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We study how to construct compressed datasets that suffice to recover optimal decisions in linear programs with an unknown cost vector $c$ lying in a prior set $\mathcal{C}$. Recent work by Bennouna et al. provides an exact geometric…

Optimization and Control · Mathematics 2026-05-25 Yuhan Ye , Saurabh Amin , Asuman Ozdaglar

We investigate the complexity of several fundamental polynomial-time solvable problems on graphs and on matrices, when the given instance has low treewidth; in the case of matrices, we consider the treewidth of the graph formed by non-zero…

Data Structures and Algorithms · Computer Science 2015-11-05 Fedor V. Fomin , Daniel Lokshtanov , Michał Pilipczuk , Saket Saurabh , Marcin Wrochna

Although recent provable methods have been developed to compute preimage bounds for neural networks, their scalability is fundamentally limited by the #P-hardness of the problem. In this work, we adopt a novel probabilistic perspective,…

Machine Learning · Computer Science 2025-11-18 Luca Marzari , Manuele Bicego , Ferdinando Cicalese , Alessandro Farinelli

We consider supervised learning with random decision trees, where the tree construction is completely random. The method is popularly used and works well in practice despite the simplicity of the setting, but its statistical mechanism is…

Machine Learning · Computer Science 2015-02-06 Mariusz Bojarski , Anna Choromanska , Krzysztof Choromanski , Yann LeCun

Since the breakthrough superpolynomial multilinear formula lower bounds of Raz (Theory of Computing 2006), proving such lower bounds against multilinear algebraic branching programs (mABPs) has been a longstanding open problem in algebraic…

Computational Complexity · Computer Science 2026-05-12 Deepanshu Kush

The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of supervised gradual domain adaptation, where labeled data…

Machine Learning · Computer Science 2022-11-15 Jing Dong , Shiji Zhou , Baoxiang Wang , Han Zhao

Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for prediction, that work does not address policy design, because the…

Machine Learning · Computer Science 2022-10-27 Ali Behrouz , Mathias Lecuyer , Cynthia Rudin , Margo Seltzer

We study the computational complexity of approximating general constrained Markov decision processes. Our primary contribution is the design of a polynomial time $(0,\epsilon)$-additive bicriteria approximation algorithm for finding optimal…

Data Structures and Algorithms · Computer Science 2025-02-12 Jeremy McMahan

There are many classical problems in P whose time complexities have not been improved over the past decades. Recent studies of "Hardness in P" have revealed that, for several of such problems, the current fastest algorithm is the best…

Data Structures and Algorithms · Computer Science 2017-10-24 Yoichi Iwata , Tomoaki Ogasawara , Naoto Ohsaka

This paper develops a new technique for proving amortized, randomized cell-probe lower bounds on dynamic data structure problems. We introduce a new randomized nondeterministic four-party communication model that enables "accelerated",…

Data Structures and Algorithms · Computer Science 2016-04-12 Omri Weinstein , Huacheng Yu

We extend classical methods of computational complexity to the realm of distributed computing, where they sometimes prove more effective than in their original context. Our focus is on decision problems in the LOCAL model, a setting in…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-09-08 Fabian Reiter

We consider the message complexity of verifying whether a given subgraph of the communication network forms a tree with specific properties both in the KT-$\rho$ (nodes know their $\rho$-hop neighborhood, including node IDs) and the KT-$0$…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-05-01 Shay Kutten , Peter Robinson , Ming Ming Tan

In coding theory, a common question is to understand the threshold rates of various local properties of codes, such as their list decodability and list recoverability. A recent work Levi, Mosheiff, and Shagrithaya (FOCS 2025) gave a novel…

Information Theory · Computer Science 2025-10-16 Joshua Brakensiek , Yeyuan Chen , Manik Dhar , Zihan Zhang

The approximate degree of a Boolean function $f(x_{1},x_{2},\ldots,x_{n})$ is the minimum degree of a real polynomial that approximates $f$ pointwise within $1/3$. Upper bounds on approximate degree have a variety of applications in…

Computational Complexity · Computer Science 2018-01-16 Alexander A. Sherstov

We study the {\em verification} problem in distributed networks, stated as follows. Let $H$ be a subgraph of a network $G$ where each vertex of $G$ knows which edges incident on it are in $H$. We would like to verify whether $H$ has some…

Distributed, Parallel, and Cluster Computing · Computer Science 2011-10-18 Atish Das Sarma , Stephan Holzer , Liah Kor , Amos Korman , Danupon Nanongkai , Gopal Pandurangan , David Peleg , Roger Wattenhofer

We study the approximability of the NP-complete \textsc{Maximum Minimal Feedback Vertex Set} problem. Informally, this natural problem seems to lie in an intermediate space between two more well-studied problems of this type:…

Computational Complexity · Computer Science 2021-02-12 Louis Dublois , Tesshu Hanaka , Mehdi Khosravian Ghadikolaei , Michael Lampis , Nikolaos Melissinos

Sorting has a natural generalization where the input consists of: (1) a ground set $X$ of size $n$, (2) a partial oracle $O_P$ specifying some fixed partial order $P$ on $X$ and (3) a linear oracle $O_L$ specifying a linear order $L$ that…

Data Structures and Algorithms · Computer Science 2024-08-01 Ivor van der Hoog , Daniel Rutschmann

In 2009, Roeglin and Teng showed that the smoothed number of Pareto optimal solutions of linear multi-criteria optimization problems is polynomially bounded in the number $n$ of variables and the maximum density $\phi$ of the semi-random…

Data Structures and Algorithms · Computer Science 2015-03-17 Tobias Brunsch , Heiko Roeglin

Consider a set of labels $L$ and a set of trees ${\mathcal T} = \{{\mathcal T}^{(1), {\mathcal T}^{(2), ..., {\mathcal T}^{(k) \$ where each tree ${\mathcal T}^{(i)$ is distinctly leaf-labeled by some subset of $L$. One fundamental problem…

Data Structures and Algorithms · Computer Science 2008-02-21 Viet Tung Hoang , Wing-Kin Sung

We develop a new technique for proving cell-probe lower bounds for static data structures. Previous lower bounds used a reduction to communication games, which was known not to be tight by counting arguments. We give the first lower bound…

Computational Complexity · Computer Science 2007-05-23 Mihai Patrascu , Mikkel Thorup
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