yusuke1997/dblp-id-title huggingface dataset:
Multiple Kernel Learning Algorithms
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Constraint-based causal discovery from multiple interventions over overlapping variable sets
Robust hierarchical clustering
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images
Fast interior-point inference in high-dimensional sparse, penalized state-space models
SnFFT: a Julia toolkit for Fourier analysis of functions over permutations
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data
Comment on "Robustness and regularization of support vector machines" by H. Xu et al. (Journal of machine learning research, volume 10, pp 1485-1510, 2009)
Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision
Online learning via sequential complexities
Convergence of Unregularized Online Learning Algorithms
Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm
Non-parametric Policy Search with Limited Information Loss
Analysis of Thompson Sampling for the Multi-armed Bandit Problem
Neural Estimation of Statistical Divergences
Doubly infinite residual neural networks: a diffusion process approach
Accurate Error Bounds for the Eigenvalues of the Kernel Matrix
Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach
Harry: A Tool for Measuring String Similarity
Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression
Learning to Warm-Start Fixed-Point Optimization Algorithms
Gap Safe Screening Rules for Sparsity Enforcing Penalties
Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees
Semi-supervised Learning by Higher Order Regularization
Information theoretical estimators toolbox
Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity
Learning Functions of Halfspaces using Prefix Covers
Consistency of Gaussian Process Regression in Metric Spaces
SampleSearch: A Scheme that Searches for Consistent Samples
Refinement of Operator-valued Reproducing Kernels
The Non-Overlapping Statistical Approximation to Overlapping Group Lasso
Simplifying Probabilistic Expressions in Causal Inference
Tight Bounds on Proper Equivalence Query Learning of DNF
Directional Statistics on Permutations
Unsupervised Variable Selection: when random rankings sound as irrelevancy
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
Aggregated Hold-Out
Feature Ranking Using Linear SVM
On Multi-Armed Bandit Designs for Dose-Finding Trials
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
Online Clustering with Experts
Chromatic PAC-Bayes Bounds for Non-IID Data
Some Theoretical Insights into Wasserstein GANs
Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
Boosting algorithms for detector cascade learning
Unsupervised Multiple Kernel Learning
Patchwork Kriging for Large-scale Gaussian Process Regression
Gradient Hard Thresholding Pursuit
Uniform Object Generation for Optimizing One-class Classifiers
Gaussian Kullback-Leibler approximate inference
Learning Similarity with Operator-valued Large-margin Classifiers
Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
The Separation Capacity of Random Neural Networks
Estimating Diffusion Probability Changes for AsIC-SIS Model
Learning Certifiably Optimal Rule Lists for Categorical Data
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures
Nonparametric Principal Subspace Regression
A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers
Interactive Pattern Recognition and Human Language Technology for Digital Audiovisual Content Processing
Divide-and-Conquer Fusion
Lasso screening rules via dual polytope projection
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
ILP: A Short Look Back and a Longer Look Forward
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
Automating Quantitative Narrative Analysis of News Data
An Online Convex Optimization Approach to Blackwell's Approachability
Computational Bounds on Statistical Query Learning
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models
Response-based approachability with applications to generalized no-regret problems
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
Optimal Estimation of Sparse Topic Models
AClass: A simple, online, parallelizable algorithm for probabilistic classification
A Variance Minimization Criterion to Active Learning on Graphs
Gaussian Mixture Models with Rare Events
Pattern Recognition for Conditionally Independent Data
Conditions and Assumptions for Constraint-based Causal Structure Learning
Simple Cycle Reservoirs are Universal
Ellipsoidal Machines
BayesPy: Variational Bayesian Inference in Python
Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures
Guarding against Spurious Discoveries in High Dimensions
Weisfeiler-Lehman Graph Kernels
Nonparametric sparsity and regularization
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
Integrating a Partial Model into Model Free Reinforcement Learning
A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines
Bootstrapping and Learning PDFA in Data Streams
Closure-Based Confidence Boost in Association Rules
Evaluation of a Bayesian model-based approach in GA studies
Operator Norm Convergence of Spectral Clustering on Level Sets
Jump Interval-Learning for Individualized Decision Making with Continuous Treatments
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Learning theory of randomized Kaczmarz algorithm
Learning Linear Ranking Functions for Beam Search with Application to Planning
Online Learning of Multiple Tasks and Their Relationships
A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification
ooDACE toolbox: a flexible object-oriented Kriging implementation
Multivariate Bayesian Structural Time Series Model
Informative Priors for Markov Blanket Discovery
Fast and Scalable Local Kernel Machines
bandicoot: a Python Toolbox for Mobile Phone Metadata
Linear regression with random projections
Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
Are All Layers Created Equal?
