Timezone: »
Opening Remarks
Thorsten Joachims · Nathan Kallus · Michele Santacatterina · Adith Swaminathan · David Sontag · Angela Zhou
Sat Dec 14 08:45 AM -- 09:00 AM (PST) @
Author Information
Thorsten Joachims (Cornell)
Nathan Kallus (Cornell University)
Michele Santacatterina (TRIPODS Center for Data Science - Cornell University)
Adith Swaminathan (Microsoft Research)
David Sontag (MIT)
Angela Zhou (Cornell University)
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