yusuke1997/dblp-id-title huggingface dataset:
Streaming kernel regression with provably adaptive mean, variance, and regularization
Profile-Based Bandit with Unknown Profiles
Flexible Model Aggregation for Quantile Regression
Learning and Testing the Bounded Retransmission Protocol
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes
NetSDM: Semantic Data Mining with Network Analysis
Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning
Inference via Low-Dimensional Couplings
On the Consistency of Multi-Label Learning
Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot be Ignored
Design and Analysis of the NIPS 2016 Review Process
Fast State Discovery for HMM Model Selection and Learning
Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness
The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models
A Library for Locally Weighted Projection Regression
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Consistency of Trace Norm Minimization
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
Improving physics-informed neural networks with meta-learned optimization
Efficient Algorithms for Conditional Independence Inference
Branch and Bound for Piecewise Linear Neural Network Verification
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
Graph Matching with Partially-Correct Seeds
Autoregressive Networks
Scaling Up Models and Data with t5x and seqio
Regularization Techniques for Learning with Matrices
A Family of Additive Online Algorithms for Category Ranking
Exact Clustering of Weighted Graphs via Semidefinite Programming
Differentially Private Data Release for Mixed-type Data via Latent Factor Models
PREA: personalized recommendation algorithms toolkit
A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods
Dlib-ml: A Machine Learning Toolkit
Active Learning for Unbalanced Data in the Challenge with Multiple Models and Biasing
Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks
A Fast Hybrid Algorithm for Large-Scale l 1 -Regularized Logistic Regression
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning
Efficient Sampling from Combinatorial Space via Bridging
Preface to this special issue
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Mining Topological Structure in Graphs through Forest Representations
Tapkee: an efficient dimension reduction library
Multiple Texture Boltzmann Machines
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
To Tune or Not to Tune the Number of Trees in Random Forest
Asymptotic behavior of Support Vector Machine for spiked population model
Monotone Learning with Rectified Wire Networks
Active Learning by Spherical Subdivision
Sufficient Dimensionality Reduction
A New Algorithm and Theory for Penalized Regression-based Clustering
Generalization and Stability of Interpolating Neural Networks with Minimal Width
Deep Learning using Robust Interdependent Codes
A Greedy Algorithm for Quantizing Neural Networks
Causal Bounds and Observable Constraints for Non-deterministic Models
Adversarial Robustness Guarantees for Gaussian Processes
Learning A* underestimates: Using inference to guide inference
Semi-supervised learning using greedy max-cut
Stagewise Lasso
Fundamental Conditions for Low-CP-Rank Tensor Completion
Combining PAC-Bayesian and Generic Chaining Bounds
Factorized Diffusion Map Approximation
Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design
Inner Product Spaces for Bayesian Networks
Primal-Dual methods for sparse constrained matrix completion
On the Complexity of Approximating Multimarginal Optimal Transport
On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem
ptwt - The PyTorch Wavelet Toolbox
Differentiable reservoir computing
Robust Process Discovery with Artificial Negative Events
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon
Training highly multiclass classifiers
Kernel Topic Models
Erratum: SGDQN is Less Careful than Expected
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
Assessment of Cow's Body Condition Score Through Statistical Shape Analysis and Regression Machines
Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier
Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
The Coding Divergence for Measuring the Complexity of Separating Two Sets
Hierarchical Beta Processes and the Indian Buffet Process
Clustering partially observed graphs via convex optimization
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Memory-Effcient Orthogonal Least Squares Kernel Density Estimation using Enhanced Empirical Cumulative Distribution Functions
Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming
Using symmetry and evolutionary search to minimize sorting networks
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
Approximation Algorithms for Stochastic Clustering
Nonlinear Dimensionality Reduction as Information Retrieval
Sum-of-norms clustering does not separate nearby balls
Faithfulness of Probability Distributions and Graphs
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning
Fast search for Dirichlet process mixture models
Minimax Nonparametric Parallelism Test
Distributed Kernel Ridge Regression with Communications
A Cluster Elastic Net for Multivariate Regression
Trust-Region Variational Inference with Gaussian Mixture Models
An efficient distributed learning algorithm based on effective local functional approximations
tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer-Monteiro Factorization with Global Optimality Certification
Estimating the Lasso's Effective Noise
Multilevel CNNs for Parametric PDEs
Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach
Lower Bounds and Accelerated Algorithms for Bilevel Optimization
On the Consistency of Feature Selection using Greedy Least Squares Regression
Encog: library of interchangeable machine learning models for Java and C#
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A Rotation Test to Verify Latent Structure
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
