On-device research index

arXiv research

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.

169,051 papers · 148 categories

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22 results for “UC Merced”

Land Use Classification Using Multi-neighborhood LBPscs.LG

Proposes multi-neighborhood LBPs for land use classification.

problem Challenges in classifying land use images due to intra class variability and inter class similarities.

method Uses multi-neighborhood LBPs combined with nearest neighbor classifier.

result Achieved an accuracy of 77.76% on UC Merced 21 class land use image dataset.

Land Use Classification Multi-neighborhood LBPs UC Merced Nearest Neighbor Classifier Accuracy

2019-02-07abs ↗pdf ↗

Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Gamescs.LG

This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of MIRL are considered: uCS-MIRL, advE-MIRL, cooE-MIRL, uCE-MIRL, and uNE-MIRL, each distinguished by its solution concept. Problem uCS-MIRL is a cooperative game in…

2018-06-26abs ↗pdf ↗

Discrete Morse theory and graph braid groupsmath.GR

If Gamma is any finite graph, then the unlabelled configuration space of n points on Gamma, denoted UC^n(Gamma), is the space of n-element subsets of Gamma. The braid group of Gamma on n strands is the fundamental group of UC^n(Gamma). We apply a discrete version of Morse theory to these UC^n(Gamma), for any n and any …

2004-10-25abs ↗pdf ↗

Square-section braid groups and Higman-Neumann-Neumann extensionsmath.GT

New groups derived from square configurations have right-angled and HNN structures.

problem Understanding the fundamental groups of square configurations and their homotopy properties.

method Analyzing configuration spaces and their fundamental groups, proving group presentations and homotopy equivalences.

result The fundamental groups of certain square configurations have minimal presentations with commutator relators and are HNN extensions of specific meta-square groups.

configuration spaces fundamental groups right-angled Artin groups HNN extensions Salvetti complexes

2025-10-20abs ↗pdf ↗

An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blendingcs.LG

Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised cluste…

2018-05-10abs ↗pdf ↗

Calibration of Model Uncertainty for Dropout Variational Inferencecs.LG

The paper calibrates uncertainty in dropout variational inference models.

problem Miscalibration of model uncertainty in dropout variational inference.

method Logit scaling methods are extended to recalibrate model uncertainty.

result Logit scaling reduces miscalibration, improving reliability of predictions.

dropout variational inference uncertainty calibration logit scaling UCE

2020-06-20abs ↗pdf ↗

No-Regret Exploration in Goal-Oriented Reinforcement Learningstat.ML

UC-SSP algorithm tackles exploration in stochastic shortest path problems without loop-free assumption.

problem Exploration in goal-oriented reinforcement learning problems under stochastic shortest path formulation.

method UC-SSP algorithm with a novel stopping rule to interrupt and switch policies.

result Regret bound of

\displaystyle \widetilde{\mathcal{O}}( D S \sqrt{ A D K})

after

K

episodes.

reinforcement learning stochastic shortest path exploration-exploitation UC-SSP no-regret

2019-12-07abs ↗pdf ↗

Lecture Notes on Spectral Graph Methodscs.DS

These are lecture notes that are based on the lectures from a class I taught on the topic of Spectral Graph Methods at UC Berkeley during the Spring 2015 semester.

2016-08-17abs ↗pdf ↗

Lecture Notes on Randomized Linear Algebracs.DS

These are lecture notes that are based on the lectures from a class I taught on the topic of Randomized Linear Algebra (RLA) at UC Berkeley during the Fall 2013 semester.

2016-08-16abs ↗pdf ↗

Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inferencecs.LG

This paper calibrates uncertainty in dropout variational inference models.

problem Uncertainty in variational inference with dropout is poorly calibrated.

method Temperature scaling is extended to dropout variational inference.

result Temperature scaling reduces miscalibration of uncertainty.

model uncertainty dropout variational inference temperature scaling uncertainty calibration monte carlo dropout

2019-09-30abs ↗pdf ↗

From factorizations of noncommutative polynomials to combinatorial topologymath.RA

This is an extended version of a talk given at the conference "Algebra and Topology in Interaction" on the occasion of the 70th Anniversary of D.B. Fuchs at UC Davis in September 2009. It is a brief survey of an area originated around 1995 by I. Gelfand and the author.

