regularizer
Country:
- North America > United States > Washington > King County > Seattle (0.04)
- North America > Dominican Republic (0.04)
Technology:
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents > Agent Societies (0.94)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.74)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.68)
Country:
- North America > United States > California > Alameda County > Berkeley (0.14)
- Asia > Middle East > Jordan (0.04)
- North America > Canada (0.04)
Technology:
Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations Arun Jambulapati Kevin Tian
We develop a deeper understanding of its relationship with more conventional analyses of extragradient methods [Nem04, Nes07]. We also give improved solvers for the subproblems required by variants of the [She17] algorithm, designed through the lens of relative smoothness [BBT17, LFN18].
Country:
- Asia > Middle East > Jordan (0.04)
- North America > United States > Virginia (0.04)
- North America > United States > Texas > Travis County > Austin (0.04)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
Technology:
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis > Accuracy (0.68)
A Unified Framework for Rank-based Loss Minimization
The empirical risk function is the cornerstone of machine learning.
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis > Accuracy (0.68)
Technology:
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms.
Country:
- Asia > Japan > Honshū > Kansai > Kyoto Prefecture > Kyoto (0.04)
- Asia > Japan > Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.34)
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms.
Country:
- Asia > Japan > Honshū > Kansai > Kyoto Prefecture > Kyoto (0.04)
- Asia > Japan > Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.34)
Country:
- Europe > Italy > Lombardy > Milan (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > Canada > Ontario > National Capital Region > Ottawa (0.04)
- Europe > Italy > Liguria > Genoa (0.04)
Technology:
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Data Science > Data Mining > Big Data (0.46)