Anna Korba
29 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
Achievements
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๐ Academic Marathon (9) ๐งญ Keyword Pioneer ๐ Interdisciplinary Bridge ๐ Conference Polyglot (7) ๐ Cross-Pollinator (15)
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Cross-Pollinator
(15)
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Renaissance Researcher
(6)
๐บ๏ธ
Taxonomy Completionist
(36)
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Keyword Champion
(4)
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Keyword Collector
(87)
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Century Club
(29)
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Trend Setter
โก
Prolific Year
(6)
๐ฅ
Unstoppable
(10)
Conferences
ICML (10)
NIPS (8)
AISTATS (5)
ALT (2)
ICLR (2)
JMLR (1)
UAI (1)
Top co-authors
Research topics
Keywords
optimal transport
(6)
kernel methods
(4)
wasserstein gradient flow
(4)
maximum mean discrepancy
(3)
mirror descent
(3)
stein variational gradient descent
(3)
probabilistic modeling
(3)
particle-based method
(2)
kl divergence
(2)
bayesian inference
(2)
particle method
(2)
kernel stein discrepancy
(2)
wasserstein space
(2)
gradient descent
(2)
probability distribution
(2)
ranking datum
(2)
expectation maximization
(1)
primal-dual optimization
(1)
variational inference
(1)
preference learning
(1)
Papers
Density Ratio Estimation with Conditional Probability Paths
ICML 2025
(De)-regularized Maximum Mean Discrepancy Gradient Flow
JMLR 2025
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
AISTATS 2025
Implicit Diffusion: Efficient optimization through stochastic sampling
AISTATS 2025
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
AISTATS 2025
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
ICML 2025
Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis
ICML 2025
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics
ICLR 2025
A connection between Tempering and Entropic Mirror Descent
ICML 2024
Mirror and Preconditioned Gradient Descent in Wasserstein Space
NIPS 2024
Constrained Sampling with Primal-Dual Langevin Monte Carlo
NIPS 2024
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
NIPS 2024
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
UAI 2024
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
ICML 2024
Exponential Smoothing for Off-Policy Learning
ICML 2023
Sampling with Mollified Interaction Energy Descent
ICLR 2023
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
NIPS 2022
Accurate Quantization of Measures via Interacting Particle-based Optimization
ICML 2022
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff
AISTATS 2022
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
ICML 2021
Kernel Stein Discrepancy Descent
ICML 2021
The Wasserstein Proximal Gradient Algorithm
NIPS 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
NIPS 2020
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
ALT 2019
Maximum Mean Discrepancy Gradient Flow
NIPS 2019
A Structured Prediction Approach for Label Ranking
NIPS 2018
Ranking Median Regression: Learning to Order through Local Consensus
ALT 2018
A Learning Theory of Ranking Aggregation
AISTATS 2017
Controlling the distance to a Kemeny consensus without computing it
ICML 2016