Oracle efficient truncated statistics
Authors: Konstantinos Karatapanis, Vasilis Kontonis, Christos Tzamos
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we design a new learning method with runtime and query complexity polynomial in 1/α. Our result significantly improves over the prior works by focusing on efficiently solving the underlying optimization problem using a general purpose optimization algorithm with minimal assumptions. Our main contribution is a positive answer to the above question by providing a new analysis of the truncated negative log likelihood objective. The parameter θ that will satisfy the properties of the theorem will be the output of the Algorithm 1. |
| Researcher Affiliation | Academia | Konstantinos Karatapanis1,2, Vasilis Kontonis3, Christos Tzamos1,4 1 Archimedes, Athena Research Center, Greece 2 National Technical University of Athens 3 University of Texas at Austin 4 University of Athens EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for Minimizing L-Constrained Function Algorithm 2 Projected SGD Algorithm Given Truncated Samples and Sublevel Set Algorithm 3 Sample Gradient |
| Open Source Code | No | The paper does not contain any explicit statement about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using specific datasets. Therefore, it does not provide any information about publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies. Therefore, it does not provide information about dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific implementation details or list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not provide specific experimental setup details such as hyperparameter values or training configurations. |