Algorithm predicts graph label changes online with cluster specialists.
problem Online prediction of graph label changes with changing data.
method Specialist approach with cluster specialists, focusing on probabilistic cluster structure.
result Algorithm achieves O(logn) time complexity, significantly faster than existing methods. Several recent deep neural networks experiments leverage the generalist-specialist paradigm for classification. However, no formal study compared the performance of different clustering algorithms for class assignment. In this paper we perform such a study, suggest slight modifications to the clustering procedures, and…
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
Specialists outperform generalists in ensemble classification.
problem Determining the accuracy of an ensemble of classifiers when individual classifier accuracies are known.
method Proved upper and lower bounds on ensemble accuracy, constructed specialist and generalist classifiers.
result Upper and lower bounds on ensemble accuracy, practical implications for classifier construction.
New ensemble methods improve time series forecasting accuracy.
problem Global Forecasting Models (GFM) lack localisation for heterogeneous datasets.
method Ensemble techniques with clustering and varied GFM models.
result Significantly higher accuracy achieved compared to baseline models.
mvlearn simplifies multiview machine learning for non-specialists.
problem Handling multiview data sets from disparate sources.
method Python library implementing multiview machine learning methods.
result Enables non-specialists to use multiview machine learning methods easily.
Study identifies specialist representations from generalist models without parametric constraints.
problem Identify task-relevant latent representations from generalist models.
method Nonparametric, fully unsupervised approach, proving identifiability of task structure and latent representations.
result Identifiability of task structure and latent representations in a nonparametric setting.
Pymc-learn simplifies probabilistic machine learning for non-specialists.
problem Making probabilistic machine learning accessible to non-experts.
method Inspired by scikit-learn, Pymc-learn provides a high-level language for probabilistic models.
result Pymc-learn brings probabilistic machine learning to non-specialists with ease, performance, and flexibility.
MAP-Elites generates diverse trading strategies for improved execution performance.
problem Optimizing trading execution schedules in volatile market conditions.
method Quality-diversity algorithm (MAP-Elites) generating a portfolio of specialized strategies.
result Diverse strategies achieve 8-10% performance improvements, validating quality-diversity methods.
ContextFlow++ improves generative models by conditioning on mixed-variable contexts.
problem Lack of effective methods for context conditioning in flow-based generative models.
method Proposes ContextFlow++ with additive conditioning and mixed-variable architecture.
result ContextFlow++ achieves higher performance metrics and faster training.
A map φ:K→R2 of a graph K is approximable by embeddings, if for each ε>0 there is an ε-close to φ embedding f:K→R2. Analogous notions were studied in computer science under the names of cluster planarity and weak simplicity. This short survey is intended not only for …
Shared workspace improves neural module coordination in deep learning.
problem Pairwise interactions in deep learning models lack global coordination.
method Introduce a shared global workspace with bandwidth limitations among neural modules.
result Capacity limitations encourage specialization and synchronization.
Generalist neural learner can execute multiple algorithms.
problem Building models that can execute multiple algorithms.
method Single graph neural network processor, incorporating knowledge from specialist models.
result Generalist learner can execute multiple algorithms with improved performance.
Deep learning predicts diabetic macular edema from fundus photos.
problem Diabetic macular edema diagnosis from fundus photos is inaccurate.
method Trained deep learning model on color fundus photographs.
result Deep learning model has higher sensitivity and PPV than human specialists.
We present a model that investigates the spontaneous emergence of randomness in equity market microstructure. The phase space analysis of our model exposes an endogenous source of fluctuation in price and volume. We formulate a control problem for maximizing price regularity and stability while minimizing entanglement …
A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to…
Proposes a new framework for uncertainty-aware LLM post-training.
problem Heterogeneous, conflicting data in large language models.
method α-Rényi variational framework for learning distributions over post-training parameters.
result Enables training examples to be softly routed across ensemble members, promoting model specialisation and providing uncertainty estimates.
Confidence-based deferral works well in many scenarios but fails in specific cases.
problem Understanding when confidence-based deferral fails and when other strategies are better.
method Theoretical analysis and post-hoc deferral mechanisms were studied.
result Theoretical analysis characterizes settings where confidence-based deferral may fail.
