EDBT 2026 Demo / reviewers in the wild / expert
Antonio Molina Lovett
dblp:232/1903
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0002-1890-9517ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 3 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 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.
| Theoretical computer science
2 papers |
Approximation and online algorithms · 32% Mathematical optimization · 32% Graph algorithms and graph theory · 16% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Graph algorithms and graph theory › graph matching
maximum matching |
0.6 | 1 | 2022 | Max-Weight Online Stochastic Matching: Improved Approximations Against the Online Benchmark · EC 2022 |
Approximation and online algorithms
online algorithms |
0.6 | 1 | 2022 | Max-Weight Online Stochastic Matching: Improved Approximations Against the Online Benchmark · EC 2022 |
Approximation and online algorithms › online algorithms
online matching |
0.6 | 1 | 2022 | Max-Weight Online Stochastic Matching: Improved Approximations Against the Online Benchmark · EC 2022 |
Mathematical optimization › stochastic optimization › stochastic combinatorial optimization
online stochastic matching |
0.6 | 1 | 2022 | Max-Weight Online Stochastic Matching: Improved Approximations Against the Online Benchmark · EC 2022 |
Mathematical optimization › stochastic optimization › stochastic combinatorial optimization
stochastic matching |
0.6 | 1 | 2022 | Max-Weight Online Stochastic Matching: Improved Approximations Against the Online Benchmark · EC 2022 |
Combinatorics and discrete mathematics
permutation |
0.4 | 1 | 2019 | Optimal Regular Expressions for Permutations · ICALP 2019 |
Automata and formal languages › regular languages
regular expressions |
0.4 | 1 | 2019 | Optimal Regular Expressions for Permutations · ICALP 2019 |
Methods — techniques the papers use, named apart from their topics
polynomial-time approximation algorithm · 0.6divide-and-conquer · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Max-Weight Online Stochastic Matching: Improved Approximations Against the Online BenchmarkabstractIn this paper, we study max-weight stochastic matchings on online bipartite graphs under both vertex and edge arrivals. We focus on designing polynomial time approximation algorithms with respect to the online benchmark, which was first considered by Papadimitriou, Pollner, Saberi, and Wajc [EC'21]. Mark Braverman, Mahsa Derakhshan, Antonio Molina Lovett |
EC | 3 |
| 2022 | Computational aspects of sturdy and flimsy numbers
Trevor Clokie, Thomas F. Lidbetter, Antonio Molina Lovett, Jeffrey Shallit, Leon Witzman |
Theor. Comput. Sci. | 3 |
| 2019 | Optimal Regular Expressions for PermutationsabstractThe permutation language $P_n$ consists of all words that are permutations of a fixed alphabet of size $n$. Using divide-and-conquer, we construct a regular expression $R_n$ that specifies $P_n$. We then give explicit bounds for the length of $R_n$, which we find to be $4^n n^{-(\lg n)/4+Θ(1)}$, and use these bounds to show that $R_n$ has minimum size over all regular expressions specifying $P_n$. Antonio Molina Lovett, Jeffrey Shallit |
ICALP | 1 |