arXiv subjects

Inbal Livni Navon

Publications and source records attributed to Inbal Livni Navon.

5 recordsLinked to original sources

arXiv2026

Fairness in Limited Resources Settings

In recent years many important societal decisions are made by machine-learning algorithms, and many such important decisions have strict capacity limits, allowing resources to be allocated only to the highest utility individuals. For example, allocating physician appointments to the patients most likely to have some medical condition, or choosing which children will attend a special program. When performing such decisions, we consider both the prediction aspect of the decision and the resource allocation aspect. In this work we focus on the fairness of the decisions in such settings. The fairness aspect here is critical as the resources are limited, and allocating the resources to one individual leaves less resources for others. When the decision involves prediction together with the resource allocation, there is a risk that information gaps between different populations will lead to a very unbalanced allocation of resources. We address settings by adapting definitions from resource allocation schemes, identifying connections between the algorithmic fairness definitions and resource allocation ones, and examining the trade-offs between fairness and utility. We analyze the price of enforcing the different fairness definitions compared to a strictly utility-based optimization of the predictor, and show that it can be unbounded. We introduce an adaptation of proportional fairness and show that it has a bounded price of fairness, indicating greater robustness, and propose a variant of equal opportunity that also has a bounded price of fairness.

cs.CY↗

arXiv2023

Viderman's algorithm for quantum LDPC codes

Quantum low-density parity-check (LDPC) codes, a class of quantum error correcting codes, are considered a blueprint for scalable quantum circuits. To use these codes, one needs efficient decoding algorithms. In the classical setting, there are multiple efficient decoding algorithms available, including Viderman's algorithm (Viderman, TOCT 2013). Viderman's algorithm for classical LDPC codes essentially reduces the error-correction problem to that of erasure-correction, by identifying a small envelope $L$ that is guaranteed to contain the error set. Our main result is a generalization of Viderman's algorithm to quantum LDPC codes, namely hypergraph product codes (Tillich, Zémor, IEEE T-IT, 2013). This is the first erasure-conversion algorithm that can correct up to $Ω(D)$ errors for constant-rate quantum LDPC codes, where $D$ is the distance of the code. In that sense, it is also fundamentally different from existing decoding algorithms, in particular from the small-set-flip algorithm (Leverrier, Tillich, Zémor, FOCS, 2015). Moreover, in some parameter regimes, our decoding algorithm improves on the decoding radius of existing algorithms. We note that we do not yet have linear-time erasure-decoding algorithms for quantum LDPC codes, and thus the final running time of the whole decoding algorithm is not linear; however, we view our linear-time envelope-finding algorithm as an important first step.

quant-ph↗

arXiv2023

Bidding Strategies for Proportional Representation in Advertisement Campaigns

Many companies rely on advertising platforms such as Google, Facebook, or Instagram to recruit a large and diverse applicant pool for job openings. Prior works have shown that equitable bidding may not result in equitable outcomes due to heterogeneous levels of competition for different types of individuals. Suggestions have been made to address this problem via revisions to the advertising platform. However, it may be challenging to convince platforms to undergo a costly re-vamp of their system, and in addition it might not offer the flexibility necessary to capture the many types of fairness notions and other constraints that advertisers would like to ensure. Instead, we consider alterations that make no change to the platform mechanism and instead change the bidding strategies used by advertisers. We compare two natural fairness objectives: one in which the advertisers must treat groups equally when bidding in order to achieve a yield with group-parity guarantees, and another in which the bids are not constrained and only the yield must satisfy parity constraints. We show that requiring parity with respect to both bids and yield can result in an arbitrarily large decrease in efficiency compared to requiring equal yield proportions alone. We find that autobidding is a natural way to realize this latter objective and show how existing work in this area can be extended to provide efficient bidding strategies that provide high utility while satisfying group parity constraints as well as deterministic and randomized rounding techniques to uphold these guarantees. Finally, we demonstrate the effectiveness of our proposed solutions on data adapted from a real-world employment dataset.

cs.CY↗

arXiv2021

List Decoding with Double Samplers

We strengthen the notion of "double samplers", first introduced by Dinur and Kaufman [Proc. 58th FOCS, 2017], which are samplers with additional combinatorial properties, and whose existence we prove using high dimensional expanders. The ABNNR code construction [IEEE Trans. Inform. Theory, 38(2):509--516, 1992] achieves large distance by starting with a base code $C$ with moderate distance, and then amplifying the distance using a sampler. We show that if the sampler is part of a larger double sampler then the construction has an efficient list-decoding algorithm. Our algorithm works even if the ABNNR construction is not applied to a base code $C$ but to any string. In this case the resulting code is approximate-list-decodable, i.e. the output list contains an approximation to the original input. Our list-decoding algorithm works as follows: it uses a local voting scheme from which it constructs a unique games constraint graph. The constraint graph is an expander, so we can solve unique games efficiently. These solutions are the output of the list decoder. This is a novel use of a unique games algorithm as a subroutine in a decoding procedure, as opposed to the more common situation in which unique games are used for demonstrating hardness results. Double samplers and high dimensional expanders are akin to pseudorandom objects in their utility, but they greatly exceed random objects in their combinatorial properties. We believe that these objects hold significant potential for coding theoretic constructions and view this work as demonstrating the power of double samplers in this context.

cs.CC↗

arXiv2016

Cube vs. Cube Low Degree Test

We revisit the Raz-Safra plane-vs.-plane test and study the closely related cube vs. cube test. In this test the tester has access to a "cubes table" which assigns to every cube a low degree polynomial. The tester randomly selects two cubes (affine sub-spaces of dimension $3$) that intersect on a point $x\in \mathbf{F}^m$, and checks that the assignments to the cubes agree with each other on the point $x$. Our main result is a new combinatorial proof for a low degree test that comes closer to the soundness limit, as it works for all $ε\ge poly(d)/{\mathbf{F}}^{1/2}$, where $d$ is the degree. This should be compared to the previously best soundness value of $ε\ge poly(m, d)/\mathbf{F}^{1/8}$. Our soundness limit improves upon the dependence on the field size and does not depend on the dimension of the ambient space. Our proof is combinatorial and direct: unlike the Raz-Safra proof, it proceeds in one shot and does not require induction on the dimension of the ambient space. The ideas in our proof come from works on direct product testing which are even simpler in the current setting thanks to the low degree. Along the way we also prove a somewhat surprising fact about connection between different agreement tests: it does not matter if the tester chooses the cubes to intersect on points or on lines: for every given table, its success probability in either test is nearly the same.

cs.CC↗