A Characterization of Scoring Rules for Linear Properties
Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior
Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
Spectral Ranking using Seriation
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes
Dynamic Pricing in High-dimensions
A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping
Bounding the Probability of Error for High Precision Optical Character Recognition
Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs
Node-based learning of multiple Gaussian graphical models
The Rate of Convergence of Adaboost
On Efficient Large Margin Semisupervised Learning: Method and Theory
Multitask Sparsity via Maximum Entropy Discrimination
Multiresolution Mixture Modeling using Merging of Mixture Components
Multiplicative local linear hazard estimation and best one-sided cross-validation
A Linear Ensemble of Individual and Blended Models for Music Rating Prediction
Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis
Inference on the Change Point under a High Dimensional Covariance Shift
Accelerating AdaBoost using UCB
ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm
The Two-Sided Game of Googol
Approximate Model Selection for Large Scale LSSVM
Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch
Sharp analysis of power iteration for tensor PCA
Exact and Approximate Sampling by Systematic Stochastic Search
Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models
CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
Randomized Optimum Models for Structured Prediction
JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data
Coupled Clustering: A Method for Detecting Structural Correspondence
Model Selection via the VC Dimension
Geometry and expressive power of conditional restricted Boltzmann machines
Feature Engineering in User's Music Preference Prediction
GURLS: a least squares library for supervised learning
Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection
Regret Analysis of Bilateral Trade with a Smoothed Adversary
Learning by Unsupervised Nonlinear Diffusion
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI
Approximate inference using conditional entropy decompositions
Continuous RAVE
Multi-task regression using minimal penalties
Lifted Linear Programming
Structure-Leveraged Methods in Breast Cancer Risk Prediction
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence
Structure and Majority Classes in Decision Tree Learning
Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview
Static prediction games for adversarial learning problems
Label Alignment Regularization for Distribution Shift
Fast b-matching via Sufficient Selection Belief Propagation
Nonparametric Bayesian Matrix Factorization by Power-EP
Contextual Multi-Armed Bandits
Visualization of Online Discussion Forums
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters
A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation
Distances between Data Sets Based on Summary Statistics
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains
Causal-learn: Causal Discovery in Python
Generalized Score Matching for Non-Negative Data
Deep Boltzmann Machines
MOA: Massive Online Analysis
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
Refined Error Bounds for Several Learning Algorithms
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
A general linear-time inference method for Gaussian Processes on one dimension
Semi-Supervised Learning via Generalized Maximum Entropy
Structure Spaces
The SHOGUN Machine Learning Toolbox
Reproducing Kernels and New Approaches in Compositional Data Analysis
Distributed Learning of Finite Gaussian Mixtures
Python package for causal discovery based on LiNGAM
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling
Graphical models via univariate exponential family distributions
l p -Norm Multiple Kernel Learning
Matrix Product States for Inference in Discrete Probabilistic Models
The Statistical Performance of Collaborative Inference
Rearrangement Clustering: Pitfalls, Remedies, and Applications
Pyro: Deep Universal Probabilistic Programming
Data Thinning for Convolution-Closed Distributions
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials
Cascaded Diffusion Models for High Fidelity Image Generation
Kernel Methods for Measuring Independence
Bayesian Inference and Optimal Design for the Sparse Linear Model
Generative Modeling for Maximizing Precision and Recall in Information Visualization
Active Learning Using Smooth Relative Regret Approximations with Applications
Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection
Joint estimation of multiple precision matrices with common structures
metric-learn: Metric Learning Algorithms in Python
Efficient Algorithms for Decision Tree Cross-validation
From dependency to causality: a machine learning approach
Convex Regression with Interpretable Sharp Partitions
TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents
Expectation Truncation and the Benefits of Preselection In Training Generative Models
CAKE: Convex Adaptive Kernel Density Estimation
A Simple Geometric Interpretation of SVM using Stochastic Adversaries
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
GPstuff: Bayesian modeling with Gaussian processes
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods
Nonlinear functional regression: a functional RKHS approach
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems
A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences
Scalable Learning of Bayesian Network Classifiers
Efficient Inference for Dynamic Flexible Interactions of Neural Populations
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
Learning bilinear model for matching queries and documents
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
On the bayes-optimality of F-measure maximizers
Non-Convex Matrix Completion and Related Problems via Strong Duality
Contextual Bandits with Similarity Information
Bayesian Model Selection with Graph Structured Sparsity
