This is an extended abstract of the talk given at the Oberwolfach Workshop "Algebraic Structures in Low-Dimensional Topology", 25 May -- 31 May 2014. My goal was to describe progress in distributive homology from the previous Oberwolfach Workshop June 3 - June 9, 2012, in particular my work on Yang-Baxter homology; how…
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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.
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48 results for “Progressive Trees”
We propose to use boosted regression trees as a way to compute human-interpretable solutions to reinforcement learning problems. Boosting combines several regression trees to improve their accuracy without significantly reducing their inherent interpretability. Prior work has focused independently on reinforcement lear…
This paper optimizes high-dimensional oblique splits for decision trees, enhancing performance and computational efficiency.
problem Enhancing decision tree performance and computational efficiency in high-dimensional data.
method Established Sufficient Impurity Decrease (SID) convergence for -sparse oblique splits, proposing progressive trees for iterative refinement.
result Demonstrated that SID function class expands with -sparsity, enabling capture of complex data-generating processes.
Decision trees improve performance in various fields.
problem Improving performance in machine learning and data mining.
method Introduce tree-based models and training techniques.
result Highlight probabilistic characteristics and practical benefits.
Study uses machine learning and survival analysis to predict CKD progression.
problem Early detection and management of CKD to reduce ESRD risk.
method Combines machine learning and classical statistical models to identify novel CKD progression predictors.
result Deep learning models outperform other methods in predicting CKD progression.
Novel algorithm optimizes decision trees for nonlinear metrics.
problem Optimizing decision trees for nonlinear metrics like F1-score.
method Bi-objective optimisation approach to find optimal trees on Pareto frontier.
result The optimal tree for nonlinear metrics lies on the Pareto frontier.
Simultaneous Latent Budget Trees for stratified classification
problem Classification with stratification factors
method Probabilistic machine learning framework
result Interpretation of latent components and conditional split rule
Develops a Bayesian framework for symbolic regression of scientific expressions.
problem Lack of principled uncertainty quantification and interpretability in existing symbolic regression methods.
method Hierarchical Bayesian framework with tree-structured symbolic expressions and Markov chain Monte Carlo inference.
result Robust performance on various datasets, including single-atom catalysis.
The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second,…
Paper uses CMAB to improve NAS efficiency and accuracy.
problem Improving efficiency and accuracy of NAS for DNNs.
method Formulated NAS as CMAB, used Nested Monte-Carlo Search.
result Discovered cell structure achieves comparable accuracy to state-of-the-art, 20x faster.
Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results. Explainability is not only a gateway between AI and society but also a powerful tool t…
Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performance alone is not sufficient to satisfy the needs of practical deployment where interpretability is demanded for cases involving ethics and mis…
The paper proposes a method to infer differentiation trees from RNA velocity data.
problem Reconstructing dynamic cellular processes from sequencing data.
method Defining varifold distances between RNA velocity curves to approximate shortest-path distances in a tree.
result The varifold distance method approximates the shortest-path distance in a tree isomorphic to the target differentiation tree.
Tree-based models outperform deep learning on tabular data, especially for medium-sized datasets.
problem Understanding why tree-based models outperform deep learning on tabular data.
method Extensive benchmarks of tree-based and deep learning models on 45 datasets, accounting for hyperparameters.
result Tree-based models remain state-of-the-art on medium-sized tabular data, even without hyperparameter optimization.
Proposes a method to speed up model selection for classification.
problem Time-consuming model selection process and lack of dataset-specific insights.
method Relaxes decision boundaries of neural decision trees to find equivalent or better models.
result Reduces the scope of exploration needed for model selection.
Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarc…
This paper is a contribution to interweaving two lines of research that have progressed in separate ways: network analyses of international trade and the literature on African trade and development. Gathering empirical data on African countries has important limitations and so does the space occupied by African countri…
The paper proves a new method to find the maximum Laplace eigenvalues on surfaces.
problem Maximizing Laplace eigenvalues on surfaces of fixed volume.
method Developed a new proof using the approach by the second author and Y. Sire.
result The maximum of the -th Laplace eigenvalue is either attained on a metric with conical singularities or in the limit with a bubble tree.
Models predict Alzheimer's Dementia from spontaneous speech with high accuracy.
problem Early diagnosis of Alzheimer's Dementia (AD) through spontaneous speech analysis.
method Compared natural language processing techniques including SVM, GBDT, CRFs, and Transformer-based models.
result Top models achieve 0.81-0.82 test set scores for AD vs controls and 4.58 RMSE for Mental Mini State Exam scores.
Enhanced anomaly detection using PRC-RF with autoencoders.
problem Extreme class imbalance and curse of dimensionality in anomaly detection.
method Hybrid framework combining PRC-RF and autoencoders.
result Autoencoder-PRC-RF model outperforms previous methods in accuracy, scalability, and interpretability.
PyFi uses adversarial agents to train VLMs on financial image understanding.
problem Training VLMs to understand complex financial questions.
method PyFi-600K dataset and adversarial MCTS mechanism.
result Fine-tuned VLMs improve by 19.52% and 8.06% on financial question accuracy.
