We stand with Ukraine

We stand with Ukraine

Nathan Kallus

Orcid: 0000-0003-1672-0507

According to our database¹, Nathan Kallus authored at least 106 papers between 2014 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

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In proceedings

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PhD thesis

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Links

On csauthors.net:

Bibliography

2024

Hessian-Free Laplace in Bayesian Deep Learning.

[BibT_eX]

[DOI]

James McInerney

,

CoRR, 2024

Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2024

Is Cosine-Similarity of Embeddings Really About Similarity?

[BibT_eX]

[DOI]

,

Chaitanya Ekanadham

,

CoRR, 2024

Switching the Loss Reduces the Cost in Batch Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

,

Samuel Robertson

,

James McInerney

,

,

,

Csaba Szepesvári

CoRR, 2024

Applied Causal Inference Powered by ML and AI.

[BibT_eX]

[DOI]

Victor Chernozhukov

,

Christian Hansen

,

,

Martin Spindler

,

Vasilis Syrgkanis

CoRR, 2024

More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

,

,

CoRR, 2024

Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams.

[BibT_eX]

[DOI]

,

,

CoRR, 2024

Multi-Armed Bandits with Interference.

[BibT_eX]

[DOI]

,

Peter I. Frazier

,

CoRR, 2024

2023

Treatment Effect Risk: Bounds and Inference.

[BibT_eX]

[DOI]

Manag. Sci., August, 2023

Stochastic Optimization Forests.

[BibT_eX]

[DOI]

,

Manag. Sci., April, 2023

The Power and Limits of Predictive Approaches to Observational Data-Driven Optimization: The Case of Pricing.

[BibT_eX]

[DOI]

Dimitris Bertsimas

,

INFORMS J. Optim., January, 2023

Faster Rates for Switchback Experiments.

[BibT_eX]

[DOI]

,

Sohom Bhattacharya

,

,

Christina Lee Yu

CoRR, 2023

Low-Rank MDPs with Continuous Action Spaces.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Off-Policy Evaluation for Large Action Spaces via Policy Convolution.

[BibT_eX]

[DOI]

Noveen Sachdeva

,

,

,

,

Julian J. McAuley

CoRR, 2023

Source Condition Double Robust Inference on Functionals of Inverse Problems.

[BibT_eX]

[DOI]

,

,

,

,

Vasilis Syrgkanis

,

Masatoshi Uehara

CoRR, 2023

JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

,

,

Immanuel Trummer

,

CoRR, 2023

Provable Offline Reinforcement Learning with Human Feedback.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

,

,

,

CoRR, 2023

Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness.

[BibT_eX]

[DOI]

,

,

,

,

Vasilis Syrgkanis

,

Masatoshi Uehara

CoRR, 2023

Refined Value-Based Offline RL under Realizability and Partial Coverage.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

,

,

CoRR, 2023

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

Haruka Kiyohara

,

,

Victor Chernozhukov

,

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Machine Learning, 2023

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

,

,

,

Proceedings of the International Conference on Machine Learning, 2023

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding.

[BibT_eX]

[DOI]

,

,

Marah Ghoummaid

,

,

,

Proceedings of the International Conference on Machine Learning, 2023

Smooth Non-stationary Bandits.

[BibT_eX]

[DOI]

,

,

,

Peter I. Frazier

Proceedings of the International Conference on Machine Learning, 2023

Minimax Instrumental Variable Regression and L<sub>2</sub> Convergence Guarantees without Identification or Closedness.

[BibT_eX]

[DOI]

,

,

,

,

Vasilis Syrgkanis

,

Masatoshi Uehara

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Inference on Strongly Identified Functionals of Weakly Identified Functions.

[BibT_eX]

[DOI]

,

,

,

,

Vasilis Syrgkanis

,

Masatoshi Uehara

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Large Language Models as Zero-Shot Conversational Recommenders.

[BibT_eX]

[DOI]

,

,

,

,

,

,

Bodhisattwa Prasad Majumder

,

,

Julian J. McAuley

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Robust and Agnostic Learning of Conditional Distributional Treatment Effects.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Provable Safe Reinforcement Learning with Binary Feedback.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

Oper. Res., November, 2022

Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes.

[BibT_eX]

[DOI]

,

,

Oper. Res., November, 2022

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination.

[BibT_eX]

[DOI]

,

,

Manag. Sci., 2022

Fast Rates for Contextual Linear Optimization.

[BibT_eX]

[DOI]

,

,

Manag. Sci., 2022

Data Pooling in Stochastic Optimization.

[BibT_eX]

[DOI]

,

Manag. Sci., 2022

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects.

[BibT_eX]

[DOI]

Fredrik D. Johansson

,

,

,

David A. Sontag

J. Mach. Learn. Res., 2022

A Review of Off-Policy Evaluation in Reinforcement Learning.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

,

CoRR, 2022

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Implicit Delta Method.

[BibT_eX]

[DOI]

,

James McInerney

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the International Conference on Machine Learning, 2022

Learning Bellman Complete Representations for Offline Policy Evaluation.

[BibT_eX]

[DOI]

Jonathan D. Chang

,

,

,

Proceedings of the International Conference on Machine Learning, 2022

Estimating Structural Disparities for Face Models.

[BibT_eX]

[DOI]

Shervin Ardeshir

,

Cristina Segalin

,

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Stateful Offline Contextual Policy Evaluation and Learning.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Minimax-Optimal Policy Learning Under Unobserved Confounding.

[BibT_eX]

[DOI]

,

Manag. Sci., 2021

Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding.

[BibT_eX]

[DOI]

,

,

CoRR, 2021

An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls.

[BibT_eX]

[DOI]

,

,

,

,

Richard Peterson

,

,

CoRR, 2021

Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes.

