Jacob D Abernethy

4 papers · 2021–2024 · 2 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

Jump to papers ↓

🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Bronze 🗺️ Taxonomy Completionist (10)

Venues

ICML (3) AISTATS (1)

Top co-authors

Venkatesh Saligrama (1) Andre Wibisono (1) Chi-Heng Lin (1) Jun-Kun Wang (1) Matthäus Kleindessner (1) Jamie Morgenstern (1) Bhuvesh Kumar (1) Molei Tao (1) Chris Russell (1) Pranjal Awasthi (1)

Research topics

Machine Learning (4) Optimization & Theory · Machine Learning (3) Mathematics & Optimization (2) Learning Types (2) Optimization (2) Deep Learning (1) Application Areas (1) Core Methods (1) Optimization & Theory · Deep Learning (1)

Keywords

convergence rate (2) online learning (1) convex optimization (1) variational inequality (1) neural network optimization (1) hedge algorithm (1) gradient descent (1) expert advice (1) riemannian manifold (1) regret bound (1) exponential weight (1) extragradient method (1) last-iterate convergence (1) linear network (1) group fairness (1) relu network (1) monotone operator (1) active sampling (1) loss minimization (1) multi-class prediction (1)

Papers

Extragradient Type Methods for Riemannian Variational Inequality Problems AISTATS 2024 ActiveHedge: Hedge meets Active Learning ICML 2022 Active Sampling for Min-Max Fairness ICML 2022 A Modular Analysis of Provable Acceleration via Polyak’s Momentum: Training a Wide ReLU Network and a Deep Linear Network ICML 2021