yusuke1997/dblp-id-title huggingface dataset:
Optimal Transport: Fast Probabilistic Approximation with Exact Solvers
On linearly constrained minimum variance beamforming
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption
GemBag: Group Estimation of Multiple Bayesian Graphical Models
A Family of Simple Non-Parametric Kernel Learning Algorithms
MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
An Improved GLMNET for L1-regularized Logistic Regression
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
Variational Bayesian Matching
Working Set Selection Using Second Order Information for Training Support Vector Machines
Compressed Gaussian Process for Manifold Regression
Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?
Learning Causal Structure from Overlapping Variable Sets
Recall Systems: Effcient Learning and Use of Category Indices
Statistical and Computational Guarantees for the Baum-Welch Algorithm
Learning Using Anti-Training with Sacrificial Data
CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges
High-dimensional Gaussian graphical models on network-linked data
Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
Semi-Supervised Novelty Detection
Network Regression with Graph Laplacians
Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard
Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees"
On Tight Bounds for the Lasso
Learning with Decision Lists of Data-Dependent Features
Relational Reinforcement Learning for Planning with Exogenous Effects
GPflow: A Gaussian Process Library using TensorFlow
O-IPCAC and its Application to EEG Classification
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
PAC-Bayes Analysis Of Maximum Entropy Classification
Particle gibbs with ancestor sampling
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders
Memory of recurrent networks: Do we compute it right?
Dimensionality Reduction via Sparse Support Vector Machines
Waffles : A Machine Learning Toolkit
Generalization from Observed to Unobserved Features by Clustering
Community-Based Group Graphical Lasso
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Functional Directed Acyclic Graphs
Random Rotation Ensembles
skrl: Modular and Flexible Library for Reinforcement Learning
Nesterov's Acceleration for Approximate Newton
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization
Markov Blanket and Markov Boundary of Multiple Variables
Shallow Parsing using Noisy and Non-Stationary Training Material
Analysis of Classification-based Policy Iteration Algorithms
Conditional independences and causal relations implied by sets of equations
Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models
When causality matters for prediction
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Extensions to Metric-Based Model Selection
Inductive Transfer for Bayesian Network Structure Learning
Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking
Improved Classification Rates for Localized SVMs
Generalization Bounds for Adversarial Contrastive Learning
Polytope samplers for inference in ill-posed inverse problems
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Differentially Private Topological Data Analysis
Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior
Maximum Selection and Sorting with Adversarial Comparators
Multiclass Boosting for Weak Classifiers
Sequential Monte Carlo Samplers for Dirichlet Process Mixtures
Generic Inference in Latent Gaussian Process Models
Online Learning to Rank with Top-k Feedback
Kernel Belief Propagation
Adaptive False Discovery Rate Control under Independence and Dependence
Restricted Eigenvalue Properties for Correlated Gaussian Designs
A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points
Latent Wishart Processes for Relational Kernel Learning
Post-Regularization Confidence Bands for Ordinary Differential Equations
A Method of Moments for Mixture Models and Hidden Markov Models
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
Distinguishing between cause and effect
Deep Limits and a Cut-Off Phenomenon for Neural Networks
Learning Non-Stationary Dynamic Bayesian Networks
A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Prediction Risk for the Horseshoe Regression
On the Estimation of Network Complexity: Dimension of Graphons
Hierarchical Probabilistic Models for Group Anomaly Detection
Parallel-in-Time Probabilistic Numerical ODE Solvers
Maximal Causes for Non-linear Component Extraction
Expected Regret and Pseudo-Regret are Equivalent When the Optimal Arm is Unique
Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms
Stability of density-based clustering
Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?
