yusuke1997/dblp-id-title huggingface dataset:
RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments
Memoryless Sequences for General Losses
apricot: Submodular selection for data summarization in Python
Regularized Policy Iteration with Nonparametric Function Spaces
Joint Continuous and Discrete Model Selection via Submodularity
OMLT: Optimization & Machine Learning Toolkit
How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis
Exploiting tree-based variable importances to selectively identify relevant variables
Automatic Differentiation Variational Inference
Bayesian variable order Markov models
A Hybrid Neural Network-Latent Topic Model
Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case
An Approximate Analytical Approach to Resampling Averages
Hidden Topic Markov Models
Nonparametric Regression for 3D Point Cloud Learning
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
Conic Optimization for Quadratic Regression Under Sparse Noise
Sparse Additive Gaussian Process Regression
Adjusting for Chance Clustering Comparison Measures
Information, Divergence and Risk for Binary Experiments
Behavior Priors for Efficient Reinforcement Learning
Stability and Generalization in Structured Prediction
Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model
Kernel Operations on the GPU, with Autodiff, without Memory Overflows
Hierarchical Gaussian Process Regression
The Libra toolkit for probabilistic models
Graph Reduction with Spectral and Cut Guarantees
A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases
Robust approachability and regret minimization in games with partial monitoring
Determinantal Point Processes for Coresets
Active Coevolutionary Learning of Deterministic Finite Automata
Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
A Stochastic Bundle Method for Interpolation
Variational Inference for Deblending Crowded Starfields
Word-Sequence Kernels
Learning Spectral Clustering, With Application To Speech Separation
Learning from Noisy Pairwise Similarity and Unlabeled Data
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
Sparse and Smooth Signal Estimation: Convexification of L0-Formulations
pyGPs: a Python library for Gaussian process regression and classification
Numerical Analysis near Singularities in RBF Networks
High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix
An Eigenmodel for Dynamic Multilayer Networks
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
A simple multi-armed bandit algorithm with optimal variation-bounded regret
Consistent Nonparametric Tests of Independence
A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates
How Do You Want Your Greedy: Simultaneous or Repeated?
Active Clustering of Biological Sequences
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Learning Balls of Strings from Edit Corrections
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables
Sample-efficient Adversarial Imitation Learning
Transfer Learning in Sequential Decision Problems: A Hierarchical Bayesian Approach
On semi-supervised linear regression in covariate shift problems
A Novel Integer Linear Programming Approach for Global L0 Minimization
Kernels for Sequentially Ordered Data
Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Generalized Power Method for Sparse Principal Component Analysis
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
Analysis of a Random Forests Model
Local and global scaling reduce hubs in space
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
Learning transformations for clustering and classification
Adaptation to the Range in K-Armed Bandits
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
Learning Equivariant Functions with Matrix Valued Kernels
Analysis of Langevin Monte Carlo via Convex Optimization
Classifier selection using the predicate depth
LLORMA: Local Low-Rank Matrix Approximation
Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness
Robust PCA by Manifold Optimization
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Optimal Rates of Distributed Regression with Imperfect Kernels
Tractable Bayesian Inference of Time-Series Dependence Structure
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates
Efficient Structure Learning of Bayesian Networks using Constraints
Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions
Graph Clustering with Graph Neural Networks
Open Problem: Regret Bounds for Thompson Sampling
A Variational Approach to Bayesian Phylogenetic Inference
Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns
Winning the KDD Cup Orange Challenge with Ensemble Selection
Text Chunking based on a Generalization of Winnow
Optimal Locally Private Nonparametric Classification with Public Data
Nonlinear Boosting Projections for Ensemble Construction
Unified Binary and Multiclass Margin-Based Classification
The Measure and Mismeasure of Fairness
Contextual Bandit Learning with Predictable Rewards
Hyperparameter Optimization via Sequential Uniform Designs
Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors
Stochastic variational inference
Approximating the permanent with fractional belief propagation
Distilled sensing: selective sampling for sparse signal recovery
Policy Gradient in Continuous Time
Causality: Objectives and Assessment
Change Point Problems in Linear Dynamical Systems
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
Distributed Submodular Maximization
Feature Selection using Multiple Streams
Decorrelated Variable Importance
Trading regret for efficiency: online convex optimization with long term constraints
Classification Using Geometric Level Sets
RLPy: a value-function-based reinforcement learning framework for education and research
Attribute Selection Based on FRiS-Compactness
Minimal Sample Subspace Learning: Theory and Algorithms
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity
Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation
Averaged Collapsed Variational Bayes Inference
Structure Learning of Undirected Graphical Models for Count Data
Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Dependent Indian Buffet Processes
Natural evolution strategies
Bounds for Linear Multi-Task Learning
Active Learning as Non-Convex Optimization
Complexity of Bethe Approximation
Regret Bounds for Gaussian Process Bandit Problems
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
Evolutionary Variational Optimization of Generative Models
Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma
Adaptive Learning of Density Ratios in RKHS
Exploiting the High Predictive Power of Multi-class Subgroups
Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design
Factorized Asymptotic Bayesian Inference for Mixture Modeling
Convex Calibration Dimension for Multiclass Loss Matrices
Combining Predictors for Recommending Music: the False Positives' approach to KDD Cup track 2
Maximum volume clustering: a new discriminative clustering approach
Learning the structure and parameters of large-population graphical games from behavioral data
Regulating Greed Over Time in Multi-Armed Bandits
Learning Latent Tree Graphical Models
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs
Semigroup Kernels on Measures
Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns
An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
Nonlinear Online Classificatand Michion Algorithm with Probability Margin
Counterfactual Mean Embeddings
Cramer-Wold Auto-Encoder
Optimal Transport for Stationary Markov Chains via Policy Iteration
Randomized Spectral Co-Clustering for Large-Scale Directed Networks
Active lmitation learning: formal and practical reductions to I.I.D. learning
Reverse Iterative Volume Sampling for Linear Regression
A flexible model-free prediction-based framework for feature ranking
Electronic Health Record Analysis via Deep Poisson Factor Models
Active Sequential Learning with Tactile Feedback
Optimality of Poisson processes intensity learning with Gaussian processes
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
Expectation Consistent Approximate Inference
Effective sampling and learning for mallows models with pairwise-preference data
Reinforcement Learning in Finite MDPs: PAC Analysis
Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation
Probabilistic preference learning with the Mallows rank model
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds
The DFS Fused Lasso: Linear-Time Denoising over General Graphs
In All Likelihood, Deep Belief Is Not Enough
Evaluating Instrument Validity using the Principle of Independent Mechanisms
Efficient Margin Maximizing with Boosting
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
Identifying Cause and Effect on Discrete Data using Additive Noise Models
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model
A view of margin losses as regularizers of probability estimates
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics
Sparseness of Support Vector Machines
PyOD: A Python Toolbox for Scalable Outlier Detection
State-Space Inference and Learning with Gaussian Processes
Transfer Learning with Cluster Ensembles
Learning with Structured Sparsity
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
Content-based Image Retrieval with Multinomial Relevance Feedback
iNNvestigate Neural Networks!
