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Mhna Massalha

dblp:401/0379 · DBLP profile ↗
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1ranked-venue papers
0as 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 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
1 paper
Approximation and online algorithms · 77% Mathematical optimization · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Approximation and online algorithms › online learning
blackwell approachability
0.912025
Blackwell's Approachability with Approximation Algorithms · COLT 2025

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

downward closure · 0.9approximation algorithm · 0.9approachability · 0.9
YearPublicationVenuePosition
2025 Blackwell's Approachability with Approximation Algorithms
abstract
We revisit Blackwell’s celebrated approachability problem which considers a repeated vector-valued game between a player and an adversary. Motivated by settings in which the action set of the player or adversary (or both) is difficult to optimize over, for instance when it corresponds to the set of all possible solutions to some NP-Hard optimization problem, we ask what can the player guarantee \textit{efficiently}, when only having access to these sets via approximation algorithms with ratios $\alpha_{\mX} \geq 1$ and $ 1 \geq \alpha_{\mY} > 0$, respectively. Assuming the player has monotone preferences, i.e., that he does not prefer a vector-valued loss $\ell_1$ over $\ell_2$ if $\ell_2 \leq \ell_1$, we establish that given a Blackwell instance with an approachable target set $S$, the downward closure of the appropriately-scaled set $\alpha_{\mX}\alpha_{\mY}^{-1}S$ is \textit{efficiently} approachable with optimal rate. In case only the player’s or adversary’s set is equipped with an approximation algorithm, we give simpler and more efficient algorithms.
Dan Garber, Mhna Massalha
COLT2