DNAMite creates interpretable, calibrated survival analysis models.
problem Limited interpretability in survival analysis models, especially for healthcare applications.
method Feature discretization and kernel smoothing in embedding module for flexible shape functions.
result DNAMite produces calibrated shape functions interpretable as contributions to cumulative incidence function.
HDMR provides insights into machine learning models, aiding in both prediction and explanation.
problem Understanding and interpreting complex machine learning models.
method High Dimensional Model Representation (HDMR) and its applications in machine learning.
result HDMR offers a glass box approach to machine learning models, enhancing both prediction and explanation.
Recently proposed models which learn to write computer programs from data use either input/output examples or rich execution traces. Instead, we argue that a novel alternative is to use a glass-box loss function, given as a program itself that can be directly inspected. Glass-box optimization covers a wide range of pro…
Paper proposes hybrid approach for transparent credit scoring models.
problem Lack of transparency in machine learning models limits their use in regulated environments.
method Post-hoc interpretation of black-box models guides feature selection, followed by training glass-box models.
result Reduces feature usage from 106 to 10 while maintaining comparable performance.
Method uses elastic black-boxes to create interpretable models from complex ones.
problem Lack of trust and stability in opaque models and time-consuming feature engineering in interpretable models.
method Surrogate assisted feature extraction for model learning.
result Trains interpretable and accurate models without time-consuming feature engineering.
Automated feature engineering improves interpretable models without manual work.
problem Lack of interpretability in complex models causes trust and stability issues.
method Use elastic black-box models to create simpler, interpretable glass-box models.
result Extracted features from complex models improve linear model performance.
ParamBoost uses gradient boosting to create interpretable non-linear models with constraints.
problem Creating interpretable non-linear models with expert knowledge constraints.
method Gradient Boosting of cubic polynomials with specified constraints.
result ParamBoost outperforms state-of-the-art GAMs in real-world datasets.
Data science principles enhance AI interpretability for better user control.
problem Risks from opaque AI models without clear impacts.
method Synthesizes principles from interpretability literature, emphasizing audience goals.
result Illustrates basic techniques and criteria for evaluating interpretability.
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par w…
Optimal allocation between explainable and black box models for high performance and explainability.
problem Balancing explainability and performance in model ensembles.
method Optimal allocation of observations between explainable and black box models to maximize ensemble performance and explainability.
result Learned allocations maintain high ensemble performance and explainability, sometimes outperforming individual models.
Generative model learns spin-glass dynamics and properties.
problem Complex behavior of many-body systems in statistical physics and computer science.
method Self-supervised learning with normalizing flows.
result Key physical and computational properties of spin-glasses are learned.
Bayesian hybrid models correct for missing physics in machine learning.
problem Systematic bias in machine learning models.
method Fusing physics-based insights with machine learning constructs, using Bayesian calibration and stochastic programming.
result Bayesian hybrid models outperform pure machine learning approaches with less data.
We consider the problem of rational decision making in the presence of nonlinear constraints. By using tools borrowed from spin glass and random matrix theory, we focus on the portfolio optimisation problem. We show that the number of ``optimal'' solutions is generically exponentially large: rationality is thus de fact…
In an informal way, a number of thoughts on the financial crisis 2008 are presented from a physicist's viewpoint, considering the problem as a nonergodicity transition of a spin-glass type of system. Some tentative suggestions concerning the way out of the crisis are also discussed, concerning Keynesian "deficit spendi…
EBMs become opaque in high dimensions; LASSO sparsifies them.
problem Reducing complexity and improving interpretability of EBMs in high-dimensional settings.
method Applying LASSO to reweight and remove less relevant terms from EBMs.
result EBMs maintain transparency and fast scoring times with reduced complexity.
GLASS Flows improves flow and diffusion model performance by optimizing sampling efficiency.
problem Efficiency bottleneck in sampling Markov transitions for flow and diffusion models.
method Introduces GLASS Flows, a new sampling paradigm that simulates a 'flow matching model within a flow matching model' to sample Markov transitions efficiently.
result Eliminates the trade-off between stochastic evolution and efficiency in large-scale text-to-image models.
