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Ziv Goldfeld

15 papers · 2019–2024 · 5 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+5 more ↓ 🐣 Hot Topic Early Bird 🌍 Conference Polyglot Β· Silver πŸƒ Academic Marathon Β· Sprint πŸŒ‰ Interdisciplinary Bridge Β· Bronze πŸ—ΊοΈ Taxonomy Completionist (23)
πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (68) πŸ’Ž Century Club Β· Bronze πŸ”₯ Unstoppable Β· Bronze ⚑ Prolific Year Β· Productive

Venues

NeurIPS (6) AISTATS (4) ICML (2) JMLR (2) COLT (1)

Top co-authors

Kristjan Greenewald (6) Sloan Nietert (5) Kengo Kato (4) Zhengxin Zhang (2) Sreejith Sreekumar (2) Soroosh Shafiee (2) Youssef Mroueh (1) Rachel Cummings (1) Galen Reeves (1) Brian Kingsbury (1)

Research topics

Machine Learning (14) Mathematics & Optimization (13) Optimization & Theory (12) Core Methods (7) Mathematics (7) Optimization (7) Deep Learning (3) Learning Types Β· Machine Learning (3) Learning Types Β· Deep Learning (2) Application Areas (1) Bayesian & Probabilistic (1) Techniques (1)

Keywords

wasserstein distance (7) optimal transport (5) robust estimation (3) mutual information (3) minimax optimality (2) entropic regularization (2) minimum distance estimation (2) convergence rate (2) sliced mutual information (2) distributionally robust optimization (2) gaussian smoothing (2) information theory (2) entropic gromov-wasserstein (1) sliced wasserstein distance (1) variational inference (1) high-dimensional inference (1) representation learning (1) canonical correlation analysis (1) function approximation (1) dimensionality reduction (1)

Papers

Entropic Gromov-Wasserstein Distances: Stability and Algorithms JMLR 2024 Robust Distribution Learning with Local and Global Adversarial Corruptions (extended abstract) COLT 2024 Max-Sliced Mutual Information NeurIPS 2023 Outlier-Robust Wasserstein DRO NeurIPS 2023 k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension NeurIPS 2022 Cycle Consistent Probability Divergences Across Different Spaces AISTATS 2022 Neural Estimation of Statistical Divergences JMLR 2022 Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis AISTATS 2022 Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances NeurIPS 2022 Non-asymptotic Performance Guarantees for Neural Estimation of f-Divergences AISTATS 2021 Sliced Mutual Information: A Scalable Measure of Statistical Dependence NeurIPS 2021 Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications ICML 2021 Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance NeurIPS 2020 Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency AISTATS 2020 Estimating Information Flow in Deep Neural Networks ICML 2019