yusuke1997/dblp-id-title huggingface dataset:
Bandits with Generalized Linear Models
Efficient program synthesis using constraint satisfaction in inductive logic programming
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
Existence and uniqueness of proper scoring rules
Neyman-Pearson classification under a strict constraint
Max-Margin Min-Entropy Models
The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems
Value Regularization and Fenchel Duality
Contextual Bandit Algorithms with Supervised Learning Guarantees
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
Multi-objective Monte-Carlo Tree Search
A Unified Energy-Based Framework for Unsupervised Learning
Adaptive Step-size Policy Gradients with Average Reward Metric
Tree Block Coordinate Descent for MAP in Graphical Models
Optimal Policies for Observing Time Series and Related Restless Bandit Problems
Tests of Mutual or Serial Independence of Random Vectors with Applications
Tighter Risk Certificates for Neural Networks
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent
Operator learning with PCA-Net: upper and lower complexity bounds
Learning Overcomplete, Low Coherence Dictionaries with Linear Inference
Topographic Analysis of Correlated Components
Change Point Estimation in a Dynamic Stochastic Block Model
Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain
The On-Line Shortest Path Problem Under Partial Monitoring
QWA: Spectral Algorithm
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
Lifted Variable Elimination with Arbitrary Constraints
Entropic Fictitious Play for Mean Field Optimization Problem
Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach
The weight function in the subtree kernel is decisive
Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling
Supervised Spectral Latent Variable Models
Active learning using smooth relative regret approximations with applications
Lyapunov Design for Safe Reinforcement Learning
Rational Kernels: Theory and Algorithms
ktrain: A Low-Code Library for Augmented Machine Learning
Causal Learning via Manifold Regularization
A Robust-Equitable Measure for Feature Ranking and Selection
Sample Complexity Bounds for Differentially Private Learning
There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems
Determining the Number of Communities in Degree-corrected Stochastic Block Models
ELFI: Engine for Likelihood-Free Inference
Locally Linear Denoising on Image Manifolds
Parallel vector field embedding
Adaptive minimax regression estimation over sparse lq-hulls
Importance Sampling Techniques for Policy Optimization
Materials Discovery using Max K-Armed Bandit
Nonparametric Network Models for Link Prediction
A Complete Characterization of Linear Estimators for Offline Policy Evaluation
Optimal Solutions for Sparse Principal Component Analysis
CEKA: a tool for mining the wisdom of crowds
Minimax Rates in Permutation Estimation for Feature Matching
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion
PAC Guarantees and Effective Algorithms for Detecting Novel Categories
Feature-Level Domain Adaptation
Deterministic Annealing for Semi-Supervised Structured Output Learning
Calibrated multivariate regression with application to neural semantic basis discovery
JKernelMachines: a simple framework for kernel machine
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation
Clustering hidden Markov models with variational HEM
Streamwise Feature Selection
Sub-local constraint-based learning of Bayesian networks using a joint dependence criterion
Hard-Constrained Deep Learning for Climate Downscaling
Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization
Optimality of graphlet screening in high dimensional variable selection
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data
Rare Probability Estimation under Regularly Varying Heavy Tails
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions
Margin Trees for High-dimensional Classification
Matrix-Variate Dirichlet Process Mixture Models
Best Arm Identification for Contaminated Bandits
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks
A General System of Differential Equations to Model First-Order Adaptive Algorithms
Statistical Inference for Noisy Incomplete Binary Matrix
PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering
Analysis of Multi-stage Convex Relaxation for Sparse Regularization
STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
On utility of gene set signatures in gene expression-based cancer class prediction
Decentralized Learning: Theoretical Optimality and Practical Improvements
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Dimensionality Reduction and Wasserstein Stability for Kernel Regression
Intrinsic Persistent Homology via Density-based Metric Learning
Variable selection in high-dimension with random designs and orthogonal matching pursuit
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity
Asymptotics of Network Embeddings Learned via Subsampling
Worst-Case Analysis of Selective Sampling for Linear Classification
Choosing a Variable to Clamp
On Learning Rates and Schrödinger Operators
The Impact of Random Models on Clustering Similarity
Approximations of the Restless Bandit Problem
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Variational Inference for the Indian Buffet Process
