More from the Same Authors

• 2021 : It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks »
  Michelle Bao · Angela Zhou · Samantha Zottola · Brian Brubach · Sarah Desmarais · Aaron Horowitz · Kristian Lum · Suresh Venkatasubramanian
• 2021 : Stateful Offline Contextual Policy Evaluation and Learning »
  Angela Zhou
• 2023 Poster: Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage »
  Masatoshi Uehara · Nathan Kallus · Jason Lee · Wen Sun
• 2023 Poster: The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning »
  Kaiwen Wang · Kevin Zhou · Runzhe Wu · Nathan Kallus · Wen Sun
• 2023 Poster: Future-Dependent Value-Based Off-Policy Evaluation in POMDPs »
  Masatoshi Uehara · Haruka Kiyohara · Andrew Bennett · Victor Chernozhukov · Nan Jiang · Nathan Kallus · Chengchun Shi · Wen Sun
• 2022 Panel: Panel 3C-5: Biologically-Plausible Determinant Maximization… & What's the Harm? ... »
  Bariscan Bozkurt · Nathan Kallus
• 2022 : Panel Discussion »
  Behnam Neyshabur · David Sontag · Pradeep Ravikumar · Erin Hartman
• 2022 Poster: Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems »
  Masatoshi Uehara · Ayush Sekhari · Jason Lee · Nathan Kallus · Wen Sun
• 2022 Poster: The Implicit Delta Method »
  Nathan Kallus · James McInerney
• 2022 Poster: What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment »
  Nathan Kallus
• 2021 Workshop: Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice »
  Aurelien Bibaut · Maria Dimakopoulou · Nathan Kallus · Xinkun Nie · Masatoshi Uehara · Kelly Zhang
• 2021 : Stateful Offline Contextual Policy Evaluation and Learning »
  Angela Zhou
• 2021 Workshop: Machine Learning Meets Econometrics (MLECON) »
  David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou
• 2021 Poster: Fairness in Ranking under Uncertainty »
  Ashudeep Singh · David Kempe · Thorsten Joachims
• 2021 Poster: Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning »
  Aurelien Bibaut · Nathan Kallus · Maria Dimakopoulou · Antoine Chambaz · Mark van der Laan
• 2021 Poster: Control Variates for Slate Off-Policy Evaluation »
  Nikos Vlassis · Ashok Chandrashekar · Fernando Amat · Nathan Kallus
• 2021 Poster: Post-Contextual-Bandit Inference »
  Aurelien Bibaut · Maria Dimakopoulou · Nathan Kallus · Antoine Chambaz · Mark van der Laan
• 2021 : It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks »
  Michelle Bao · Angela Zhou · Samantha Zottola · Brian Brubach · Sarah Desmarais · Aaron Horowitz · Kristian Lum · Suresh Venkatasubramanian
• 2020 Workshop: Consequential Decisions in Dynamic Environments »
  Niki Kilbertus · Angela Zhou · Ashia Wilson · John Miller · Lily Hu · Lydia T. Liu · Nathan Kallus · Shira Mitchell
• 2020 : Spotlight Talk 4: Fairness, Welfare, and Equity in Personalized Pricing »
  Nathan Kallus · Angela Zhou
• 2020 Poster: Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning »
  Nathan Kallus · Angela Zhou
• 2020 Poster: Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies »
  Nathan Kallus · Masatoshi Uehara
• 2020 Poster: MOReL: Model-Based Offline Reinforcement Learning »
  Rahul Kidambi · Aravind Rajeswaran · Praneeth Netrapalli · Thorsten Joachims
• 2020 Poster: Provably Good Batch Reinforcement Learning Without Great Exploration »
  Yao Liu · Adith Swaminathan · Alekh Agarwal · Emma Brunskill
• 2020 : Real World RL with Vowpal Wabbit: Beyond Contextual Bandits »
  John Langford · Marek Wydmuch · Maryam Majzoubi · Adith Swaminathan · · Dylan Foster · Paul Mineiro
• 2019 : Coffee Break and Poster Session »