Beyond the Golden Ratio for Variational Inequality Algorithms
The xyz algorithm for fast interaction search in high-dimensional data
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
Topology Selection in Graphical Models of Autoregressive Processes
Quantile regression with ReLU Networks: Estimators and minimax rates
Minimum Density Hyperplanes
Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation
Learning Data-adaptive Non-parametric Kernels
Learning sparse low-threshold linear classifiers
Linear Distance Metric Learning with Noisy Labels
The Stationary Subspace Analysis Toolbox
Construction of approximation spaces for reinforcement learning
Rates of convergence for density estimation with generative adversarial networks
Approximate Tree Kernels
A Generalized Kernel Approach to Dissimilarity-based Classification
Algorithms for discovery of multiple Markov boundaries
Data Biased Robust Counter Strategies
Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent
Persistence Images: A Stable Vector Representation of Persistent Homology
Deep Boltzmann Machines as Feed-Forward Hierarchies
Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial
Optimal Parameter-Transfer Learning by Semiparametric Model Averaging
Using trajectory data to improve bayesian optimization for reinforcement learning
A Software System for the Microbial Source Tracking Problem
The AIM and EM Algorithms for Learning from Coarse Data
Learning Hidden Variable Networks: The Information Bottleneck Approach
Information Theoretic Model Validation for Spectral Clustering
Learning Factor Graphs in Polynomial Time and Sample Complexity
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
Determining the Number of Latent Factors in Statistical Multi-Relational Learning
A Compression Approach to Support Vector Model Selection
Local Kernel Density Ratio-Based Feature Selection for Outlier Detection
Trust Region Newton Method for Logistic Regression
Differentially Private Empirical Risk Minimization
A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning
Using Confidence Bounds for Exploitation-Exploration Trade-offs
Weather Data Mining Using Independent Component Analysis
Sally: a tool for embedding strings in vector spaces
Structured Prediction Cascades
Inspecting Sample Reusability for Active Learning
On the Hyperparameters in Stochastic Gradient Descent with Momentum
How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information
Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics
What Causes the Test Error? Going Beyond Bias-Variance via ANOVA
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Learning from Comparisons and Choices
A Direct Method for Building Sparse Kernel Learning Algorithms
Optimal and Robust Price Experimentation: Learning by Lottery
Structure Discovery in Bayesian Networks by Sampling Partial Orders
Sharp Oracle Inequalities for Square Root Regularization
Learning Causal Networks via Additive Faithfulness
Ranking by calibrated AdaBoost
A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data
Approximate Marginals in Latent Gaussian Models
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
An Analysis of Robustness of Non-Lipschitz Networks
Loop Corrections for Approximate Inference on Factor Graphs
Bayesian Algorithms for Causal Data Mining
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
Perturbation based Large Margin Approach for Ranking
Model Averaging for Prediction with Discrete Bayesian Networks
Online Learning with Samples Drawn from Non-identical Distributions
Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model
Fast Iterative Kernel Principal Component Analysis
Gaussian Interpolation Flows
Efficient Computation of Gapped Substring Kernels on Large Alphabets
Merlion: End-to-End Machine Learning for Time Series
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
Inductive Principles for Restricted Boltzmann Machine Learning
Semi-Supervised Affinity Propagation with Instance-Level Constraints
Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity
Maximum Principle Based Algorithms for Deep Learning
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
Policy Search using Paired Comparisons
Structure-adaptive Manifold Estimation
Algorithms for Learning Kernels Based on Centered Alignment
Fast Committee-Based Structure Learning
Regularized bundle methods for convex and non-convex risks
The Laplacian Eigenmaps Latent Variable Model
Neural conditional random fields
Mappings for Marginal Probabilities with Applications to Models in Statistical Physics
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency
Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality
Neyman-Pearson classification: parametrics and sample size requirement
On Inclusion-Driven Learning of Bayesian Networks
SFO: A Toolbox for Submodular Function Optimization
Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
A theory of multiclass boosting
Boosted Optimization for Network Classification
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
Large Scale Online Learning of Image Similarity Through Ranking
Iterative Regularization for Learning with Convex Loss Functions
Equivalence of Graphical Lasso and Thresholding for Sparse Graphs
Robust Statistics for Describing Causality in Multivariate Time Series
Augmentable Gamma Belief Networks
MurTree: Optimal Decision Trees via Dynamic Programming and Search
Reinforcement Learning for Joint Optimization of Multiple Rewards
Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions
Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure
Mathematical Framework for Online Social Media Auditing
Scaling Laws from the Data Manifold Dimension
Estimating Uncertainty Intervals from Collaborating Networks
Novel Min-Max Reformulations of Linear Inverse Problems
Monotonic Calibrated Interpolated Look-Up Tables
L-SVRG and L-Katyusha with Arbitrary Sampling
Enhancing Identification of Causal Effects by Pruning
Universal Latent Space Model Fitting for Large Networks with Edge Covariates
Active Learning with Hinted Support Vector Machine
The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures
Regularization on Graphs with Function-adapted Diffusion Processes
Generalized Darting Monte Carlo
Ranking a Random Feature for Variable and Feature Selection
EnsembleSVM: a library for ensemble learning using support vector machines
GANs as Gradient Flows that Converge
Linear-Time Estimators for Propensity Scores
LocalGAN: Modeling Local Distributions for Adversarial Response Generation
Estimation of Gradients and Coordinate Covariation in Classification
Induction of Non-Deterministic Finite Automata on Supercomputers
Second-Order Stochastic Optimization for Machine Learning in Linear Time
Provably Accurate Double-Sparse Coding
Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems
An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data
Composite self-concordant minimization
Active Batch Learning with Tree Ensembles
Spatial meshing for general Bayesian multivariate models
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
Two-Layer Multiple Kernel Learning
Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation
Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Dissimilarity in Graph-Based Semi-Supervised Classification
Modelling Interactions in High-dimensional Data with Backtracking
RCV1: A New Benchmark Collection for Text Categorization Research
Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering
An introduction to artificial prediction markets for classification
Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures
On Online Learning of Decision Lists
Maximum-Gain Working Set Selection for SVMs
Visualization Databases for the Analysis of Large Complex Datasets
Structured Output Learning with High Order Loss Functions
The Interplay of Optimization and Machine Learning Research
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks
Active Automata Learning: From DFAs to Interface Programs and Beyond
Avalanche: A PyTorch Library for Deep Continual Learning
Open Problem: Does AdaBoost Always Cycle?
Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
Constraint Reasoning Embedded Structured Prediction
Gradients Weights improve Regression and Classification
A unified view of performance metrics: translating threshold choice into expected classification loss
Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
Conditional Density Estimation via Least-Squares Density Ratio Estimation
Support Vector Clustering
Actively Learning Probabilistic Subsequential Transducers
Optimal First-Order Algorithms as a Function of Inequalities
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
Some Theory for Generalized Boosting Algorithms
Hash Kernels
MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
Generalization Bound for Infinitely Divisible Empirical Process
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Relative Novelty Detection
Non-parametric Quantile Regression via the K-NN Fused Lasso
Efficient and Exact MAP-MRF Inference using Branch and Bound
NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
Explaining by Removing: A Unified Framework for Model Explanation
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
SQLFlow: An Extensible Toolkit Integrating DB and AI
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
Nonparametric Inference under B-bits Quantization
On efficient multilevel Clustering via Wasserstein distances
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality
Bayesian nonparametric hidden semi-Markov models
On the Impact of Kernel Approximation on Learning Accuracy
Attribution-based Explanations that Provide Recourse Cannot be Robust
Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
Classification of Time Sequences using Graphs of Temporal Constraints
Alexey Chervonenkis's bibliography: introductory comments
Marginalizing Out Transition Probabilities for Several Subclasses of PFAs
ProtoAttend: Attention-Based Prototypical Learning
The multimarginal optimal transport formulation of adversarial multiclass classification
On Batch Teaching Without Collusion
Using Contextual Representations to Efficiently Learn Context-Free Languages
Truncated Emphatic Temporal Difference Methods for Prediction and Control
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Non-negative Matrix Factorization with Sparseness Constraints
Dimension Reduction and MARS
Expectation propagation for neural networks with sparsity-promoting priors
Statistical Models for Exploring Individual Email Communication Behavior
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima
On the Influence of the Kernel on the Consistency of Support Vector Machines
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing
Sharpness-Aware Minimization and the Edge of Stability
AMLB: an AutoML Benchmark
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
Introduction to Special Issue on Independent Components Analysis
Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries
Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality
Multi-class Heterogeneous Domain Adaptation
Complexity-Based Approach to Calibration with Checking Rules
Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control
FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection
Stable Implementation of Probabilistic ODE Solvers
Learning Representations of Persistence Barcodes
Confidence and Uncertainty Assessment for Distributional Random Forests
Noise Accumulation in High Dimensional Classification and Total Signal Index
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
On Finding Predictors for Arbitrary Families of Processes
No Internal Regret via Neighborhood Watch
Unlabeled Compression Schemes for Maximum Classes
Global convergence of online limited memory BFGS
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
Combining Experiments to Discover Linear Cyclic Models with Latent Variables
Matched Bipartite Block Model with Covariates
Multi-label Active Learning with Auxiliary Learner
A Universal Well-Calibrated Algorithm for On-line Classification
Active-set Methods for Submodular Minimization Problems
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
Quasi-Newton methods: a new direction
Learning to rank with extremely randomized trees
Revisiting Stein's paradox: multi-task averaging
Multi-Objective Markov Decision Processes for Data-Driven Decision Support
Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization
Coherence functions with applications in large-margin classification methods
On the Size of Convex Hulls of Small Sets
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Quasi-Equivalence between Width and Depth of Neural Networks
Universal Approximation in Dropout Neural Networks
Inducing Tree-Substitution Grammars
Graph estimation from multi-attribute data
Information Retrieval by Inferring Implicit Queries from Eye Movements
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs
Key Instance Detection in Multi-Instance Learning
Deep Learning of Representations for Unsupervised and Transfer Learning
Combined l1 and greedy l0 penalized least squares for linear model selection
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
Value Function Based Reinforcement Learning in Changing Markovian Environments
Eigenwords: spectral word embeddings
Bridging Supervised Learning and Test-Based Co-optimization
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning
Quantile Graphical Models: a Bayesian Approach
PyStruct: learning structured prediction in python
Tslearn, A Machine Learning Toolkit for Time Series Data
Least-Squares Policy Iteration
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Lower bounds and selectivity of weak-consistent policies in stochastic multi-armed bandit problem
Convex and scalable weakly labeled SVMs
PAC-learnability of Probabilistic Deterministic Finite State Automata
Integrating Random Effects in Deep Neural Networks
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
Soft Margin Support Vector Classification as Buffered Probability Minimization
Fast Active-set-type Algorithms for L1-regularized Linear Regression
Fast Stagewise Sparse Factor Regression
Learning theory analysis for association rules and sequential event prediction
Accelerated Alternating Projections for Robust Principal Component Analysis
A Geometric Approach to Sample Compression
Angle-based Multicategory Distance-weighted SVM
Pathwise Conditioning of Gaussian Processes
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective
Multitask Learning in Computational Biology
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization
Stochastic Bandit Based on Empirical Moments
High-dimensional Varying Index Coefficient Models via Stein's Identity
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games
Learning Equivalence Classes of Bayesian-Network Structures