Complexity
Computational complexity, P vs NP, and lower bounds
20 papers indexed
Explainable PQC: A Layered Interpretive Framework for Post-Quantum Cryptographic Security Assumptions
The paper proposes 'Explainable PQC,' a layered interpretive framework designed to structure and clarify how post-quantum cryptographic security assumptions are represented and communicated, particula…
Low-degree estimation thresholds in planted hypergraphs and tensor PCA
The paper analyzes low-degree estimation thresholds for recovering hidden signals in planted hypergraphs and tensor PCA, establishing sharp phase transitions and providing polynomial-time recovery alg…
Towards Worst-case Hardness for Low-Noise LPN
Divesh Aggarwal, Rishav Gupta, Hai Hoang Nguyen, Kel Zin Tan +1 more
The paper presents a new worst-case to average-case reduction for the Learning Parity with Noise (LPN) problem, achieving hardness for inverse-polynomial noise rates previously unattainable.
The Complexity of Verifying Feedforward Neural Networks in Quantised Settings
This paper analyzes the computational complexity of verifying feedforward neural networks when their weights are restricted to finite-width arithmetic, finding that verification remains NP-complete fo…
Diffusion-Robust Optimization over Graphs
The paper introduces a diffusion-based uncertainty model for robust optimization on graphs, showing that the resulting computational complexity depends critically on the interaction between the uncert…
Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation
The paper establishes information-theoretic lower bounds for stochastic optimization using low-bit gradients by reducing the problem to compressed Gaussian mean estimation, yielding sharp bounds on co…
Sketching Intersection Profiles: A Simple Proof and Three Applications
This paper settles the complexity of three sketching problems in graphs and distributions.
Collision Resistance of Single-Layer Neural Nets
The paper analyzes the algorithmic complexity of finding collisions in single-layer binary neural networks, establishing that the collision resistance depends critically on the activation function's t…
Multifidelity Proper Orthogonal Decomposition
The paper introduces Multifidelity Proper Orthogonal Decomposition (MFPOD), a method that significantly reduces the computational cost of dimension reduction by intelligently combining data from cheap…
Verifying global identifiability of parametric linear ODE models is NP-hard
This paper determines that verifying global parameter identifiability for linear ODE models is an NP-hard problem, establishing a computational complexity boundary for the field.
Privately Estimating Monotone Statistics in Polynomial Time
The paper introduces novel, efficient differentially private algorithms for estimating monotone statistics, significantly improving sample complexity compared to existing methods.
The Sample Complexity of Multiclass and Sparse Contextual Bandits
Liad Erez, Fan Chen, Alon Cohen, Tomer Koren +3 more
The paper analyzes the sample complexity of contextual bandits in the $s$-sparse setting, achieving optimal sample bounds for identifying an $\epsilon$-optimal policy.
LoRe: Adaptive Interaction-Evaluation Routing with Per-Step Interaction Budgets for Iterative Graph Solvers
LoRe is a training-free wrapper that dynamically budgets interaction evaluation at each step of graph solvers, significantly improving scalability and speed while maintaining solution quality.
Zero-determinant Strategy for Moving Target Defense: Existence, Performance, and Computation
Zhaoyang Cheng, Guanpu Chen, Yiguang Hong, Ming Cao +1 more
This paper proposes using a zero-determinant (ZD) strategy to construct an effective Moving Target Defense (MTD) that maintains performance comparable to the optimal Stackelberg equilibrium while dras…
Post-Quantum Cryptography from Quantum Stabilizer Decoding
The paper proposes that decoding random quantum stabilizer codes is a robust, novel post-quantum cryptographic assumption, demonstrating that its average-case hardness implies core primitives like PKE…
Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding
This paper compares PCA and LPC for dimensionality reduction in cyberattack classification, demonstrating that both techniques can achieve substantial feature compression with minimal loss of classifi…
Byte-level Object Bounds Protection
PRISM is a novel, precise object-bounds protection scheme that significantly reduces runtime overhead by encoding the object's end address directly into the pointer tag, thereby eliminating costly met…
Public Key Encryption from High-Corruption Constraint Satisfaction Problems
The paper introduces a novel public key encryption scheme with high security by leveraging the conjectured intractability of two types of highly corrupted constraint satisfaction problems (CSPs).
Hardness of Approximate Hylland-Zeckhauser Equilibria
The paper establishes that finding approximate Hylland-Zeckhauser equilibria (a type of market allocation) is computationally hard, specifically showing it is PPAD-hard under certain complexity assump…
Provably Secure Steganography Based on List Decoding
The paper proposes a provably secure steganography scheme based on list decoding that significantly increases embedding capacity for Large Language Models (LLMs) compared to existing methods.