Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Dynamic Diameter in High-Dimensions against Adaptive Adversary and Beyond
Authors: Kiarash Banihashem, Jeff Giliberti, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 34,341 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,700 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper makes a significant theoretical contribution and does not include experimental results. |
| Researcher Affiliation | Academia | Kiarash Banihashem University of Maryland College Park, MD, USA EMAIL; Jeff Giliberti University of Maryland College Park, MD, USA EMAIL; Samira Goudarzi University of Maryland College Park, MD, USA EMAIL; Mohammad Taghi Hajiaghayi University of Maryland College Park, MD, USA EMAIL; Peyman Jabbarzade University of Maryland College Park, MD, USA EMAIL; Morteza Monemizadeh TU Eindhoven Eindhoven, The Netherlands EMAIL |
| Pseudocode | Yes | Algorithm 1 APPROXIMATEDIAMETERQUERY(P, d, ε, t, δ); Algorithm 2 APPROXIMATEDIAMETERINSERTION(P, p); Algorithm 3 APPROXIMATEDIAMETERDELETION(P, p); Algorithm 4 DE-AMORTIZEDCENTERPOINTCOMPUTATION(d, ε); Algorithm 5 INIT(P, d, k, ε); Algorithm 6 CLUSTERING(X, d, ε, ℓ, i 1); Algorithm 7 CLUSTERINGINSERTION(P 1, , P ℓ, p, Cℓ, Cℓwhere ℓ [L]); Algorithm 8 CLUSTERINGDELETION(P 1, , P ℓ, p, Cℓ, Cℓwhere ℓ [L]) |
| Open Source Code | No | The answer NA means that paper does not include experiments requiring code. |
| Open Datasets | No | The paper makes a significant theoretical contribution and does not include experimental results. |
| Dataset Splits | No | The paper makes a significant theoretical contribution and does not include experimental results. |
| Hardware Specification | No | The paper makes a significant theoretical contribution and does not include experimental results. |
| Software Dependencies | No | The paper makes a significant theoretical contribution and does not include experimental results. |
| Experiment Setup | No | The paper makes a significant theoretical contribution and does not include experimental results. |