Gaussian Processes for Machine Learning (GPML) Toolbox
On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size
Dependent relevance determination for smooth and structured sparse regression
Sparsity regret bounds for individual sequences in online linear regression
Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models
Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
Sparse Estimation in Ising Model via Penalized Monte Carlo Methods
A survey of Algorithms and Analysis for Adaptive Online Learning
A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra
Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality
V-statistics and Variance Estimation
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
Online Learning for Matrix Factorization and Sparse Coding
Online PCA with Optimal Regret
Unbiased generative semi-supervised learning
Ranked bandits in metric spaces: learning diverse rankings over large document collections
Low-rank Variational Bayes correction to the Laplace method
A Lyapunov Analysis of Accelerated Methods in Optimization
Image Categorization by Learning and Reasoning with Regions
mvlearn: Multiview Machine Learning in Python
The Common-directions Method for Regularized Empirical Risk Minimization
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
Sparsistency of the Edge Lasso over Graphs
Information Processing Equalities and the Information-Risk Bridge
Transfer learning decision forests for gesture recognition
The Sample Complexity of Dictionary Learning
Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Learning Bayesian Networks from Ordinal Data
Decision Tree for Dynamic and Uncertain Data Streams
Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization
A Maximum Likelihood Approach to Single-channel Source Separation
Residual Energy-Based Models for Text
Improved Loss Bounds For Multiple Kernel Learning
Neural Architecture Search: A Survey
Augmented Sparsifiers for Generalized Hypergraph Cuts
Resource-Efficient Neural Networks for Embedded Systems
Detecting Latent Communities in Network Formation Models
A General Framework for Adversarial Label Learning
Contraction rates for sparse variational approximations in Gaussian process regression
pomegranate: Fast and Flexible Probabilistic Modeling in Python
Flexible high-dimensional classification machines and their asymptotic properties
Convergence Rates of Efficient Global Optimization Algorithms
Model-based Causal Discovery for Zero-Inflated Count Data
Estimation of Graphical Models through Structured Norm Minimization
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
A Framework and Benchmark for Deep Batch Active Learning for Regression
An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity
Inverse Optimal Heuristic Control for Imitation Learning
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Breaking the Curse of Nonregularity with Subagging - Inference of the Mean Outcome under Optimal Treatment Regimes
Using Machine Learning to Guide Architecture Simulation
Multiscale Strategies for Computing Optimal Transport
Regularized K-means Through Hard-Thresholding
SpeedBoost: Anytime Prediction with Uniform Near-Optimality
Message-passing algorithms for quadratic minimization
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
Smoothing multivariate performance measures
On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity
Sampling Methods for the Nyström Method
Boosting as a Regularized Path to a Maximum Margin Classifier
Learning to Rank under Multinomial Logit Choice
Lightning-speed Structure Learning of Nonlinear Continuous Networks
Bayesian group factor analysis with structured sparsity
Topology of Deep Neural Networks
The Weighted Generalised Covariance Measure
Differentially Private Regression and Classification with Sparse Gaussian Processes
Scale Invariant Power Iteration
Latent Space Inference of Internet-Scale Networks
Finding Groups of Cross-Correlated Features in Bi-View Data
The Minimum Error Minimax Probability Machine
Graphlet decomposition of a weighted network
VC Theory of Large Margin Multi-Category Classifiers
An Information-Theoretic Analysis of Thompson Sampling
GluonTS: Probabilistic and Neural Time Series Modeling in Python
On the convergence rate of lp-norm multiple kernel learning
On Truthing Issues in Supervised Classification
Multi-scale Classification using Localized Spatial Depth
Statistical Inference on Random Dot Product Graphs: a Survey
Absent data generating classifier for imbalanced class sizes
Spherical Rotation Dimension Reduction with Geometric Loss Functions
Fisher Consistency for Prior Probability Shift
Benchmarking Graph Neural Networks
Training energy-based models for time-series imputation
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks
Bagging in overparameterized learning: Risk characterization and risk monotonization
Bayesian nonparametric crowdsourcing
Dual Control for Approximate Bayesian Reinforcement Learning
Multi-class Gaussian Process Classification with Noisy Inputs
Evolutionary Model Type Selection for Global Surrogate Modeling
Detecting Weak but Hierarchically-Structured Patterns in Networks
Missing Information Impediments to Learnability
Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure
Are Random Forests Truly the Best Classifiers?