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Optimal Estimation and Completion of Matrices with Biclustering Structures
Optimal Bayesian estimation in random covariate design with a rescaled Gaussian process prior
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization
Dynamical Systems as Temporal Feature Spaces
Gaussian Lower Bound for the Information Bottleneck Limit
Supervised Dimension Reduction Using Bayesian Mixture Modeling
A Case Study on Meta-Generalising: A Gaussian Processes Approach
Speeding Up Syntactic Learning Using Contextual Information
Universal Approximation of Functions on Sets
Learning over Sets using Kernel Principal Angles
Simple Variable Length N-grams for Probabilistic Automata Learning
Integrating Naïve Bayes and FOIL
Spatial Multivariate Trees for Big Data Bayesian Regression
spark-crowd: A Spark Package for Learning from Crowdsourced Big Data
Unlinked Monotone Regression
Local Discriminant Wavelet Packet Coordinates for Face Recognition
Online Learning: Beyond Regret
Nonparametric guidance of autoencoder representations using label information
The algebraic combinatorial approach for low-rank matrix completion
A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
A Bahadur Representation of the Linear Support Vector Machine
Optimal Dictionary for Least Squares Representation
Second-Order Bilinear Discriminant Analysis
Tangential Wasserstein Projections
Linear Bandits on Uniformly Convex Sets
Decontamination of Mutual Contamination Models
Reconstructing Undirected Graphs from Eigenspaces
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
FLIP: A Utility Preserving Privacy Mechanism for Time Series
An MDP-Based Recommender System
Reduced-Rank Hidden Markov Models
Spectral learning of latent-variable PCFGs: algorithms and sample complexity
Learning Exercise Policies for American Options
Handling Missing Values when Applying Classification Models
Open Problem: Better Bounds for Online Logistic Regression
Maximum Entropy Density Estimation with Incomplete Presence-Only Data
Nonparametric Continuous Sensor Registration
Concave Learners for Rankboost
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension
Feature Discovery in Non-Metric Pairwise Data
Commentary on "Online Optimization with Gradual Variations"
Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression
Confidence intervals and hypothesis testing for high-dimensional regression
Uniform deconvolution for Poisson Point Processes
Topological Convolutional Layers for Deep Learning
Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
Stick-Breaking Beta Processes and the Poisson Process
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Fast Approximate k NN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
Stochastic Proximal AUC Maximization
Buffered Asynchronous SGD for Byzantine Learning
Bayesian Gaussian Process Latent Variable Model
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables
Soft Tensor Regression
Learning theory approach to minimum error entropy criterion
Beta-Negative Binomial Process and Poisson Factor Analysis
Modeling Random Networks with Heterogeneous Reciprocity
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
Inference of k-Testable Directed Acyclic Graph Languages
Learning to identify concise regular expressions that describe email campaigns
Manifold Coordinates with Physical Meaning
Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes
Information Rates of Nonparametric Gaussian Process Methods
Structured Sparse Canonical Correlation Analysis
Unbiased estimators for random design regression
Relational Learning as Search in a Critical Region
Bayesian Quadratic Discriminant Analysis
Optimal Rates for Multi-pass Stochastic Gradient Methods
Learning low-rank output kernels
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
Deep Optimal Stopping
Parallelizable Sampling of Markov Random Fields
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Winning The Transfer Learning Track of Yahoo!'s Learning To Rank Challenge with YetiRank
Hybrid Recommendation Models for Binary User Preference Prediction Problem
Probabilistic Models for Incomplete Multi-dimensional Arrays
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maximum Entropy Discrimination Markov Networks
Decrypting "Cryptogenic" Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
DART: Distance Assisted Recursive Testing
Optimality and Stability in Non-Convex Smooth Games
SGD with Coordinate Sampling: Theory and Practice
Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning
On the Suitable Domain for SVM Training in Image Coding
A Computationally Efficient Framework for Vector Representation of Persistence Diagrams
Learning linear cyclic causal models with latent variables
The gesture recognition toolkit
Semi-Supervised Learning with Measure Propagation
Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression
Agnostic KWIK learning and efficient approximate reinforcement learning
Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices
Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies
Distributed Coordinate Descent Method for Learning with Big Data
L1 Covering Numbers for Uniformly Bounded Convex Functions
Characterization of translation invariant MMD on Rd and connections with Wasserstein distances
Bayesian Distance Clustering
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
Support Vector Machine Soft Margin Classifiers: Error Analysis
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts
Characteristic Kernels and Infinitely Divisible Distributions
A Comparison of the Lasso and Marginal Regression
A Unified Analysis of Multi-task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty
Sparse Markov Models for High-dimensional Inference
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers
Personalized PCA: Decoupling Shared and Unique Features
Bouligand Derivatives and Robustness of Support Vector Machines for Regression