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Soutrik Sarangi

dblp:420/0691 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 50% Representation and self-supervised learning · 50%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › geometric deep learning › set learning
set function learning
0.912025
Monotone and Separable Set Functions: Characterizations and Neural Models · NeurIPS 2025
Machine learning › Representation and self-supervised learning › representation learning › structured representation learning
set representation learning
0.912025
Monotone and Separable Set Functions: Characterizations and Neural Models · NeurIPS 2025

Methods — techniques the papers use, named apart from their topics

neural network architecture design · 0.9holder continuity · 0.9
YearPublicationVenuePosition
2025 Monotone and Separable Set Functions: Characterizations and Neural Models
abstract
Motivated by applications for set containment problems, we consider the following fundamental problem: can we design set-to-vector functions so that the natural partial order on sets is preserved, namely $S \subseteq T$ if and only if $F (S) \leq F (T )$. We call functions satisfying this property Monotone and Separating (MAS) set functions. We establish lower and upper bounds for the vector dimension necessary to obtain MAS functions, as a function of the cardinality of the multisets and the underlying ground set. In the important case of an infinite ground set, we show that MAS functions do not exist, but provide a model called MASNET which provably enjoys a relaxed MAS property we name “weakly MAS” and is stable in the sense of Holder continuity. We also show that MAS functions can be used to construct universal models that are monotone by construction and can approximate all monotone set functions. Experimentally, we consider a variety of set containment tasks. The experiments show the benefit of using our MASNET model, in comparison with standard set models which do not incorporate set containment as an inductive bias.
Soutrik Sarangi, Yonatan Sverdlov, Nadav Dym, Abir De
NeurIPS1