← Authors

Nathan Kallus

66 papers · 2017–2025 · 9 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (9) 🌈 Renaissance Researcher (9) 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (8) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (6) 🏠 Conference Loyalist (23) 🐺 Lone Wolf (8) πŸ‘‘ Triple Crown πŸ† Keyword Champion (11) 🀝 Dynamic Duo (15) πŸ”¬ Deep Specialist (21) πŸ—ƒοΈ Keyword Collector (205) ⚑ Prolific Year (8) πŸ’Ž Century Club (66) πŸ”₯ Unstoppable (9) ❓ The Questioner πŸ“ˆ Trend Setter

Conferences

NIPS (23) ICML (20) AISTATS (11) COLT (4) JMLR (4) ALT (1) CVPR (1) EMNLP (1) ICLR (1)

Top co-authors

Masatoshi Uehara (15) Wen Sun (11) Andrew Bennett (10) Xiaojie Mao (8) Angela Zhou (8) Kaiwen Wang (8) Miruna Oprescu (5) James McInerney (3) Aurelien Bibaut (3) Dawen Liang (3)

Keywords

causal inference (24) off-policy evaluation (11) regret bound (8) observational datum (7) contextual bandit (7) policy learning (7) doubly robust estimator (6) reinforcement learning (5) markov decision process (5) doubly robust (5) instrumental variable (4) policy evaluation (4) conditional average treatment effect (4) semiparametric efficiency (4) function approximation (4) importance sampling (3) off-policy learning (3) heterogeneous treatment effect (3) policy gradient (2) multi-armed bandit (2)

Papers

Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits AISTATS 2025
A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents ICML 2025
Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits ICML 2025
LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience EMNLP 2025
Anytime-Valid A/B Testing of Counting Processes AISTATS 2025
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty AISTATS 2025
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond JMLR 2024
Contextual Linear Optimization with Bandit Feedback NIPS 2024
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes NIPS 2024
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data NIPS 2024
Low-rank MDPs with Continuous Action Spaces AISTATS 2024
Provable Offline Preference-Based Reinforcement Learning ICLR 2024
Switching the Loss Reduces the Cost in Batch Reinforcement Learning ICML 2024
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams ICML 2024
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments ICML 2024
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning ICML 2024
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage NIPS 2023
Robust and Agnostic Learning of Conditional Distributional Treatment Effects AISTATS 2023
Provable Safe Reinforcement Learning with Binary Feedback AISTATS 2023
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs NIPS 2023
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR ICML 2023
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning NIPS 2023
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings ICML 2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding ICML 2023
Smooth Non-stationary Bandits ICML 2023
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness COLT 2023
Inference on Strongly Identified Functionals of Weakly Identified Functions COLT 2023
The Implicit Delta Method NIPS 2022
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects JMLR 2022
Stateful Offline Contextual Policy Evaluation and Learning AISTATS 2022
Estimating Structural Disparities for Face Models CVPR 2022
Learning Bellman Complete Representations for Offline Policy Evaluation ICML 2022
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning ICML 2022
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems NIPS 2022
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment NIPS 2022
Optimal Off-Policy Evaluation from Multiple Logging Policies ICML 2021
Fast Rates for the Regret of Offline Reinforcement Learning COLT 2021
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders AISTATS 2021
Post-Contextual-Bandit Inference NIPS 2021
Control Variates for Slate Off-Policy Evaluation NIPS 2021
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning NIPS 2021
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes COLT 2020
Efficient Policy Learning from Surrogate-Loss Classification Reductions ICML 2020
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training ICML 2020
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation ICML 2020
Statistically Efficient Off-Policy Policy Gradients ICML 2020
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes JMLR 2020
Generalized Optimal Matching Methods for Causal Inference JMLR 2020
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies NIPS 2020
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning NIPS 2020
Policy Evaluation with Latent Confounders via Optimal Balance NIPS 2019
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds NIPS 2019
Deep Generalized Method of Moments for Instrumental Variable Analysis NIPS 2019
Classifying Treatment Responders Under Causal Effect Monotonicity ICML 2019
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding AISTATS 2019
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric NIPS 2019
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning NIPS 2019
Causal Inference with Noisy and Missing Covariates via Matrix Factorization NIPS 2018
Policy Evaluation and Optimization with Continuous Treatments AISTATS 2018
Confounding-Robust Policy Improvement NIPS 2018
Residual Unfairness in Fair Machine Learning from Prejudiced Data ICML 2018
Balanced Policy Evaluation and Learning NIPS 2018
Removing Hidden Confounding by Experimental Grounding NIPS 2018
Instrument-Armed Bandits ALT 2018
Recursive Partitioning for Personalization using Observational Data ICML 2017
A Framework for Optimal Matching for Causal Inference AISTATS 2017