Agnostic Smoothed Online Regression with Adversarial Responses

arXiv:2609.32478 · stat.ML, cs.LG, math.ST, stat.TH · Submitted 2026-09-26 · Read on arXiv

stat.ML, cs.LG, math.ST, stat.TH

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Regret — 78× in this paper · explained in 3 episodes
• Decoupling — 8× in this paper · explained in 2 episodes
• VC Dimension — 8× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Minimax Regret — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Minimax optimality — 1× in this paper · explained in 2 episodes

Sources

• Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
• An Optimal Agnostic PAC Algorithm
• A uniform relative deviation inequality for VC-subgraph classes
• Expected Worst Case Regret via Stochastic Sequential Covering

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