156 papers
This paper addresses the problem of set-membership localization using noisy range measurements by characterizing the nonconvex set of consistent points and developing efficient convex programming methods to compute tight, guaranteed outer-bounding boxes or ellipsoids, as well as inner approximations, without relying on semidefinite programming relaxations.
This paper proposes a hybrid methodology combining theoretical modeling with empirical benchmarking to accurately determine the optimal allocation of Prefill-Decode disaggregated hardware resources for Large Language Model inference while satisfying throughput, SLO, and request characteristic constraints.
This paper presents complete diagrammatic axiomatisations for Kullback-Leibler and Rényi divergences by characterizing them as quantitative enrichments of stochastic matrix categories under both Kronecker product and direct sum monoidal structures within a graphical string diagram framework.
This paper introduces Dictionary Based Pattern Entropy (DPE), a novel framework that combines Algorithmic and Shannon Information Theories to infer causal directions and identify driving subpatterns in symbolic sequences by quantifying how compact, rule-based patterns in a cause systematically reduce uncertainty in an effect, demonstrating robust performance across diverse synthetic and real-world datasets.
This paper introduces a geometric analysis of a recently proposed family of geometric analog error correction codes to characterize their m-height profiles, thereby expanding the limited range of available code families for handling multiple outliers in crossbar-based in-memory computing.
This paper proposes minimax-optimal online conformal prediction algorithms for non-stationary data streams with distribution drift, utilizing drift detection to adaptively update calibration sets and leveraging model stability to achieve optimal training-conditional cumulative regret.
This paper introduces xBound, the first theoretical framework for computing provable join size lower bounds, which addresses the critical issue of cardinality underestimation in production databases by correcting 23.6% of errors and delivering up to 20.1x query speedups on Microsoft's Fabric Data Warehouse.
This paper proposes and proves the asymptotic optimality of new coded distributed computing schemes for ring networks, demonstrating that while redundant computation yields an additive reduction in communication load, increasing the broadcast distance provides a multiplicative gain for both all-gather and all-to-all data exchange scenarios.
This paper proposes an enhanced iterative belief propagation decoder for quantum Tanner codes that groups check nodes into generalized checks decoded via maximum a posteriori methods, demonstrating significant performance gains over existing decoders in finite-length settings while identifying limited benefits for other qLDPC code families through both empirical results and theoretical cycle analysis.
This paper introduces a new class of smooth conditional entropies based on measured Rényi divergences to establish a tightened one-shot bound for quantum privacy amplification that significantly improves existing results, recovers optimal asymptotic error exponents, and proves optimality up to logarithmic terms.
The paper introduces the Pivotal Information Criterion (PIC), a continuous optimization method that dynamically selects its penalty parameter at the detection boundary to overcome the high false discovery rates and computational infeasibility of traditional criteria like BIC and AIC in high-dimensional settings.
This paper provides a rigorous generalization of Rosenbloom and Tsfasman's algebraic-geometric codes to arbitrary genus curves by defining differential Goppa codes via -jets and Hasse-Schmidt derivatives, analyzing their structural properties and distance variations, and establishing that they encompass all linear codes on while strictly generalizing classical Goppa codes.
This paper demonstrates that while centralized precoding in cell-free massive MIMO is theoretically superior, its performance advantage vanishes under realistic per-AP power constraints, rendering distributed precoding a more robust and practical alternative.
This paper proposes a positioning-assisted beamforming approach to reduce CSI estimation overhead in massive MIMO systems by deriving closed-form expressions for the outage probability of joint Gaussian beams and optimizing their patterns for both 2D and 3D scenarios.
This book surveys recent significant advances in the list decoding of Reed-Solomon and related polynomial-based codes, highlighting developments that achieve information-theoretic capacity with optimal list sizes using efficient, nearly-linear, and sublinear-time algorithms.
This paper constructs linear codes over finite fields from arbitrary simplicial complexes by linking their topological properties to coding parameters, enabling the explicit control of code metrics through geometric operations and the creation of optimal binary codes.
This paper demonstrates that any non-adaptive -query relaxed locally decodable code (RLDC) with sufficiently small soundness error can be converted into a standard -query locally decodable code (LDC) with comparable parameters, thereby generalizing previous separation results and yielding improved lower bounds for RLDCs, relaxed locally correctable codes (RLCCs), and probabilistically checkable proofs of proximity (PCPPs).
This paper proposes a novel framework for efficient CSI feedback in FDD massive MIMO systems by reformulating the task as a masked token prediction problem and employing an information-theoretic self-information-based mask selection strategy to optimize Large Language Model performance.
This paper presents an information-theoretic framework that optimizes experimental stimulus distributions to maximize the distinguishability between competing probabilistic neural coding hypotheses (likelihood vs. posterior encoding) by quantifying the expected performance gap between their respective decoders.
This paper completes Le Cam's program by establishing a necessary and sufficient condition for the existence of nontrivial hypothesis tests between two sets of probability measures without requiring a common dominating measure, specifically by utilizing the closures of their convex hulls within the space of bounded finitely additive measures.