Uri Shalit
33 papers · 2009–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (13) π Renaissance Researcher (5) π Interdisciplinary Bridge π£ Hot Topic Early Bird
π
Conference Polyglot
(9)
π
Renaissance Researcher
(5)
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Cross-Pollinator
(14)
π
Keyword Trendsetter Combo
(4)
π₯
Mega-Team
(20)
π
Grand Slam
π¬
Deep Specialist
(12)
π
Keyword Champion
(5)
π§¬
Topic Evolution
π
Century Club
(33)
β‘
Prolific Year
(5)
π
Conference Pioneer
π
Trend Setter
β
The Questioner
π₯
Unstoppable
(10)
ποΈ
Keyword Collector
(114)
Conferences
NIPS (11)
ICML (10)
JMLR (3)
AAAI (2)
AISTATS (2)
ICLR (2)
ACL (1)
MLHC (1)
UAI (1)
Top co-authors
Keywords
causal inference
(13)
hidden confounding
(5)
online learning
(5)
domain adaptation
(4)
reinforcement learning
(3)
treatment effect estimation
(2)
matrix factorization
(2)
observational study
(2)
low-rank matrix
(2)
manifold optimization
(2)
representation learning
(2)
causal effect estimation
(2)
metric learning
(2)
regret bound
(2)
covariate shift
(2)
sensitivity analysis
(2)
similarity learning
(2)
uncertainty quantification
(2)
image similarity
(2)
treatment effect
(2)
Papers
Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition
AISTATS 2025
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
ICML 2025
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
AISTATS 2025
BIG-Bench Extra Hard
ACL 2025
Set Valued Predictions For Robust Domain Generalization
ICML 2025
When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding
NIPS 2024
Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds
ICLR 2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
ICML 2023
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
ICLR 2022
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
JMLR 2022
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
NIPS 2022
Reinforcement Learning with a Terminator
NIPS 2022
Bandits with partially observable confounded data
UAI 2021
On Calibration and Out-of-Domain Generalization
NIPS 2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
NIPS 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
ICML 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
ICML 2021
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
NIPS 2020
Robust Learning with the Hilbert-Schmidt Independence Criterion
ICML 2020
Using deep networks for scientific discovery in physiological signals
MLHC 2020
A causal view of compositional zero-shot recognition
NIPS 2020
Off-Policy Evaluation in Partially Observable Environments
AAAI 2020
Building Causal Graphs from Medical Literature and Electronic Medical Records
AAAI 2019
Removing Hidden Confounding by Experimental Grounding
NIPS 2018
Estimating individual treatment effect: generalization bounds and algorithms
ICML 2017
Causal Effect Inference with Deep Latent-Variable Models
NIPS 2017
Learning Representations for Counterfactual Inference
ICML 2016
Coordinate-descent for learning orthogonal matrices through Givens rotations
ICML 2014
Modeling Musical Influence with Topic Models
ICML 2013
Online Learning in the Embedded Manifold of Low-rank Matrices
JMLR 2012
Online Learning in The Manifold of Low-Rank Matrices
NIPS 2010
Large Scale Online Learning of Image Similarity Through Ranking
JMLR 2010
An Online Algorithm for Large Scale Image Similarity Learning
NIPS 2009