VLDB 2026 Research / reviewers in the wild / expert
Soutrik Sarangi
dblp:420/0691
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › geometric deep learning › set learning
set function learning |
0.9 | 1 | 2025 | 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 |
0.9 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Monotone and Separable Set Functions: Characterizations and Neural ModelsabstractMotivated 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 |
NeurIPS | 1 |