State-by-state Minimax Adaptive Estimation for Nonparametric Hidden Markov Models
An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models
On Consistent Vertex Nomination Schemes
Convergence Theorems for Generalized Alternating Minimization Procedures
Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients
Sparse single-index model
Partial Policy Iteration for L1-Robust Markov Decision Processes
Rank Determination for Low-Rank Data Completion
Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems
A Unifying View of Sparse Approximate Gaussian Process Regression
Importance Sampling for Continuous Time Bayesian Networks
Exceptional rotations of random graphs: a VC theory
Additive smoothing error in backward variational inference for general state-space models
Integrated Common Sense Learning and Planning in POMDPs
Hitting and commute times in large random neighborhood graphs
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
Multi-object tracking with representations of the symmetric group
Sparse matrix inversion with scaled Lasso
Gap Minimization for Knowledge Sharing and Transfer
Learning Rules from Incomplete Examples via Implicit Mention Models
Kernel Multi-task Learning using Task-specific Features
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
On multilabel classification and ranking with bandit feedback
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
Hierarchical Relative Entropy Policy Search
Consensus-Based Distributed Support Vector Machines
Completing any low-rank matrix, provably
A Unified Algorithmic Approach for Efficient Online Label Ranking
Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming
On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems
Attention is Turing-Complete
Learning a Parametric Embedding by Preserving Local Structure
Mixability is Bayes Risk Curvature Relative to Log Loss
Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation
Classification vs regression in overparameterized regimes: Does the loss function matter?
Infinitely Imbalanced Logistic Regression
Separating a Real-Life Nonlinear Image Mixture
New Perspectives on k-Support and Cluster Norms
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Variable Selection Using SVM-based Criteria
An Efficient Implementation of an Active Set Method for SVMs
Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models
Refinement of Reproducing Kernels
A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
Transferring Knowledge by Prior Feature Sampling
Random Feature Maps for Dot Product Kernels
solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score
Probabilistic Low-Rank Matrix Completion from Quantized Measurements
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling
Stability of Randomized Learning Algorithms
Sparse Continuous Distributions and Fenchel-Young Losses
Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations
Privacy-Aware Rejection Sampling
Maximum Volume Clustering
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
Near-Optimal Evasion of Convex-Inducing Classifiers
Exploiting Probabilistic Independence for Permutations
Beware of the DAG!
Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries
ProtoryNet - Interpretable Text Classification Via Prototype Trajectories
Projected Robust PCA with Application to Smooth Image Recovery
Simple Classification Using Binary Data
High Dimensional Forecasting via Interpretable Vector Autoregression
Feature Selection: An Ever Evolving Frontier in Data Mining
Locally Minimax Optimal Predictive Modeling with Bayesian Networks
Fast MCMC sampling for Markov jump processes and extensions
Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes
Lagrangian Support Vector Machines
When Hardness of Approximation Meets Hardness of Learning
Distribution-Independent Evolvability of Linear Threshold Functions
A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations
Sparse Linear Identifiable Multivariate Modeling
A fast algorithm for learning large scale preference relations
A Finite Newton Algorithm for Non-degenerate Piecewise Linear Systems
Knowledge Hypergraph Embedding Meets Relational Algebra
Conditional random field with high-order dependencies for sequence labeling and segmentation
Clustering Algorithms for Chains
Approximate Newton Methods
Loss Minimization and Parameter Estimation with Heavy Tails
Learnability, Stability and Uniform Convergence
Regression for sets of polynomial equations
Learning partial correlation graphs and graphical models by covariance queries
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
Focused Belief Propagation for Query-Specific Inference
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks
An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity
A Bayesian Divergence Prior for Classiffier Adaptation
A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
Nonuniformity of P-values Can Occur Early in Diverging Dimensions
