Kristjan Greenewald
32 papers · 2017–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (8) π§ Keyword Pioneer π Interdisciplinary Bridge π Conference Polyglot (5) π Cross-Pollinator (10)
π
Academic Marathon
(8)
π§
Keyword Pioneer
π
Cross-Pollinator
(10)
π₯
Unstoppable
(7)
π
Century Club
(32)
β‘
Prolific Year
(5)
π
Trend Setter
ποΈ
Keyword Collector
(118)
Conferences
NIPS (17)
ICML (9)
AISTATS (3)
ICLR (2)
UAI (1)
Top co-authors
Keywords
optimal transport
(5)
causal inference
(4)
mutual information
(3)
information theory
(3)
causal discovery
(3)
sliced mutual information
(2)
causal graph
(2)
approximation algorithm
(2)
structural causal model
(2)
deep neural network
(2)
differential privacy
(2)
wasserstein distance
(2)
treatment effect
(2)
entropic causal inference
(2)
stochastic dominance
(2)
variational inference
(2)
latent variable model
(2)
minimum entropy coupling
(2)
manifold learning
(1)
online learning
(1)
Papers
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
ICLR 2025
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
ICML 2025
Thermometer: Towards Universal Calibration for Large Language Models
ICML 2024
Risk Aware Benchmarking of Large Language Models
ICML 2024
Slicing Mutual Information Generalization Bounds for Neural Networks
ICML 2024
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
NIPS 2024
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
NIPS 2024
Asymmetry in Low-Rank Adapters of Foundation Models
ICML 2024
Score Distillation via Reparametrized DDIM
NIPS 2024
Distributional Preference Alignment of LLMs via Optimal Transport
NIPS 2024
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
NIPS 2023
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
NIPS 2023
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier
AISTATS 2023
Max-Sliced Mutual Information
NIPS 2023
Learning Proximal Operators to Discover Multiple Optima
ICLR 2023
Entropic Causal Inference: Graph Identifiability
ICML 2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
ICML 2022
$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
NIPS 2022
Improving approximate optimal transport distances using quantization
UAI 2021
Measuring Generalization with Optimal Transport
NIPS 2021
Sliced Mutual Information: A Scalable Measure of Statistical Dependence
NIPS 2021
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
AISTATS 2021
Entropic Causal Inference: Identifiability and Finite Sample Results
NIPS 2020
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
NIPS 2020
Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency
AISTATS 2020
Active Structure Learning of Causal DAGs via Directed Clique Trees
NIPS 2020
Estimating Information Flow in Deep Neural Networks
ICML 2019
Bayesian Nonparametric Federated Learning of Neural Networks
ICML 2019
Statistical Model Aggregation via Parameter Matching
NIPS 2019
Sample Efficient Active Learning of Causal Trees
NIPS 2019
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
NIPS 2017
Action Centered Contextual Bandits
NIPS 2017