We analyze operational risk in terms of a spin glass model. Several regimes are investigated, as a functions of the parameters that characterize the dynamics. The system is found to be robust against variations of these parameters. We unveil the presence of limit cycles and scrutinize the features of the asymptotic sta…
Bayesian model improves BCI performance for ALS users.
problem Classifying EEG signals for P300 BCIs with low SNR and complex correlations.
method GLASS model with Gaussian Latent channel and Sparse time-varying effects.
result GLASS substantially improves BCI performance in ALS users.
New method generates equilibrium glass configurations efficiently.
problem Sampling equilibrium configurations of amorphous materials is slow and difficult.
method Riemannian stochastic interpolation framework combining Riemannian stochastic interpolant and equivariant flow matching.
result Enforcing geometric and symmetry constraints significantly improves generative performance.
We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of futu…
LLMs excel at summarizing and repairing complex models without needing full models.
problem Understanding and repairing complex models like GAMs.
method Hierarchical reasoning and extensive background knowledge.
result LLMs can detect anomalies, describe reasons, and suggest repairs.
Deep neural networks favor symmetric structures, enabling multilevel symmetries.
problem Understanding and optimizing deep neural networks.
method Formulating DNN training as convex Lasso problems with geometric algebra.
result Deep networks inherently favor symmetric structures, enabling multilevel symmetries.
Deep learning improves classification and characterization of amorphous materials.
problem Challenges in quantifying structure-property relationships and identifying structural features in amorphous materials.
method Application of convolutional neural networks and message passing neural networks to molecular dynamics simulations.
result Message passing neural networks outperform convolutional neural networks in classifying and characterizing amorphous materials.
Restricted Boltzmann Machines are described by the Gibbs measure of a bipartite spin glass, which in turn corresponds to the one of a generalised Hopfield network. This equivalence allows us to characterise the state of these systems in terms of retrieval capabilities, both at low and high load. We study the paramagnet…
RPNN-EOFs model improves time series forecasting accuracy.
problem Improving time series forecasting accuracy for complex systems.
method Combines higher-order neural networks with error-output feedbacks.
result RPNN-EOFs outperformed other models in forecasting the Mackey-Glass time series.
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
problem Understanding the principles behind emergent intelligent behaviors in disordered systems.
method Statistical physics approach to charting learning mechanisms and dynamics.
result Uncovering relationships between learning mechanisms and physical dynamics.
Statistical learning theory connects to spin glass models via Rademacher complexity and replica theory.
problem Bounding generalization gap in statistical learning theory.
method Linking Rademacher complexity in statistical learning to synthetic models in statistical physics.
result Rademacher complexity is closely related to ground state energy in spin glass models.
MPF method improves parameter estimation in probabilistic models.
problem Difficulty in fitting probabilistic models due to intractable partition function.
method Minimum Probability Flow (MPF) method for parameter estimation.
result MPF outperforms existing techniques in convergence time and accuracy.
Derives TAP approximation for Bayesian linear regression.
problem Log-normalizing constant of posterior distribution in high-dimensional linear regression.
method Variational representation and Thouless-Anderson-Palmer approximation.
result Proves TAP approximation for spherical prior in proportional asymptotic regime.
New framework to understand and exploit curvature in deep learning loss landscapes.
problem Understanding and optimizing the loss landscape in deep learning models.
method New conceptual framework and techniques to estimate and exploit curvature of expected loss changes.
result Alice algorithm optimizes training by incorporating curvature terms and step bounds.
Study higher-order spin glass models for social network behavior with peer-group effects.
problem Modeling correlation phenomena on social networks with peer-group effects.
method Inference in higher-order Ising models to recover coefficients and peer-group effects.
result Strong concavity of log pseudo-likelihood implies statistical error rate of sqrt(d/n) for MPLE.
EBM improves car insurance claim severity and frequency prediction while maintaining interpretability.
problem Balancing predictive accuracy and interpretability in insurance claim modeling.
method Combines GAM and cyclic gradient boosting, providing interpretable predictions.
result EBM outperforms benchmark models in claim severity and frequency prediction.
