Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Online Learning for Adversaries with Memory: Price of Past Mistakes
Authors: Oren Anava, Elad Hazan, Shie Mannor
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low regret. The first algorithm applies to Lipschitz continuous loss functions, obtaining optimal regret bounds for both convex and strongly convex losses. The second algorithm attains the optimal regret bounds and applies more broadly to convex losses without requiring Lipschitz continuity, yet is more complicated to implement. We complement the theoretical results with two applications: statistical arbitrage in finance, and multi-step ahead prediction in statistics. |
| Researcher Affiliation | Academia | Oren Anava Technion Haifa, Israel EMAIL Elad Hazan Princeton University New York, USA EMAIL Shie Mannor Technion Haifa, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 1: Input: learning rate η > 0, σ-strongly convex and smooth regularization function R(x). 2: Choose x0, . . . , xm K arbitrarily. 3: for t = m to T do 4: Play xt and suffer loss ft(xt m, . . . , xt). 5: Set xt+1 = arg minx K n η Pt τ=m fτ(x) + R(x) o |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe actual experiments that involve training on a specific dataset. The applications sections (5 and 6) describe how the proposed algorithms could be used, but do not involve empirical training or dataset usage. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, hence no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe running experiments, hence no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe running experiments, hence no experimental setup details like hyperparameters or training configurations are provided. |