New method for summarizing ranking distributions using consensus ranking distributions.
problem Summarizing ranking distributions efficiently and accurately.
method Introducing consensus ranking distributions and a top-down tree-structured statistical algorithm.
result Optimal distortion can be expressed as a function of pairwise probabilities, enabling efficient learning methods.
Efficient search methods can outperform random search on challenging tasks.
problem Comparing the performance of efficient and random search methods in neural architecture search.
method Comparison of weight sharing and random search methods on progressively larger search spaces for image classification and detection.
result Efficient search methods can provide substantial gains over random search on large, realistic tasks.
Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Treescs.LG
Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models…
Survey of Bayesian nonparametric space partition models and their applications.
problem Partitioning high-dimensional spaces into homogeneous regions.
method Various strategies for generating partitions in a D-dimensional space.
result Review of current progress in BNSP research.
Machine learning models predict depression risk based on various factors.
problem Identifying individuals at greatest risk for depression.
method Random Effects/Expectation Maximization (RE-EM) trees and Mixed Effects Random Forest (MERF) algorithms.
result Machine learning models accurately predict depression severity and identify key predictors.
Paper presents a new training method for overparametrized neural networks that reduces time per iteration.
problem Scalability issue in training overparametrized neural networks.
method Uses a new view of neural networks as binary search trees, modifying a small subset of nodes per iteration.
result Reduces amortized time per iteration to from previous .
RelaySum improves decentralized deep learning by uniformly distributing data across workers.
problem Handling data heterogeneity in decentralized deep learning.
method RelaySum uses spanning trees to distribute information exactly uniformly across all workers with finite delays.
result RelaySum is independent of data heterogeneity and scales to many workers, enabling highly accurate decentralized deep learning.
The paper studies degenerations of rational maps and their limits as geometrically finite rational maps.
problem Understanding the limits of quasi post-critically finite degenerations of rational maps.
method Constructing limits as geometrically finite rational maps on a tree of Riemann spheres, proving boundedness, and giving convergence criteria.
result Progress towards Thurston's compactness theorem and double limit theorem in complex dynamics.
Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; w…
In this article, we investigate when the set of primitive geodesic lengths on a Riemannian manifold have arbitrarily long arithmetic progressions. We prove that in the space of negatively curved metrics, a metric having such arithmetic progressions is quite rare. We introduce almost arithmetic progressions, a coarsific…
This is a survey paper of our current research on the theory of partial differential equations in conformal geometry. Our intention is to describe some of our current works in a rather brief and expository fashion. We are not giving a comprehensive survey on the subject and references cited here are not intended to be …
Cluster Forestsstat.ME
With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments fo…
Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering…
Which factor dominates the industry evolution? A synergy analysis based on China's ICT industryphysics.soc-ph
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Paper introduces a new curriculum generation method for reinforcement learning.
problem Improving reinforcement learning performance and speed through curriculum learning.
method The paper proposes a novel curriculum generation paradigm based on progression and mapping functions.
result Empirical results show the new approach outperforms state-of-the-art algorithms.
Lie groups applied to tech progress in economic growth.
problem Understanding the impact of technical progress on economic growth.
method Application of Lie theory and economic modeling.
result Estimation of GDP function for Viet Nam, highlighting tech progress impact.
Boosting meta-trees improve decision tree performance.
problem Overfitting in decision trees.
method Boosting approach to construct multiple meta-trees.
result Ensembles of meta-trees prevent overfitting.
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conquer approach based upon an auxiliary tree-structured decomposition of the model of interest, turning the overall inferential task into a coll…
This paper improves disentanglement in VAEs by progressively learning hierarchical representations.
problem Compromised disentanglement in VAEs due to high-level abstraction extraction.
method Progressive learning of independent hierarchical representations from high to low levels.
result Improved disentanglement demonstrated on two benchmark datasets using new metrics.
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
The paper studies geometric properties of quasi-trees and tree approximations.
problem Geometric properties and tree approximations of quasi-trees.
method Construction of a tree approximating quasi-trees, proving quasi-isometric properties.
result Every quasi-tree is -quasi-isometric to a simplicial tree.
New algorithm outperforms existing ones by focusing on mastering rate.
problem Low sample efficiency in learning progress-based algorithms.
method Proposes a new algorithm based on mastering rate.
result Significantly outperforms learning progress-based algorithms.
Efficiently updates posterior tree distributions over meta-trees.
problem Updating posterior distributions over meta-trees efficiently.
method Batch updating method for posterior tree distributions.
result More efficient batch updating method.
The purpose of this paper relies on the study of long term yield curves modeling. Inspired by the economic litterature, it provides a financial interpretation of the Ramsey rule that links discount rate and marginal utility of aggregate optimal consumption. For such a long maturity modelization, the possibility of adju…
In this article, we prove that every arithmetic locally symmetric orbifold of classical type without Euclidean or compact factors has arbitrarily long arithmetic progressions in its primitive length spectrum. Moreover, we show the stronger property that every primitive length occurs in arbitrarily long arithmetic progr…
The concept of progress has characterized human society from millennia. However, this concept is elusive and too often given for certain. The goal of this paper is to suggest a general definition of human progress that satisfies, whenever possible the conditions of independence, generality, epistemological applicabilit…
This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach. Ideally, we would like to consider predictio…