Kristjan Greenewald
32 papers · 2017–2025 · 5 venues · across top CS/AI venues
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Achievements
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๐บ๏ธ Taxonomy Completionist (56) ๐ Academic Marathon ยท Sprint ๐ Conference Polyglot ยท Silver ๐ Interdisciplinary Bridge ยท Silver ๐ฃ Hot Topic Early Bird
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Keyword Collector
(148)
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Renaissance Researcher
(6)
๐ฅ
Unstoppable ยท Bronze
โก
Prolific Year ยท Productive
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Century Club ยท Bronze
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Trend Setter
Top co-authors
Research topics
Machine Learning
(25)
Artificial Intelligence
(17)
Core AI
(14)
Optimization & Theory
(14)
Mathematics & Optimization
(10)
Core Methods
(9)
Deep Learning
(7)
Mathematics
(7)
Learning Types
(5)
Optimization
(5)
Application Areas
(4)
Learning Paradigms
(3)
Models
(3)
Knowledge & Reasoning
(2)
Bayesian & Probabilistic
(2)
Keywords
optimal transport
(6)
mutual information
(4)
causal inference
(4)
large language model
(3)
causal discovery
(3)
information theory
(3)
stochastic dominance
(3)
generalization bound
(3)
causal graph
(2)
differential privacy
(2)
treatment effect
(2)
wasserstein distance
(2)
approximation algorithm
(2)
minimum entropy coupling
(2)
latent variable model
(2)
deep neural network
(2)
feature extraction
(2)
variational inference
(2)
structural causal model
(2)
entropic causal inference
(2)
Papers
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
ICML 2025
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
ICLR 2025
Asymmetry in Low-Rank Adapters of Foundation Models
ICML 2024
Distributional Preference Alignment of LLMs via Optimal Transport
NeurIPS 2024
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
NeurIPS 2024
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
NeurIPS 2024
Risk Aware Benchmarking of Large Language Models
ICML 2024
Score Distillation via Reparametrized DDIM
NeurIPS 2024
Slicing Mutual Information Generalization Bounds for Neural Networks
ICML 2024
Thermometer: Towards Universal Calibration for Large Language Models
ICML 2024
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
NeurIPS 2023
Learning Proximal Operators to Discover Multiple Optima
ICLR 2023
Max-Sliced Mutual Information
NeurIPS 2023
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier
AISTATS 2023
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
NeurIPS 2023
k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
NeurIPS 2022
Entropic Causal Inference: Graph Identifiability
ICML 2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
ICML 2022
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
AISTATS 2021
Improving approximate optimal transport distances using quantization
UAI 2021
Measuring Generalization with Optimal Transport
NeurIPS 2021
Sliced Mutual Information: A Scalable Measure of Statistical Dependence
NeurIPS 2021
Active Structure Learning of Causal DAGs via Directed Clique Trees
NeurIPS 2020
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
NeurIPS 2020
Entropic Causal Inference: Identifiability and Finite Sample Results
NeurIPS 2020
Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency
AISTATS 2020
Bayesian Nonparametric Federated Learning of Neural Networks
ICML 2019
Estimating Information Flow in Deep Neural Networks
ICML 2019
Sample Efficient Active Learning of Causal Trees
NeurIPS 2019
Statistical Model Aggregation via Parameter Matching
NeurIPS 2019
Action Centered Contextual Bandits
NeurIPS 2017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
NeurIPS 2017