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
Low rank continuous-space graphical models
Model Merging versus Model Splitting Context-Free Grammar Induction
Loopy Belief Propagation: Convergence and Effects of Message Errors
Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
Exploiting Best-Match Equations for Efficient Reinforcement Learning
Tree-Values: Selective Inference for Regression Trees
Discrete Variational Calculus for Accelerated Optimization
InterpretDL: Explaining Deep Models in PaddlePaddle
Active Learning and Experimental Design with SVMs
Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases
Bayesian time-aligned factor analysis of paired multivariate time series
Learning Dissimilarities for Categorical Symbols
A spectral-based analysis of the separation between two-layer neural networks and linear methods
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs
Domain Adaptation with Coupled Subspaces
Discussion of "A conditional game for comparing approximations"
Improved Random Features for Dot Product Kernels
Single versus Multiple Sorting in All Pairs Similarity Search
A Direct Approach for Sparse Quadratic Discriminant Analysis
Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components
Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning
Estimating Functions for Blind Separation When Sources Have Variance Dependencies
Multiple Descent in the Multiple Random Feature Model
The Asymptotic Performance of Linear Echo State Neural Networks
Learning to Combine Motor Primitives Via Greedy Additive Regression
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits
EV-GAN: Simulation of extreme events with ReLU neural networks
Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
Hierarchical Cost-Sensitive Algorithms for Genome-Wide Gene Function Prediction
Unsupervised Basis Function Adaptation for Reinforcement Learning
Discriminative Topic Segmentation of Text and Speech
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
Deletion and Insertion Tests in Regression Models
Optimal Estimation of Derivatives in Nonparametric Regression
Properties of Monotonic Effects on Directed Acyclic Graphs
Continuous Time Analysis of Momentum Methods
Subspace Clustering through Sub-Clusters
Statistical Inference for Fairness Auditing
Can We Trust the Bootstrap in High-dimensions? The Case of Linear Models
Replica Exchange for Non-Convex Optimization
Metric Learning for Kernel Regression
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
Multiresolution Deep Belief Networks
A finite sample analysis of the Naive Bayes classifier
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
Joint Inference of Multiple Graphs from Matrix Polynomials
Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses
Data dependent kernels in nearly-linear time
CD-split and HPD-split: Efficient Conformal Regions in High Dimensions
Discussion of "Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables"
MAUVE Scores for Generative Models: Theory and Practice
Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks
Complete Search for Feature Selection in Decision Trees
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals
Tutorial on Practical Prediction Theory for Classification
Hierarchical Latent Dictionaries for Models of Brain Activation
Optimistic Bayesian Sampling in Contextual-Bandit Problems
Online Learning with Sample Path Constraints
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
On the Importance of Small Coordinate Projections
Nonparametric Modeling of Higher-Order Interactions via Hypergraphons
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
Deep Exploration via Randomized Value Functions
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
Learning and Model-Checking Networks of I/O Automata
Tight conditions for consistent variable selection in high dimensional nonparametric regression
A Classification Framework for Anomaly Detection
Speedup Learning for Repair-based Search by Identifying Redundant Steps
Decentralized Dictionary Learning Over Time-Varying Digraphs
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data
A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation
ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM
Classification with Deep Neural Networks and Logistic Loss
Activized Learning: Transforming Passive to Active with Improved Label Complexity
Margin-Based Active Learning of Classifiers
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
Transfer Learning by Kernel Meta-Learning
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Covering Number Bounds of Certain Regularized Linear Function Classes
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
Causal Classification: Treatment Effect Estimation vs. Outcome Prediction
Fast Binary Feature Selection with Conditional Mutual Information
Robust High-Dimensional Low-Rank Matrix Estimation: Optimal Rate and Data-Adaptive Tuning
Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling
Salient Point and Scale Detection by Minimum Likelihood
Multi-view Positive and Unlabeled Learning
Optimal Clustering with Bandit Feedback
HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Estimating Density Models with Truncation Boundaries using Score Matching
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
Dimensionality Reduction for Spectral Clustering
Nonparametric Bayesian Aggregation for Massive Data
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space
Learning Low Density Separators
An Empirical Study of the Use of Relevance Information in Inductive Logic Programming
Set-valued Classification with Out-of-distribution Detection for Many Classes
Efficient occlusive components analysis
Residual Splash for Optimally Parallelizing Belief Propagation
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation
Sparse PCA: a Geometric Approach
Hash Kernels for Structured Data
Learning Image Components for Object Recognition
Optimal Bounds for Johnson-Lindenstrauss Transformations
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
Robust Methods for High-Dimensional Linear Learning
Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards
Approximate Bayesian Computation via Classification
Statistical Analysis and Parameter Selection for Mapper
A Worst Case Analysis of Calibrated Label Ranking Multi-label Classification Method
Convex envelopes of complexity controlling penalties: the case against premature envelopment
Strong consistency of the prototype based clustering in probabilistic space
Distribution-Specific Hardness of Learning Neural Networks
Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
Dimension independent similarity computation
