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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spectral Learning of Predictive State Representations with Insufficient Statistics
Authors: Alex Kulesza, Nan Jiang, Satinder Singh
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on both synthetic and real-world problems. Using synthetic HMMs, we show that our method is robust to learning under a variety of transition topologies; compared to a baseline using the shortest tests and histories, our method achieves error rates up to an order of magnitude lower. We also demonstrate significantly improved prediction results on a real-world language modeling task using a large collection of text from Wikipedia. |
| Researcher Affiliation | Academia | Computer Science & Engineering University of Michigan Ann Arbor, MI, USA |
| Pseudocode | Yes | Algorithm 1 Search for sets of k tests and histories that approximately minimize maxo σ1(Bo). Input: dataset D, initial T and H of size k, distributions p T /p H over candidate tests/histories, number of rounds r {Bo} := SPECTLEARN(D, T , H) σopt := maxo O σ1(Bo) for i = 1, . . . , r do Sample h H p H for h H do {Bo} := SPECTLEARN(D, T , H \ h {h}) σ(h ) := maxo O σ1(Bo) h = arg minh σ(h ) if σ(h ) < σopt then σopt := σ(h ) H := H \ h {h} [Repeat the same procedure for T ] Output: T ,H |
| Open Source Code | No | No explicit statement about open-source code for the described methodology or a link to a repository was found. |
| Open Datasets | Yes | Finally, we apply our algorithm to model a real-world text dataset of over 6.5 million sentences from Wikipedia articles (Sutskever, Martens, and Hinton 2011). |
| Dataset Splits | No | The majority of the data is used for training, but we reserve 100,000 sentences for evaluation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were provided for the experimental setup. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers or other detailed software dependencies. |
| Experiment Setup | Yes | Our algorithm is initialized at the baseline T and H, and we sample new tests and histories whose length is one observation longer than the longest sequences in the baseline sets; the sampling probability of a sequence x is proportional to p2(x). We run our algorithm for 10 rounds. |