[BibT_eX]

[DOI]

,

CoRR, 2021

Residual Overfit Method of Exploration.

[BibT_eX]

[DOI]

James McInerney

,

CoRR, 2021

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach.

[BibT_eX]

[DOI]

,

,

Masatoshi Uehara

CoRR, 2021

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency.

[BibT_eX]

[DOI]

Masatoshi Uehara

,

Masaaki Imaizumi

,

,

,

,

CoRR, 2021

Control Variates for Slate Off-Policy Evaluation.

[BibT_eX]

[DOI]

,

Ashok Chandrashekar

,

Fernando Amat Gil

,

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.

[BibT_eX]

[DOI]

Aurélien Bibaut

,

,

Maria Dimakopoulou

,

Antoine Chambaz

,

Mark J. van der Laan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Post-Contextual-Bandit Inference.

[BibT_eX]

[DOI]

Aurélien Bibaut

,

Maria Dimakopoulou

,

,

Antoine Chambaz

,

Mark J. van der Laan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal Off-Policy Evaluation from Multiple Logging Policies.

[BibT_eX]

[DOI]

,

,

Masatoshi Uehara

Proceedings of the 38th International Conference on Machine Learning, 2021

Fairness, Welfare, and Equity in Personalized Pricing.

[BibT_eX]

[DOI]

,

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Fast Rates for the Regret of Offline Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

Masatoshi Uehara

Proceedings of the Conference on Learning Theory, 2021

Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

From Predictive to Prescriptive Analytics.

[BibT_eX]

[DOI]

Dimitris Bertsimas

,

Manag. Sci., 2020

Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

J. Mach. Learn. Res., 2020

Generalized Optimal Matching Methods for Causal Inference.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Dynamic Assortment Personalization in High Dimensions.

[BibT_eX]

[DOI]

,

Madeleine Udell

Oper. Res., 2020

The Variational Method of Moments.

[BibT_eX]

[DOI]

,

CoRR, 2020

Rejoinder: New Objectives for Policy Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

CoRR, 2020

DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret.

[BibT_eX]

[DOI]

,

CoRR, 2020

Comment: Entropy Learning for Dynamic Treatment Regimes.

[BibT_eX]

[DOI]

CoRR, 2020

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data.

[BibT_eX]

[DOI]

,

CoRR, 2020

Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Statistically Efficient Off-Policy Policy Gradients.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

Proceedings of the 37th International Conference on Machine Learning, 2020

Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

Proceedings of the 37th International Conference on Machine Learning, 2020

DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient Policy Learning from Surrogate-Loss Classification Reductions.

[BibT_eX]

[DOI]

,

Proceedings of the 37th International Conference on Machine Learning, 2020

Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes.

[BibT_eX]

[DOI]

,

,

Proceedings of the Conference on Learning Theory, 2020

2019

Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond.

[BibT_eX]

[DOI]

,

,

Masatoshi Uehara

CoRR, 2019

Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

CoRR, 2019

More Efficient Policy Learning via Optimal Retargeting.

[BibT_eX]

[DOI]

CoRR, 2019

Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds.

[BibT_eX]

[DOI]

,

CoRR, 2019

The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning.

[BibT_eX]

[DOI]

,

Masatoshi Uehara

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Generalized Method of Moments for Instrumental Variable Analysis.

[BibT_eX]

[DOI]

,

,

Tobias Schnabel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Policy Evaluation with Latent Confounders via Optimal Balance.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Classifying Treatment Responders Under Causal Effect Monotonicity.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved.

[BibT_eX]

[DOI]

,

,

,

,

Madeleine Udell

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding.

[BibT_eX]

[DOI]

,

,

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Robust sample average approximation.

[BibT_eX]

[DOI]

Dimitris Bertsimas

,

,

Math. Program., 2018

Data-driven robust optimization.

[BibT_eX]

[DOI]

Dimitris Bertsimas

,

,

Math. Program., 2018

Confounding-Robust Policy Improvement.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Removing Hidden Confounding by Experimental Grounding.

[BibT_eX]

[DOI]

,

Aahlad Manas Puli

,

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Causal Inference with Noisy and Missing Covariates via Matrix Factorization.

[BibT_eX]

[DOI]

,

,

Madeleine Udell

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Balanced Policy Evaluation and Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Residual Unfairness in Fair Machine Learning from Prejudiced Data.

[BibT_eX]

[DOI]

,

Proceedings of the 35th International Conference on Machine Learning, 2018

Instrument-Armed Bandits.

[BibT_eX]

[DOI]

Proceedings of the Algorithmic Learning Theory, 2018

Policy Evaluation and Optimization with Continuous Treatments.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Recursive Partitioning for Personalization using Observational Data.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

A Framework for Optimal Matching for Causal Inference.

[BibT_eX]

[DOI]

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Causal Inference by Minimizing the Dual Norm of Bias: Kernel Matching & Weighting Estimators for Causal Effects.

[BibT_eX]

[DOI]

Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016

Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices.

[BibT_eX]

[DOI]

,

Madeleine Udell

Proceedings of the 2016 ACM Conference on Economics and Computation, 2016

2015

The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples.

[BibT_eX]

[DOI]

Dimitris Bertsimas

,

,

Oper. Res., 2015

Learning Preferences from Assortment Choices in a Heterogeneous Population.

[BibT_eX]

[DOI]

,

Madeleine Udell

CoRR, 2015

2014

From Predictions to Data-Driven Decisions Using Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2014

Predicting crowd behavior with big public data.

[BibT_eX]

[DOI]

Proceedings of the 23rd International World Wide Web Conference, 2014

On the Predictive Power of Web Intelligence and Social Media - The Best Way to Predict the Future Is to tweet It.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics in the Social and Ubiquitous Context, 2014

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