arXiv research

Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.

This work maps Boltzmann distributions to ARNNs for better physics-based model approximations.

In the present work, the optimal portfolio minimizing the investment risk with cost is discussed analytically, where this objective function is constructed in terms of two negative aspects of investment, the risk and cost. We note the mathematical similarity between the Hamiltonian in the mean-variance model and the Ha…

New algorithm nearly achieves ground state free energy of SK model.

We propose a general framework for solving statistical mechanics of systems with finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural networks, which support direct sampling and exact calculation of normalized probability of configurations. It computes variation…

New method uses quantum annealing and VAN for better statistical mechanics calculations.

New tools in nonlinear random matrices improve understanding of the Sum of Squares hierarchy.

We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from a prior that is i.i.d. across coordinates, we prove that the log-likelihood ratio of the spiked model against the non-spiked one is asymptotically normal below a certai…

AMP algorithms can be efficiently simulated by SDPs even with corrupted data.

Using methods of statistical physics, we analyse the error of learning couplings in large Ising models from independent data (the inverse Ising problem). We concentrate on learning based on local cost functions, such as the pseudo-likelihood method for which the couplings are inferred independently for each spin. Assum…

New algorithm Momentum-QNG improves optimization of quantum circuits.

Many problems in real life can be converted to combinatorial optimization problems (COPs) on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve bec…

Evolutionary strategy optimizes quantum circuit design and parameters.

A new algorithm optimizes graph problems faster and more accurately.

Study higher-order spin glass models for social network behavior with peer-group effects.

CLuP achieves near optimal ground state energies for positive and negative Hopfield models.

Unified approach learns Ising models from various dynamics and data types.

We describe and analyze some novel approaches for studying the dynamics of Ising spin glass models. We first briefly consider the variational approach based on minimizing the Kullback-Leibler divergence between independent trajectories and the real ones and note that this approach only coincides with the mean field equ…

Estimates natural parameters of p-tensor Ising models efficiently.

Quantum algorithm speeds up MIP solving by a near-quadratic factor.

The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.

The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.

Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.

The study examines how model predictions hold up under model extensions.

Revises Bayesian model averaging for foundation models.

Paper introduces symmetric divergence link models for probability distributions.

New method to handle credit portfolio model uncertainties.

The paper tests stock return models and uses LSTM to predict stock returns.

Researchers review challenges in interpreting additive models, especially neural additive models.

Novel hybrid modeling combines ML and physics for real-time diagnosis.

CRS model improves ranking data modeling with theoretical guarantees.

Sigma models linked to Gross-Neveu models via quiver varieties.

Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…

Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model a…

Matryoshka hides secret models in a carrier model, achieving high capacity and robustness.

Seq2Seq models speed up epidemic model predictions.

This work develops scalable model selection methods with fast update and selection.

Paper proposes BMPO to optimize policies using bidirectional models.

Copulas outperform marginal models in multivariate risk forecasting, reducing model risk by narrowing down the set of models.

BayesBlend blends multiple models' predictions for better insurance loss predictions.

The paper identifies when larger models improve predictions and proposes a switcher model.

Improved diffusion model generation speed with speculative sampling.

We propose a generalization of neural network sequence models. Instead of predicting one symbol at a time, our multi-scale model makes predictions over multiple, potentially overlapping multi-symbol tokens. A variation of the byte-pair encoding (BPE) compression algorithm is used to learn the dictionary of tokens that …

The paper extends statistical inference methods for black-box generative models.

PMM uses Bayesian inference to generate data from noisy approximations.

Unified model improves sampling speed and quality.

In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic mar…

Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications. In this paper, we propose a novel type of model where interpretable models compete and collaborate with black-box models. We present the Model-Agnostic Linear Comp…