New statistical methods improve explainability of boosting models.
problem Uncertainty quantification for boosting models is computationally intensive and hard to interpret.
method Derive methods for statistical inference using gradient boosting and Boulevard regularization.
result Achieve asymptotically normal predictions with theoretical guarantees and runtime independent of data size.
Injectivity of ReLU networks studied using statistical physics.
problem When can the input of a ReLU neural network be inferred from its output?
method Connection to spherical integral geometry and statistical physics.
result Replica symmetry-breaking theory and Gordon's min--max theorem provide insights into the injectivity threshold.
Deep learning has become a powerful and popular tool for a variety of machine learning tasks. However, it is challenging to understand the mechanism of deep learning from a theoretical perspective. In this work, we propose a random active path model to study collective properties of deep neural networks with binary syn…
We study rough high-dimensional landscapes in which an increasingly stronger preference for a given configuration emerges. Such energy landscapes arise in glass physics and inference. In particular we focus on random Gaussian functions, and on the spiked-tensor model and generalizations. We thoroughly analyze the stati…
Researchers use statistical methods to infer transmission matrices in complex media.
problem Comprehending and exploiting photon scattering through disordered media.
method Pseudolikelihood decimation to learn the coupling matrix via random sampling.
result Transmission matrices can be inferred and used like normal optical elements.
Sharp asymptotics reveal how network width controls learnability in quadratic neural networks.
problem Understanding learnability in overparameterized quadratic neural networks.
method Mapping ERM to convex matrix sensing with nuclear norm penalization.
result Characterization of global minima and precise generalization thresholds.
Spatio-temporal RBF neural networks improve chaotic time series prediction.
problem Predicting chaotic time series due to their dynamic nature.
method Proposes an spatio-temporal extension of RBF neural networks.
result Spatio-temporal RBF outperforms standard RBF in chaotic time series prediction.
Study free energy in spherical spin glasses, proving universality dichotomy.
problem Analyzing free energy in spherical spin glass models with different tail exponents.
method Introduced a tail-adapted normalization and used universality dichotomy.
result Sharp universality dichotomy for free energy across different tail exponents.
Project aims to diagnose and track IPF disease using deep learning.
problem Diagnosing and tracking IPF disease in lung images.
method Developed a deep learning model for identifying honeycombing and ground glass patterns in HRCT lung images.
result Deep learning model achieved high accuracy in identifying specific lung regions.
Deep neural networks are optimizable due to their multilayered structure.
problem Understanding why deep neural networks are easily optimizable despite their non-convex loss functions.
method Analysis of a spin glass model of deep neural networks using random matrix theory and algebraic geometry.
result The multilayered structure of deep neural networks leads to fewer stationary points, more clustered minima, and less severe tradeoffs between depth and width of minima.
The study reveals the spectral structure of attention layers and its implications for generalization.
problem Understanding the spectral structure and generalization of trained attention layers.
method Empirical risk minimization in a single-head tied-attention layer, using random matrix theory, spin-glass theory, and approximate message passing.
result Exact high-dimensional characterization of training and test error, interpolation and recovery thresholds, and spectrum of the key and query matrices.
We show that every irreducible toroidal integer homology sphere graph manifold has a left-orderable fundamental group. This is established by way of a specialization of a result due to Bludov and Glass for the almagamated products that arise, and in this setting work of Boyer, Rolfsen and Wiest may be applied. Our resu…
We investigate the bi-orderability of two-bridge knot groups and the groups of knots with 12 or fewer crossings by applying recent theorems of Chiswell, Glass and Wilson. Amongst all knots with 12 or fewer crossings (of which there are 2977), previous theorems were only able to determine bi-orderability of 599 of the c…
New insights into optimal portfolios and ecological equilibria reveal surprising complexity.
problem Optimal portfolio construction with ecological constraints.
method Computational analysis of multispecies Lotka-Volterra equations with unit rank interaction matrices.
result Logarithm of the average number of solutions grows as \(N^{2/3}\), with most likely solutions being much smaller.
DIGIT is a low-cost tactile sensor for in-hand manipulation.
problem Difficulty in sensing contact forces limits robotic manipulation.
method DIGIT miniaturizes and improves a vision-based tactile sensor.
result DIGIT enables better control of interactions with the environment.
Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price…