, 2018)

A Distillation Approach to Data Efficient Individual Treatment Effect Estimation. - Maggie Makar; Adith Swaminathan; Emre Kiciman (AAAI, 2019)

REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation. - Thorsten Joachims; Maria Dimakopoulou; Adith Swaminathan; Yves Raimond; Olivier Koch; Flavian Vasile (RecSys, 2019)

Off-Policy Policy Gradient with Stationary Distribution Correction. - Yao Liu 009; Adith Swaminathan; Alekh Agarwal; Emma Brunskill (UAI, 2019)

NAIL: A General Interactive Fiction Agent. - Matthew J. Hausknecht; Ricky Loynd; Greg Yang; Adith Swaminathan; Jason D. Williams (CoRR, 2019)

Multi-Preference Actor Critic. - Ishan Durugkar; Matthew J. Hausknecht; Adith Swaminathan; Patrick MacAlpine (CoRR, 2019)

Off-Policy Policy Gradient with State Distribution Correction. - Yao Liu 009; Adith Swaminathan; Alekh Agarwal; Emma Brunskill (CoRR, 2019)

Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations. - Aditya Modi 002; Debadeepta Dey; Alekh Agarwal; Adith Swaminathan; Besmira Nushi; Sean Andrist; Eric Horvitz (AAAI, 2020)

Working Memory Graphs. - Ricky Loynd; Roland Fernandez; Asli Celikyilmaz; Adith Swaminathan; Matthew J. Hausknecht (ICML, 2020)

Learning Calibratable Policies using Programmatic Style-Consistency. - Eric Zhan; Albert Tseng; Yisong Yue; Adith Swaminathan; Matthew J. Hausknecht (ICML, 2020)

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. - Yao Liu 009; Adith Swaminathan; Alekh Agarwal; Emma Brunskill (NeurIPS, 2020)

REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. - Thorsten Joachims; Yves Raimond; Olivier Koch; Maria Dimakopoulou; Flavian Vasile; Adith Swaminathan (RecSys, 2020)

Active Learning for ML Enhanced Database Systems. - Lin Ma 006; Bailu Ding; Sudipto Das; Adith Swaminathan (SIGMOD Conference, 2020)

Improved Image Wasserstein Attacks and Defenses. - J. Edward Hu; Adith Swaminathan; Hadi Salman; Greg Yang (CoRR, 2020)

Provably Good Batch Reinforcement Learning Without Great Exploration. - Yao Liu 009; Adith Swaminathan; Alekh Agarwal; Emma Brunskill (CoRR, 2020)

Heuristic-Guided Reinforcement Learning. - Ching-An Cheng; Andrey Kolobov; Adith Swaminathan (NeurIPS, 2021)

Recommendations as Treatments. - Thorsten Joachims; Ben London 001; Yi Su; Adith Swaminathan; Lequn Wang (AI Mag., 2021)

Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL. - Bogdan Mazoure; Paul Mineiro; Pavithra Srinath; Reza Sharifi Sedeh; Doina Precup; Adith Swaminathan (CoRR, 2021)

Hindsight Learning for MDPs with Exogenous Inputs. - Sean R. Sinclair; Felipe Frujeri; Ching-An Cheng; Adith Swaminathan (CoRR, 2022)

Hindsight Learning for MDPs with Exogenous Inputs. - Sean R. Sinclair; Felipe Vieira Frujeri; Ching-An Cheng; Luke Marshall; Hugo de Oliveira Barbalho; Jingling Li; Jennifer Neville; Ishai Menache; Adith Swaminathan (ICML, 2023)

Interactive Robot Learning from Verbal Correction. - Huihan Liu; Alice Chen; Yuke Zhu; Adith Swaminathan; Andrey Kolobov; Ching-An Cheng (CoRR, 2023)

LLF-Bench: Benchmark for Interactive Learning from Language Feedback. - Ching-An Cheng; Andrey Kolobov; Dipendra Misra; Allen Nie; Adith Swaminathan (CoRR, 2023)

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs. - Ching-An Cheng; Allen Nie; Adith Swaminathan (NeurIPS, 2024)

How to Solve Contextual Goal-Oriented Problems with Offline Datasets? - Ying Fan; Jingling Li; Adith Swaminathan; Aditya Modi 002; Ching-An Cheng (NeurIPS, 2024)

On Overcoming Miscalibrated Conversational Priors in LLM-based ChatBots. - Christine Herlihy; Jennifer Neville; Tobias Schnabel; Adith Swaminathan (UAI, 2024)

AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks. - Jiacen Xu; Jack W. Stokes; Geoff McDonald; Xuesong Bai; David Marshall 001; Siyue Wang; Adith Swaminathan; Zhou Li 001 (CoRR, 2024)

The Importance of Directional Feedback for LLM-based Optimizers. - Allen Nie; Ching-An Cheng; Andrey Kolobov; Adith Swaminathan (CoRR, 2024)

Trace is the New AutoDiff - Unlocking Efficient Optimization of Computational Workflows. - Ching-An Cheng; Allen Nie; Adith Swaminathan (CoRR, 2024)

Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders. - Kaushal Paneri; Michael Munje; Kailash Singh Maurya; Adith Swaminathan; Yifan Shi (CoRR, 2024)

Lost in Transmission: When and Why LLMs Fail to Reason Globally. - Tobias Schnabel; Kiran Tomlinson; Adith Swaminathan; Jennifer Neville (CoRR, 2025)

A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. - Trenton Chang; Tobias Schnabel; Adith Swaminathan; Jenna Wiens (CoRR, 2025)

Provably Learning from Language Feedback. - Wanqiao Xu; Allen Nie; Ruijie Zheng; Aditya Modi 002; Adith Swaminathan; Ching-An Cheng (CoRR, 2025)