Confidence Sets with Expected Sizes for Multiclass Classification
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models
Statistical Query Lower Bounds for Tensor PCA
Part & Clamp: Efficient Structured Output Learning
Particle Swarm Model Selection
Closed-Form Entropy Limits - A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models
A New Class of Time Dependent Latent Factor Models with Applications
A widely applicable Bayesian information criterion
Step Size Adaptation in Reproducing Kernel Hilbert Space
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
Robust Topological Inference: Distance To a Measure and Kernel Distance
Dependency Networks for Inference, Collaborative Filtering, and Data Visualization
Graph-Aided Online Multi-Kernel Learning
SPMF: a Java open-source pattern mining library
Nonparametric adaptive control and prediction: theory and randomized algorithms
Characterizing the Function Space for Bayesian Kernel Models
giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
Learning Operators with Coupled Attention
A General Framework for Constrained Bayesian Optimization using Information-based Search
Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
A Theory of Learning with Corrupted Labels
Interval-censored Hawkes processes
SnapVX: A Network-Based Convex Optimization Solver
Manifold regularization and semi-supervised learning: some theoretical analyses
A Unified Continuous Optimization Framework for Center-Based Clustering Methods
Machine Learning in an Auction Environment
An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
From Low Probability to High Confidence in Stochastic Convex Optimization
Assessing Approximate Inference for Binary Gaussian Process Classification
Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet
Provable Convex Co-clustering of Tensors
Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model
New Horn Revision Algorithms
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming
Why are DBNs sparse?
Partial orientation and local structural learning of causal networks for prediction
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities
Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup'09 Small Data Set
Detecting Sentiment Change in Twitter Streaming Data
Why do deep convolutional networks generalize so poorly to small image transformations?
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery
Multiplicative Multitask Feature Learning
Adaptation Augmented Model-based Policy Optimization
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning
Dimension Reduction in Text Classification with Support Vector Machines
Interlocking Backpropagation: Improving depthwise model-parallelism
BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits
Spectral Clustering Based on Local PCA
Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions
A Potential-based Framework for Online Multi-class Learning with Partial Feedback
Generalized Optimal Reverse Prediction
Collaborative Filtering Ensemble for Ranking
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels
Task Clustering and Gating for Bayesian Multitask Learning
RVCL: Evaluating the Robustness of Contrastive Learning via Verification
Optimistic planning for Markov decision processes
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
Generalized Ambiguity Decomposition for Ranking Ensemble Learning
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
Behavioral Shaping for Geometric Concepts
Harnessing the Expertise of 70, 000 Human Editors: Knowledge-Based Feature Generation for Text Categorization
Optimization Techniques for Semi-Supervised Support Vector Machines
Distributed matrix completion and robust factorization
Iterative and active graph clustering using trace norm minimization without cluster size constraints
Kernel Bayes' rule: Bayesian inference with positive definite kernels
Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
On the Stability of Feature Selection Algorithms
Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening
Bifurcation Spiking Neural Network
On the Representer Theorem and Equivalent Degrees of Freedom of SVR
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Optimized Score Transformation for Consistent Fair Classification
Fast SVM training using approximate extreme points
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
Impossibility Theorems for Domain Adaptation
Nonlinear Models Using Dirichlet Process Mixtures
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
Optimally fuzzy temporal memory
Unsupervised dimensionality reduction via gradient-based matrix factorization with two adaptive learning rates
Causal Aggregation: Estimation and Inference of Causal Effects by Constraint-Based Data Fusion
On Universal Approximation and Error Bounds for Fourier Neural Operators
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
Blind Source Separation via Generalized Eigenvalue Decomposition
Convex Reinforcement Learning in Finite Trials
Supervised feature selection in graphs with path coding penalties and network flows
Discriminative Learning Under Covariate Shift
Protocols for Learning Classifiers on Distributed Data
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Representation Learning for Dynamic Graphs: A Survey
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
A Unified q-Memorization Framework for Asynchronous Stochastic Optimization
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
Spatial Locality-Aware Sparse Coding and Dictionary Learning
Predictive Learning on Hidden Tree-Structured Ising Models
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
A Ranking-based KNN Approach for Multi-Label Classification
On Spectral Learning
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Bayesian co-boosting for multi-modal gesture recognition
Maximum likelihood estimation in Gaussian process regression is ill-posed
Max-margin Classification of Data with Absent Features
Two Sample Testing in High Dimension via Maximum Mean Discrepancy
Locally Private k-Means Clustering
Variational Bridge Regression
Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process
Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls
Online Optimization over Riemannian Manifolds
A Regularization Approach to Nonlinear Variable Selection
On the Riemannian Search for Eigenvector Computation
Modeling Knowledge Worker Activity
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions
In Defense of One-Vs-All Classification
Information Theoretic Model Selection for Pattern Analysis
On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent
Learning Planar Ising Models
Tree-Based Models for Correlated Data