Guided Visual Exploration of Relations in Data Sets
Hierarchical Average Reward Reinforcement Learning
On Time Varying Undirected Graphs
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Dynamic Tensor Recommender Systems
On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization
Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations
Maximum-likelihood learning of cumulative distribution functions on graphs
Convergence Rates for Gaussian Mixtures of Experts
A Close Look to Margin Complexity and Related Parameters
Unsupervised Tree Boosting for Learning Probability Distributions
ICML2011 Unsupervised and Transfer Learning Workshop
Linear State-Space Models for Blind Source Separation
Discriminative hierarchical part-based models for human parsing and action recognition
Minimax analysis of active learning
Structured Sparsity and Generalization
Collaborative Filtering via Rating Concentration
On Perturbed Proximal Gradient Algorithms
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
On Instrumental Variable Regression for Deep Offline Policy Evaluation
Improved Generalization Bounds for Adversarially Robust Learning
An Empirical Study of Bayesian Optimization: Acquisition Versus Partition
Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes
Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality
MLPACK: a scalable C++ machine learning library
Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles
Clustering with Hidden Markov Model on Variable Blocks
On Estimation and Selection for Topic Models
Bayesian Network Learning via Topological Order
The huge Package for High-dimensional Undirected Graph Estimation in R
MAP- and MLE-Based Teaching
Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications
Aggregation of SVM Classifiers Using Sobolev Spaces
Dynamic Factorization Tests: Applications to Multi-modal Data Association
Bayesian Inference and Optimal Design in the Sparse Linear Model
Faithfulness in Chain Graphs: The Gaussian Case
MDL Histogram Density Estimation
PAC-learning for Strategic Classification
Hilbert Space Embeddings and Metrics on Probability Measures
Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods
A Combination of Boosting and Bagging for KDD Cup 2009 - Fast Scoring on a Large Database
A Study of Approximate Inference in Probabilistic Relational Models
Transport Analysis of Infinitely Deep Neural Network
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Cross-associating unlabelled timbre distributions to create expressive musical mappings
Joint Causal Inference from Multiple Contexts
Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications
Group Nonnegative Matrix Factorization for EEG Classification
Robust Synthetic Control
Dependence, Correlation and Gaussianity in Independent Component Analysis
Consistency and Localizability
Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning
Divvy: fast and intuitive exploratory data analysis
Efficient Computation of Rankings from Pairwise Comparisons
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Partykit: a modular toolkit for recursive partytioning in R
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
A Momentumized, Adaptive, Dual Averaged Gradient Method
Scalable Gaussian-process regression and variable selection using Vecchia approximations
A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families
On the Intrinsic Structures of Spiking Neural Networks
Revisiting RIP Guarantees for Sketching Operators on Mixture Models
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
Two-way Parallel Class Expression Learning
Constrained 1-Spectral Clustering
Learning a Robust Relevance Model for Search Using Kernel Methods
Very Fast Online Learning of Highly Non Linear Problems
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Minimax Policies for Combinatorial Prediction Games
Exploration in relational domains for model-based reinforcement learning
A General Framework for the Analysis of Kernel-based Tests
Divide and Transfer: an Exploration of Segmented Transfer to Detect Wikipedia Vandalism
Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion
The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation
Learning Taxonomy Adaptation in Large-scale Classification
Outlier-Robust Subsampling Techniques for Persistent Homology
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes
Sparse Probabilistic Principal Component Analysis
Weakly Supervised Disentangled Generative Causal Representation Learning
Covariance in Unsupervised Learning of Probabilistic Grammars
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Handwritten Text Recognition for Ancient Documents
Regularized Discriminant Analysis, Ridge Regression and Beyond
Inconsistency of Pitman-Yor process mixtures for the number of components
Multi-class Discriminant Kernel Learning via Convex Programming
Quantum set intersection and its application to associative memory
Expected Policy Gradients for Reinforcement Learning