This paper evaluates features for assessing digital ophthalmoscopy image quality.
problem Accurate teleophthalmology requires high-quality ophthalmoscopic imagery.
method Statistical metrics, gradient-based metrics, and wavelet transform coefficient derived indicators were tested using machine learning.
result Suitability of features for image quality assessment confirmed, though on a small data set.
Constructs quantum representations of mapping class groups using skein theory.
problem Quantum representations of mapping class groups.
method Skein theory (Kauffman Bracket) without surgery techniques.
result Provides an almost self-contained construction with properties like Hermitian structure and irreducibility.
VQShape learns interpretable time-series representations and achieves comparable performance to specialist models.
problem Lack of interpretability in existing time-series models.
method Vector quantization of time-series data into abstracted shapes.
result VQShape achieves comparable performance to specialist models in classification tasks.
MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.
problem Lack of principled credit assignment in multi-agent LLM decision systems, vulnerability to regime shifts, and limited transparency.
method Market Regime Council (MRC) computes exact Shapley credits, uses exponentially weighted performance histories, Bayesian adaptive mixture, and regime-dependent multipliers.
result MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1% over 1,037 trading days, ranking first on CR, SR, and IR.
Recently, classical results on completeness of trajectories of Hamiltonian systems obtained at the beginning of the seventies, have been revisited, improved and applied to Lorentzian Geometry. Our aim here is threefold: to give explicit proofs of some technicalities in the background of the specialists, to show that th…
Foundation models outperform supervised methods in time series forecasting across various operational regimes.
problem Lack of domain-specific training and ongoing maintenance in supervised learning for time series forecasting.
method Evaluation of foundation models against standard supervised approaches across four operational regimes: periodic, physically constrained, stochastic, and demand forecasting.
result Foundation models are optimal for cold-start or long-tail scenarios and perform well in domains with transferable periodic structures.
Interactive art piece shows braid groups and plane motions.
problem Understanding braid groups and their actions.
method Interactive art piece illustrating two braid group definitions and their action on free group.
result Demonstrates all plane motions in a single subspace.
Defines a new integration method for 1D currents, proving a generalized FTC.
problem Developing a new integration method for 1D integral currents.
method Integrates 1D integral currents using a Henstock-Kurzweil type approach.
result Proves a generalized Fundamental Theorem of Calculus for these currents.
Bayesian network structures are usually built using only the data and starting from an empty network or from a naive Bayes structure. Very often, in some domains, like medicine, a prior structure knowledge is already known. This structure can be automatically or manually refined in search for better performance models.…
Develops a framework for multi-objective learning in diffusion models with limited labeled data.
problem Achieving good trade-offs in multi-objective learning with diffusion models requires a generalist model class with larger capacity than individual tasks.
method Proposes a two-stage training procedure: first fitting specialist models from limited paired data, then distilling them into a generalist model.
result Establishes generalization bounds showing the number of paired samples depends only on specialist model complexity.
Survey simplifies embedding theorems for manifolds.
problem Embedding manifolds into Euclidean space.
method Combining complement and neighborhood ideas to reduce embeddability to algebraic problems.
result Clarified exposition of Browder-Levine theorem on realization of normal systems.
Model for detecting rare labels in imbalanced crowdsourcing data.
problem Detecting rare labels in imbalanced crowdsourcing data.
method Generative aggregation model combining item difficulty and class-dependent annotator competence.
result Our model achieves the highest minority recall while maintaining competitive balanced accuracy.