  Rameswar Panda · Prasanna Sattigeri · Kush Varshney · Karthikeyan Natesan Ramamurthy · Harvineet Singh · Vishwali Mhasawade · Shalmali Joshi · Laleh Seyyed-Kalantari · Matthew McDermott · Gal Yona · James Atwood · Hansa Srinivasan · Yonatan Halpern · D. Sculley · Behrouz Babaki · Margarida Carvalho · Josie Williams · Narges Razavian · Haoran Zhang · Amy Lu · Irene Y Chen · Xiaojie Mao · Angela Zhou · Nathan Kallus
• 2019 Workshop: Machine Learning with Guarantees »
  Ben London · Gintare Karolina Dziugaite · Daniel Roy · Thorsten Joachims · Aleksander Madry · John Shawe-Taylor
• 2019 Workshop: “Do the right thing”: machine learning and causal inference for improved decision making »
  Michele Santacatterina · Thorsten Joachims · Nathan Kallus · Adith Swaminathan · David Sontag · Angela Zhou
• 2019 : Thorsten Joachim: Fair Ranking with Biased Data »
  Thorsten Joachims
• 2019 : Nathan Kallus: Efficiently Breaking the Curse of Horizon with Double Reinforcement Learning »
  Nathan Kallus
• 2019 Poster: The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric »
  Nathan Kallus · Angela Zhou
• 2019 Poster: Policy Learning for Fairness in Ranking »
  Ashudeep Singh · Thorsten Joachims
• 2019 Poster: Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds »
  Nathan Kallus · Angela Zhou
• 2019 Poster: Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning »
  Nathan Kallus · Masatoshi Uehara
• 2019 Poster: Policy Evaluation with Latent Confounders via Optimal Balance »
  Andrew Bennett · Nathan Kallus
• 2019 Poster: Deep Generalized Method of Moments for Instrumental Variable Analysis »
  Andrew Bennett · Nathan Kallus · Tobias Schnabel
• 2018 Workshop: Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy »
  Manuela Veloso · Nathan Kallus · Sameena Shah · Senthil Kumar · Isabelle Moulinier · Jiahao Chen · John Paisley
• 2018 Poster: Causal Inference with Noisy and Missing Covariates via Matrix Factorization »
  Nathan Kallus · Xiaojie Mao · Madeleine Udell
• 2018 Poster: Removing Hidden Confounding by Experimental Grounding »
  Nathan Kallus · Aahlad Puli · Uri Shalit
• 2018 Spotlight: Removing Hidden Confounding by Experimental Grounding »
  Nathan Kallus · Aahlad Puli · Uri Shalit
• 2018 Poster: Confounding-Robust Policy Improvement »
  Nathan Kallus · Angela Zhou
• 2018 Poster: Balanced Policy Evaluation and Learning »
  Nathan Kallus
• 2017 : Equality of Opportunity in Rankings »
  Thorsten Joachims · Ashudeep Singh
• 2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
  Ricardo Silva · Panagiotis Toulis · John Shawe-Taylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan
• 2017 Poster: Off-policy evaluation for slate recommendation »
  Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni
• 2017 Oral: Off-policy evaluation for slate recommendation »
  Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni
• 2016 : Panel Discussion »
  Gisbert Schneider · Ross E Goodwin · Simon Colton · Russ Salakhutdinov · Thorsten Joachims · Florian Pinel
• 2016 : Structured Prediction with Logged Bandit Feedback »
  Thorsten Joachims
• 2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
  Ricardo Silva · John Shawe-Taylor · Adith Swaminathan · Thorsten Joachims
• 2015 Poster: The Self-Normalized Estimator for Counterfactual Learning »
  Adith Swaminathan · Thorsten Joachims
• 2015 Spotlight: The Self-Normalized Estimator for Counterfactual Learning »
  Adith Swaminathan · Thorsten Joachims
• 2013 Poster: Learning Trajectory Preferences for Manipulators via Iterative Improvement »
  Ashesh Jain · Brian Wojcik · Thorsten Joachims · Ashutosh Saxena
• 2011 Poster: Semantic Labeling of 3D Point Clouds for Indoor Scenes »
  Hema Koppula · Abhishek Anand · Thorsten Joachims · Ashutosh Saxena
• 2007 Workshop: Machine Learning for Web Search »
  Denny Zhou · Olivier Chapelle · Thorsten Joachims · Thomas Hofmann