1.7k citations · 2.3k across the 34 of their papers we have counts for
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SCRuB: Social Concept Reasoning under Rubric-Based Evaluation
Jamelle Watson-Daniels, Himaghna Bhattacharjee, Skyler Wang +11
While many studies of Large Language Model (LLM) reasoning capabilities emphasize mathematical or technical tasks, few address reasoning about social concepts: the abstract ideas s…
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems
Maximilian Nickel
Rapid model validation via the train-test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a comb…
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel +2
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same…
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Maximilian Nickel, Douwe Kiela
We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores. For this purpose, we study different models of hyperbolic space a…
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe Kiela
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent h…
Relational Models
Volker Tresp, Maximilian Nickel
We provide a survey on relational models. Relational models describe complete networked {domains by taking into account global dependencies in the data}. Relational models can lead…
Holographic Embeddings of Knowledge Graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose…