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Ecole Polytechnique

Academic institutioneurope · fr
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Research library507linked papers
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Selected work

Representative Papers

Mirroring Call-by-Need, or Values Acting Silly

Feb 19, 2024International Conference on Formal Structures for Computation and Deduction

This paper addresses the compositional interaction of copying and erasing behaviors in λ-calculus, identifying a gap in understanding how inefficiencies in evaluation strategies arise from their interplay. Method: We introduce *call-by-silly*, a symmetrically degenerate evaluation strategy orthogonal to call-by-need, deliberately combining the redundant copying of call-by-name with the blind erasure of call-by-value. We formally define the call-by-silly calculus and—using rewriting theory and a tight multi-type system—rigorously prove its contextual equivalence to call-by-value. Moreover, we construct the first type system that precisely characterizes the length of longest reduction sequences. Contribution: Our work reveals that contextual equivalence is insensitive to operational inefficiencies; provides the first symmetric degenerate model exhibiting both worst-case copying and worst-case erasure; and achieves a decidable, type-based characterization of evaluation length—establishing a novel bridge between type-theoretic precision and quantitative operational semantics.

3 citationsRead paper

The"double"square-root law: Evidence for the mechanical origin of market impact using Tokyo Stock Exchange data

Feb 22, 2025

This paper addresses the long-standing debate on the microfoundations of price impact: whether it arises mechanically from order flow or informationally from informed trading. Using high-frequency, trader-identified order-level data from the Tokyo Stock Exchange (2012–2018), we provide the first empirical evidence of the square-root impact law at the individual order level and discover that its temporal decay follows an inverse square-root pattern—collectively termed the “double square-root law”: impact ∝ √volume × 1/√time. Through meta-order reconstruction, anonymized control experiments, and nonparametric impact curve estimation, we demonstrate the robustness of this law and show that synthetically reconstructed meta-orders replicate observed impact dynamics. Our findings strongly support a purely mechanical origin of price impact, offering the first high-resolution empirical validation for market microstructure theory and challenging the dominant informational paradigm.

2 citationsRead paper

LightSBB-M: Bridging Schr\"odinger and Bass for Generative Diffusion Modeling

Jan 27, 2026

This work addresses the computational bottleneck in jointly controlling drift and diffusion in generative diffusion models by proposing the LightSBB-M algorithm. Within the Schrödinger Bridge and Bass (SBB) joint modeling framework, it achieves the first analytical solution to the SBB problem. By leveraging a dual representation of the objective function, the method explicitly derives the optimal drift and diffusion coefficients and introduces a tunable parameter β to continuously interpolate between them, thereby unifying the Schrödinger bridge and Bass transport mechanisms. The approach significantly enhances both computational efficiency and generation quality: on synthetic data, it reduces the 2-Wasserstein distance by up to 32% compared to existing methods, and demonstrates high-fidelity unpaired image translation in the challenging task of adult-to-child face conversion on FFHQ.

1 citationsRead paper

Variants of Higher-Dimensional Automata

Jan 24, 2026

Higher-dimensional automata (HDA) are often too rigid in their formalism, limiting their direct applicability in modeling and reasoning. This work systematically integrates several weakened variants—such as HDA with interfaces, partial HDA, ST-automata, and relational HDA—and demonstrates, through formal language theory, automata transformations, and algebraic analysis, that these variants fall into only two distinct classes at the language level: those closed under inclusion and those that are not. The paper’s core contributions include the first proof that partial HDA satisfy a Kleene theorem and admit determinization, alongside the establishment of a unified framework that clarifies the expressive power boundaries among all considered variants. These results lay the foundational groundwork for regular expression characterizations and determinization procedures for partial HDA.

1 citationsRead paper

Categorical Reparameterization with Denoising Diffusion models

Jan 02, 2026arXiv.org

Gradient-based optimization of discrete categorical variables has long been hindered by either the high variance of score function estimators or the bias introduced by continuous relaxations. This work proposes a novel, training-free soft reparameterization approach by introducing denoising diffusion mechanisms to categorical variable modeling. Specifically, it constructs a differentiable sampler via a closed-form denoiser derived from a Gaussian noising process, enabling efficient gradient estimation with low bias. The method achieves optimization performance on par with or superior to existing techniques across multiple benchmark tasks, establishing a new paradigm for discrete optimization that is training-free, differentiable, and low-bias.

1 citationsRead paper
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