Anna Korba

29 papers · 2016–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🏃 Academic Marathon (9) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (6) 🗺️ Taxonomy Completionist (36) 🏆 Keyword Champion (4) 🗃️ Keyword Collector (87) 💎 Century Club (29) 📈 Trend Setter ⚡ Prolific Year (6) 🔥 Unstoppable (10)

Conferences

ICML (10) NIPS (8) AISTATS (5) ALT (2) ICLR (2) JMLR (1) UAI (1)

Top co-authors

Arthur Gretton (4) Stéphan Clémençon (3) Pierre-Cyril Aubin-Frankowski (3) David Rohde (3) Imad Aouali (3) Victor-Emmanuel Brunel (3) Adil SALIM (3) Clément Bonet (3) Eric Sibony (3) Christophe Vauthier (2)

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