2009-11-23abs ↗pdf ↗

Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularizationcs.LG

The paper improves uncertainty quantification for node classification using distance-based regularization.

problem Uncertainty in deep learning models, especially for node classification tasks.

method Graph posterior networks (GPNs) with UCE loss function, followed by a distance-based regularization.

result The proposed distance-based regularization outperforms state-of-the-art methods in OOD detection and misclassification detection.

uncertainty quantification node classification graph posterior networks distance-based regularization OOD detection

2023-11-10abs ↗pdf ↗

Singularities of projected immersions revisitedmath.GT

Sz\H ucs proved in 2000 that the $r$ -tuple-point manifold of a generic immersion is cobordant to the $Σ^{1_{r-1}}$ -point manifold of its generic projection. Here we slightly extend this by showing that the natural mappings of these manifolds are bordant to each other. The main novelty of our approach is that we constru…

2008-03-30abs ↗pdf ↗

Accelerating spherical K-means clustering for large-scale sparse document datastat.ML

Paper accelerates K-means clustering for large sparse document data.

problem Efficiently clustering large-scale sparse document data.

method Designs an AFM algorithm leveraging UCs and inverted-index structure.

result Significantly improves clustering speed for large-scale documents.

K-means clustering sparse document data acceleration AFM inverted-index

2024-11-18abs ↗pdf ↗

Optimal Transport for Treatment Effect Estimationcs.LG

Proposes ESCFR to estimate treatment effects from biased data.

problem Treatment selection bias in observational data.

method Stochastic optimal transport with relaxed mass-preserving and proximal factual outcome regularizers.

result Significantly better performance in estimating treatment effects.

optimal transport treatment effect observational data bias mitigation stochastic optimal transport

2023-10-27abs ↗pdf ↗

Evolving Multi-label Classification Rules by Exploiting High-order Label Correlationcs.LG

This paper improves multi-label classification by leveraging high-order label correlations.

problem Improving accuracy in multi-label classification tasks using label correlations.

method Exploiting high-order label correlations through a supervised learning classifier system (UCS) and label powerset (LP) strategy.

result The proposed method outperforms other LP-based methods on multiple benchmark datasets.

multi-label classification label correlation supervised learning UCS label powerset

2020-07-22abs ↗pdf ↗

Donor-Aware scRNA-seq Benchmarks for IBD Classificationq-bio.QM

Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.

problem Influenza disease classification from scRNA-seq data is prone to donor-level confounding.

method Developed and evaluated three feature representations across two IBD cohorts.

result Compartment-stratified CLR composition and GatedStructuralCFN embeddings outperform linear models in classification accuracy.

scRNA-seq IBD Donor-aware Cross-validation Feature embeddings

2026-05-05abs ↗pdf ↗

Predicting Surgery Duration with Neural Heteroscedastic Regressionstat.ML

Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking. We investigate neural regression algorithms to estimate the parameters of surgery case durations, focusing on the issue of heteroscedasticity…

2017-02-17abs ↗pdf ↗

Fast communication-efficient spectral clustering over distributed datacs.LG

A new framework for spectral clustering over distributed data with minimal communication.

problem Efficiently computing spectral clustering over data distributed across multiple sites.

method Local parallel computing at data locations, enabling distributed data to be a benefit.

result Achieves almost no loss in accuracy with negligible communication overhead and substantial speedup.

spectral clustering distributed data communication efficiency privacy

2019-05-05abs ↗pdf ↗

Comparative Visual Analytics for Assessing Medical Records with Sequence Embeddingphysics.med-ph

Visual analytics system for comparing medical records using sequence embeddings.

problem Challenges in analyzing medical records due to high dimensionality, irregularity, and sparsity.

method Event and sequence embeddings using autoencoder and self-attention mechanism, with sequence alignment for comparison.

result Demonstrated effectiveness with real-world neonatal ICU dataset.

medical records sequence embeddings autoencoder self-attention neonatal ICU

2020-02-18abs ↗pdf ↗

Neural Graph Evolution: Towards Efficient Automatic Robot Designcs.LG

NGE uses neural graphs to efficiently design robots.

problem Designing robots is hard due to combinatorial search space and evaluation costs.

method Formulated as graph search, NGE uses neural networks for policy parameterization and graph mutation with uncertainty.

result NGE significantly outperforms previous methods, discovering kinematically preferred structures.

robotics graph search neural networks evolution search automatic design

2019-06-12abs ↗pdf ↗

No-Regret Online Reinforcement Learning with Adversarial Losses and Transitionscs.LG

New algorithms achieve no-regret learning even with adversarial transitions and losses.

problem No-regret learning impossible with adversarial transitions and losses.

method Developed algorithms for adversarial Markov Decision Processes with smooth regret increase.

result Achieved

\widetilde{O}(\sqrt{T} + C^{ extsf{P}})

regret, with

C^{ extsf{P}}

measuring adversarial transition function.

Reinforcement Learning Online Learning Adversarial Markov Decision Processes No-Regret Adversarial Losses

2023-05-27abs ↗pdf ↗