In this expository paper we present short simple proofs of Conway-Gordon-Sachs' theorem on intrinsic linking in three-dimensional space, as well as van Kampen-Flores' and Ummel's theorems on intrinsic intersections. The latter are related to nonrealizability of certain hypergraphs in four-dimensional space. The proofs …
Hilbert(ian) A-modules over finite von Neumann algebras A with a faithful normal trace state (from global analysis) and Hilbert W*-modules over A (from operator algebra theory) are compared, and a categorical equivalence is established. The correspondence between these two structures sheds new light on basic results in…
A binary classifier capable of abstaining from making a label prediction has two goals in tension: minimizing errors, and avoiding abstaining unnecessarily often. In this work, we exactly characterize the best achievable tradeoff between these two goals in a general semi-supervised setting, given an ensemble of predict…
This is an expository article with complete proofs intended for a general non-specialist audience. The results are two-fold. First, we discuss a geometric invariant, that we call the width, of a manifold and show how it can be realized as the sum of areas of minimal 2-spheres. For instance, when M is a homotopy 3-sph…
Specialists tolerate defects to gain flexibility, which can be removed when needed.
problem The economic benefits and limitations of deliberately tolerating defects in decision-making.
method Analyzes the conditions under which defects can be kept and removed, using economic models and structural analysis.
result A defect is profitably removable if certain conditions are met, and the premium is the support function of the class's ROC set.
Specialists tolerate defects to gain flexibility, which can be removed when needed.
problem The economic benefits and limits of deliberately tolerating defects in decision-making.
method Analyzes the economic position of keeping and removing defects, using a coupling lemma and structural economic models.
result A defect is profitably removable if the detector-relevant distinction survives a restriction and the advantage condition holds.
CSA fills a gap in RLVR-trained LLM deployment by providing anytime-valid selective risk control.
problem Deployment of RLVR-trained LLMs in regulated organizations requires a safety certificate for every round without waiting for long-run averages.
method CSA uses a (test statistic, validity guarantee, deployment rule) framework to fill the gap, maintaining a Ville-type e-process per threshold on a Bonferroni grid.
result CSA provides the first anytime-valid selective risk control for RLVR-trained LLMs, matching the long-run average certification rate and satisfying pathwise validity and non-refusing deployment on every cell.
Python library for causal discovery from observational data.
problem Revealing causal relations from observational data.
method Comprehensive collection of causal discovery methods in Python.
result Ease of use for non-specialists and modular building blocks for developers.
Predict genetic inheritance patterns using hypergraphs and latent models.
problem Diagnosing inherited diseases requires identifying family genetic patterns.
method Represent family trees as hypergraphs, use latent state space models for causal inference.
result Allows for explainable predictions of patient genotypes based on relatives' phenotypes.
This paper introduces a deep-learning based efficient classifier for common dermatological conditions, aimed at people without easy access to skin specialists. We report approximately 80% accuracy, in a situation where primary care doctors have attained 57% success rate, according to recent literature. The rationale of…
The topological Tverberg conjecture was considered a central unsolved problem of topological combinatorics. The conjecture asserts that for any integers r,d>1 and any continuous map f:Δ→Rd of the (d+1)(r−1)-dimensional simplex there are pairwise disjoint faces σ1,…,σr⊂Δ such that $f(σ_1)…
Explains basic knot and link theory for students.
problem Understanding simple invariants of knots and links.
method Defining and explaining linking number, Arf-Casson invariants, Alexander-Conway polynomials, and skein relations.
result Introduction of stronger invariants and the Vassiliev-Kontsevich theorem.
New fair-by-design model reduces bias in recidivism prediction.
problem Discriminatory bias in recidivism prediction models.
method Prototype-based, locally learned, data distribution extraction.
result Reduces bias and provides interpretable rules.
A simplified proof for embedding higher-dimensional complexes into manifolds.
problem Embedding higher-dimensional complexes into manifolds with constraints.
method A short and accessible proof for the Patak-Tancer theorem.
result A simplified proof for the Heawood inequality in higher dimensions.
We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of He…
This is an invited article for the Discussion and Debate special issue of The European Physical Journal Special Topics on the subject "Can Economics Be a Physical Science?" The first part of the paper traces the personal path of the author from theoretical physics to economics. It briefly summarizes applications of sta…
iNNvestigate simplifies neural network analysis.
problem Understanding and analyzing complex neural networks is difficult.
method Provides a common interface and implementation for analysis methods.
result Demonstrates versatility in analyzing various neural network architectures.
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each…