Acceleration and Averaging in Stochastic Descent Dynamics
Authors: Walid Krichene, Peter L. Bartlett
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formulate and study a general family of (continuous-time) stochastic dynamics for accelerated first-order minimization of smooth convex functions. Building on an averaging formulation of accelerated mirror descent, we propose a stochastic variant in which the gradient is contaminated by noise, and study the resulting stochastic differential equation. We prove a bound on the rate of change of an energy function associated with the problem, then use it to derive estimates of convergence rates of the function values (almost surely and in expectation), both for persistent and asymptotically vanishing noise. |
| Researcher Affiliation | Collaboration | Walid Krichene Google, Inc. walidk@google.com Peter Bartlett U.C. Berkeley bartlett@cs.berkeley.edu |
| Pseudocode | No | The paper is highly theoretical and presents mathematical formulations and proofs, but it does not include pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. Therefore, no information about publicly available training data is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required for reproducibility of empirical experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |