arXiv Machine Learning
2d ago
Identifiability and Order-Dimension Limits of In-Context Learning on Partial Orders
arXiv:2608. 14004v1 Announce Type: new Abstract: In-context learning is commonly formalized as inference from examples of a function.
Representative Sets in Propositional Abduction
arXiv:2607. 21183v1 Announce Type: cross Abstract: The propositional abduction problem is a well-known form of non-monotonic reasoning where we are asked to find an explanation of a given manifestation.
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arXiv AI
Aug 5
Hypercubes, Hyperplanes, and Constraint-Induced Complexity Collapse in Atomic Concept Learning
arXiv:2608. 02930v1 Announce Type: new Abstract: We revisit higher-arity atomic concept learning through the geometry of hypercubes and hyperplanes of ground instances.
arXiv AI
Jul 8
ExplAIner: A Declarative Query Language for Explaining Classification Models
arXiv:2607. 06407v1 Announce Type: new Abstract: The XAI community has studied a wide range of queries and scores for explaining predictions of ML models.
arXiv AI
Jul 28
Maximum Satisfiability of Simple Temporal Problems
arXiv:2607. 23785v1 Announce Type: cross Abstract: The Simple Temporal Problem (STP) is a core framework for quantitative temporal constraints.
arXiv AI
Jun 3
Towards Non-Monotonic Entailment in Propositional Defeasible Standpoint Logic
arXiv:2606. 03655v1 Announce Type: new Abstract: Recent work in defeasible reasoning has seen notions of preferential semantics and entailment in the style of Kraus et al.
arXiv AI
Jun 18
The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics for ELbot
arXiv:2606. 19197v1 Announce Type: cross Abstract: Abduction is a central approach to explain missing entailments from a knowledge base by providing a hypothesis, that would, if added to the knowledge base, make the missing entailment become true.
Hugging Face Trending Papers
Jul 26
Maximum Satisfiability of Simple Temporal Problems
The Simple Temporal Problem (STP) is a core framework for quantitative temporal constraints. As STP data can be inconsistent, we study MAXSTP: compute a maximum-cardinality consistent subset of constraints.
Hugging Face Trending Papers
Jun 2
Towards Non-Monotonic Entailment in Propositional Defeasible Standpoint Logic
Recent work in defeasible reasoning has seen notions of preferential semantics and entailment in the style of Kraus et al. applied to modal logics.
arXiv Machine Learning
Aug 4
Optimal Unambiguous DNFs and Alon-Saks-Seymour
arXiv:2608. 02533v1 Announce Type: cross Abstract: We construct unambiguous DNFs having width $O(n)$ but $0$-certificate complexity $\Omega(n^2)$.
arXiv Machine Learning
Jul 14
Precedence-Constrained Decision Trees and Coverings
arXiv:2602. 21312v4 Announce Type: replace-cross Abstract: This work considers a number of optimization problems and reductive relations between them.
arXiv Machine Learning
Jun 2
How (and when) can you fit examples to logic-based hypothesis classes over infinite structures?
arXiv:2606. 01107v1 Announce Type: cross Abstract: We study fitting problems, sometimes called ``training problems'', where we have a finite sample consisting of inputs and outputs, and we want to know whether there is a function in a certain class that could produce these outputs, exactly or approximately, on the given inputs.
Hugging Face Trending Papers
Aug 3
Optimal Unambiguous DNFs and Alon-Saks-Seymour
We construct unambiguous DNFs having width $O(n)$ but $0$-certificate complexity $Ω(n^2)$. By utilizing the special structure of these DNFs, we prove a lifting theorem with a constant-sized gadget that lifts the DNF to a communication problem, while losslessly translating the separation in certificate complexity to a separation in communication complexity.