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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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48 results for mechanism equivalence

LDP is equivalent to contraction of E_γ-divergence, impacting privacy and utility.

problem Analyzing trade-offs between privacy and utility in estimation problems.
method Equivalence of LDP constraints to contraction coefficients of E_γ-divergence, using f-divergences and estimation-theoretic tools.
result LDP guarantees can be expressed in terms of contraction coefficients of arbitrary f-divergences.

Paper reviews algebraic research in machine learning theory.

problem Understanding phase transitions in machine learning models.
method Algebraic approaches in statistical mechanics.
result Algebraic methods are essential for analyzing machine learning models with singularities.

By analyzing the relationships between a socioeconomical system modeled through evolutionary game theory and a physical system modeled through quantum mechanics we show how although both systems are described through two theories apparently different both are analogous and thus exactly equivalents. The extensions of qu…

2007-04-30abs ↗pdf ↗

The paper defines and proves equivalence of nonholonomic brackets in contact mechanical systems.

problem Nonholonomic constraints in contact geometry.
method Construct a general framework for non-holonomic constraints, define and prove equivalence of different nonholonomic brackets.
result All nonholonomic brackets coincide and one is an almost Jacobi bracket.

The abstract discusses connecting quantum mechanics and algebraic index theories.

problem Exploring the connection between quantum mechanics and algebraic index theories.
method Explains how the classical algebraic index theorem can be proved in terms of BV quantization of topological quantum mechanics and 2d chiral CFT.
result Shows how the generating function of all genus Gromov-Witten invariants on elliptic curves is mirror equivalent to an elliptic chiral index.

These notes were inspired by the course ''Quantum Field Theory from a Functional Integral Point of View'' given at the University of Zurich in Spring 2017 by Santosh Kandel. We describe Feynman's path integral approach to quantum mechanics and quantum field theory from a functional integral point of view, where the mai…

2019-02-22abs ↗pdf ↗
Quantum Econophysicsphysics.soc-ph

The relationships between game theory and quantum mechanics let us propose certain quantization relationships through which we could describe and understand not only quantum but also classical, evolutionary and the biological systems that were described before through the replicator dynamics. Quantum mechanics could be…

2006-09-28abs ↗pdf ↗

New mechanism protects neural network weights from privacy attacks during self-supervised learning.

problem Privacy risks during fine-tuning stage of self-supervised learning.
method Proposes a novel differential privacy mechanism using additive logistic noise.
result Reduces membership inference attack accuracy to 50% while maintaining below 5% performance loss.

Unified framework reveals regularization mechanism in deep ReLU networks via convex optimization.

problem Understanding the success of deep neural networks.
method Developed a unified framework using convex optimization to reveal regularization mechanisms.
result ReLU networks can be globally optimized via convex programs, enforcing sparsity.

The paper proves a nonholonomic version of Maupertuis-Jacobi principle and shows that nonholonomic trajectories minimize length.

problem Nonholonomic dynamics and their length minimization.
method Contact bundle formulation and geometric equivalence between problems.
result Regular solutions of nonholonomic mechanical problems are reparametrizations of geodesics with minimized Riemannian length.

Unified framework for fairness, robustness, and distribution shifts.

problem Diverse failure modes of machine learning systems.
method Formalizes biases as violations of conditional independence and proves equivalence conditions.
result Equivalent effects of biases in different failure modes under specific conditions.

The usual formulations of time-dependent mechanics start from a given splitting Y=R×MY=R\times M of the coordinate bundle YRY\to R. From physical viewpoint, this splitting means that a reference frame has been chosen. Obviously, such a splitting is broken under reference frame transformations and time-dependent canonical …

1997-02-25abs ↗pdf ↗

In the present paper we discuss an independent on the Grothendieck-Sato isomorphism approach to the Riemann-Roch-Hirzebruch formula for an arbitrary differential operator. Instead of the Grothendieck-Sato isomorphism, we use the Topological Quantum Mechanics (more or less equivalent to the well-known constructions with…

2004-01-28abs ↗pdf ↗

Paper uses second-order differential geometry to study stochastic mechanics.

problem Stochastic differential equations and their symmetries.
method Develops second-order differential geometry to study symmetries of SDEs and constructs stochastic mechanics.
result Establishes stochastic Lagrangian and Hamiltonian mechanics and their relations with HJB equations.

Method detects neural network equivalence via matrix ensembles and spectral analysis.

problem Detecting equivalence among different deep learning architectures.
method Generating Mixed Matrix Ensembles (MMEs) and matching to conjugate circular ensembles.
result Empirical evidence shows vanishing differences in spectral densities with long tail decay rates.

Attention mechanisms in deep learning become Gaussian process-like as the number of heads increases.

problem Understanding the behavior of attention mechanisms in deep learning models.
method Extending the equivalence between wide neural networks and Gaussian processes to attention architectures.
result Multi-head attention architectures behave as Gaussian processes as the number of heads tends to infinity.

Optimal Gaussian noise mechanisms achieve nearly optimal error in unbiased mean estimation.

problem Efficiently estimating the mean of high-dimensional data while preserving privacy.
method Differential privacy mechanisms with Gaussian noise, focusing on optimal covariance.
result Gaussian noise mechanisms achieve nearly optimal error among all private unbiased mean estimation mechanisms.

Generating logical form equivalents of human language is a fresh way to employ neural architectures where long short-term memory effectively captures dependencies in both encoder and decoder units. The logical form of the sequence usually preserves information from the natural language side in the form of similar token…

2018-07-19abs ↗pdf ↗

Meta-causal states group equivalent qualitative causal dynamics, useful for analyzing system changes.

problem Qualitative changes in causal relationships due to agent actions or environmental tipping points.
method Propose meta-causal states to group causal models based on equivalent qualitative behavior and parameterize specific mechanisms.
result Meta-causal states can be inferred from observed agent behavior and disentangled from unlabeled data.

The paper develops a method for inferring second opinions from experts using counterfactual inference.

problem Designing efficient decision support systems for second opinions.
method Set invariant Gumbel-Max structural causal model for multiclass classification.
result The proposed model can infer second opinions more accurately than non-causal models.

A new type of knot energy is presented via real life experiments involving a thin resilient metallic tube. Knotted in different ways, the device mechanically acquires a uniquely determined (up to isometry) normal form at least when the original knot diagram has a small number of crossings, thus outperforming the famous…

2010-11-22abs ↗pdf ↗

Pion optimizes LLMs by preserving weight matrix singular values.

problem Training large language models (LLMs) with standard optimizers leads to unstable weight matrices.
method Pion uses orthogonal transformations to update weight matrices, preserving their singular values.
result Pion offers a stable alternative to standard optimizers for LLM pretraining and finetuning.

Exact 1-Wasserstein distance between location-scale distributions derived, with privacy effects studied.

problem Calculating the 1-Wasserstein distance between location-scale distributions and its impact on differential privacy.
method Exact expressions and special functions for 1-Wasserstein distance, new upper bounds, and asymptotic analysis.
result New linear upper bound and detailed asymptotic bounds for Gaussian case, effect of differential privacy studied.

Proposes MSS to identify causal structure from heterogeneous environments.

problem Distribution shifts between environments violate i.i.d. data assumption.
method Sparse mechanism shift hypothesis, score-based approach.
result Identifies entire causal structure with high probability.

Transformers' self-attention mechanism is mapped to a generalized Potts model.

problem Uncertainty in what type of data distribution self-attention can efficiently learn.
method Decouple word positions and embeddings, then show self-attention learns a generalized Potts model.
result Training self-attention is equivalent to solving the inverse Potts problem.