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---
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license: mit
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task_categories:
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- text-classification
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tags:
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- AI
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- ICML
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- ICML2023
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---
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## ICML 2023 International Conference on Machine Learning 2023 Accepted Paper Meta Info Dataset |
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This dataset is collect from the ICML 2024 OpenReview website (https://openreview.net/group?id=ICML.cc/2023/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/icml2023). For researchers who are interested in doing analysis of ICML 2023 accepted papers and potential trends, you can use the already cleaned up json files. |
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Each row contains the meta information of a paper in the ICML 2023 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain. |
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### Meta Information of Json File |
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``` |
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{ |
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"abs": "https://proceedings.mlr.press/v202/aamand23a.html", |
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"Download PDF": "https://proceedings.mlr.press/v202/aamand23a/aamand23a.pdf", |
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"OpenReview": "https://openreview.net/forum?id=BVomXLJQoH", |
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"title": "Data Structures for Density Estimation", |
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"url": "https://proceedings.mlr.press/v202/aamand23a.html", |
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"authors": "Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal", |
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"detail_url": "https://proceedings.mlr.press/v202/aamand23a.html", |
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"tags": "ICML 2023", |
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"abstract": "We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \\ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is \"close\" to $p$. Our main result is the first data structure that, given a sublinear (in $n$) number of samples from $p$, identifies $v_i$ in time sublinear in $k$. We also give an improved version of the algorithm of Acharya et al. (2018) that reports $v_i$ in time linear in $k$. The experimental evaluation of the latter algorithm shows that it achieves a significant reduction in the number of operations needed to achieve a given accuracy compared to prior work." |
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} |
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``` |
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## Related |
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## AI Equation |
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[List of AI Equations and Latex](http://www.deepnlp.org/equation/category/ai) <br> |
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[List of Math Equations and Latex](http://www.deepnlp.org/equation/category/math) <br> |
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[List of Physics Equations and Latex](http://www.deepnlp.org/equation/category/physics) <br> |
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[List of Statistics Equations and Latex](http://www.deepnlp.org/equation/category/statistics) <br> |
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[List of Machine Learning Equations and Latex](http://www.deepnlp.org/equation/category/machine%20learning) <br> |
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## AI Agent Marketplace and Search |
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[AI Agent Marketplace and Search](http://www.deepnlp.org/search/agent) <br> |
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[Robot Search](http://www.deepnlp.org/search/robot) <br> |
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[Equation and Academic search](http://www.deepnlp.org/search/equation) <br> |
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[AI & Robot Comprehensive Search](http://www.deepnlp.org/search) <br> |
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[AI & Robot Question](http://www.deepnlp.org/question) <br> |
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[AI & Robot Community](http://www.deepnlp.org/community) <br> |
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[AI Agent Marketplace Blog](http://www.deepnlp.org/blog/ai-agent-marketplace-and-search-portal-reviews-2025) <br> |
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## AI Agent Reviews |
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[AI Agent Marketplace Directory](http://www.deepnlp.org/store/ai-agent) <br> |
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[Microsoft AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-microsoft-ai-agent) <br> |
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[Claude AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-claude-ai-agent) <br> |
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[OpenAI AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-openai-ai-agent) <br> |
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[Saleforce AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-salesforce-ai-agent) <br> |
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[AI Agent Builder Reviews](http://www.deepnlp.org/store/ai-agent/ai-agent-builder) <br> |