Approximate Counting of Graphical Models Via MCMC
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models
Online Learning of Complex Prediction Problems Using Simultaneous Projections
A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds
Fisher Consistency of Multicategory Support Vector Machines
Towards Understanding Situated Natural Language
Linking Granger Causality and the Pearl Causal Model with Settable Systems
Faster Rates of Differentially Private Stochastic Convex Optimization
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
Pykg2vec: A Python Library for Knowledge Graph Embedding
A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis
How Powerful Can Any Regression Learning Procedure Be?
Globally Injective ReLU Networks
Pygmtools: A Python Graph Matching Toolkit
Learning From Ordered Sets and Applications in Collaborative Ranking
Simple, Robust and Optimal Ranking from Pairwise Comparisons
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach
Blind Source Recovery: A Framework in the State Space
The Power of Contrast for Feature Learning: A Theoretical Analysis
Data-driven Calibration of Penalties for Least-Squares Regression
Neural Autoregressive Distribution Estimation
Bandits, Query Learning, and the Haystack Dimension
On the Proper Learning of Axis-Parallel Concepts
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity
Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers
Learning to Rank for Personalized News Article Retrieval
Accelerating t-SNE using tree-based algorithms
Simultaneous pursuit of sparseness and rank structures for matrix decomposition
The Set Covering Machine
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Discriminative Mixtures of Sparse Latent Fields for Risk Management
SIGNET: Boolean Rile Deetermination for Abscisic Acid Signaling
Practical Large Scale Classification with Additive Kernels
On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm
A General Linear Non-Gaussian State-Space Model
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
Better Algorithms for Benign Bandits
A Convergent Online Single Time Scale Actor Critic Algorithm
Learning from positive and unlabeled examples by enforcing statistical significance
River: machine learning for streaming data in Python
Minimax Estimation of Kernel Mean Embeddings
TorchOpt: An Efficient Library for Differentiable Optimization
Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data
High-dimensional Covariance Estimation Based On Gaussian Graphical Models
Posterior Contraction for Deep Gaussian Process Priors
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
Optimal Convergence Rates for Distributed Nystroem Approximation
A Representer Theorem for Deep Neural Networks
A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators
A comprehensive survey on safe reinforcement learning
The Rademacher Complexity of Co-Regularized Kernel Classes
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
New Convergence Aspects of Stochastic Gradient Algorithms
Denoising Source Separation
Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization
On the Robustness to Misspecification of α-posteriors and Their Variational Approximations
Scaling up Data Augmentation MCMC via Calibration
Neyman-Pearson Classification, Convexity and Stochastic Constraints
Community detection in sparse latent space models
Power Iteration for Tensor PCA
Smooth neighborhood recommender systems
Similarity-based clustering by left-stochastic matrix factorization
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
Non-Sparse Multiple Kernel Fisher Discriminant Analysis
Ranking Categorical Features Using Generalization Properties
An Inertial Newton Algorithm for Deep Learning
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
Using Markov Blankets for Causal Structure Learning
Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes
Dual Temporal Difference Learning
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis
On Partitioning Rules for Bipartite Ranking
OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems
A Bayes-Optimal View on Adversarial Examples
Temporal Abstraction in Reinforcement Learning with the Successor Representation
Best Agglomerative Ranked Subset for Feature Selection
Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
Mixed Regression via Approximate Message Passing
Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis
A Kernel Two-Sample Test
Clusterability: A Theoretical Study
Variational algorithms for marginal MAP
fastFM: A Library for Factorization Machines
X -Armed Bandits
Efficient state-space inference of periodic latent force models
Semi-Supervised Learning with Max-Margin Graph Cuts
Random Search for Hyper-Parameter Optimization
ChainerRL: A Deep Reinforcement Learning Library
Committee Based Prediction System for Recommendation: KDD Cup 2011, Track2
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
Personalized Ranking for Non-Uniformly Sampled Items
Random Design Analysis of Ridge Regression
Small Transformers Compute Universal Metric Embeddings
IALE: Imitating Active Learner Ensembles
Hartigan's Method: k-means Clustering without Voronoi
Improving multiple-try Metropolis with local balancing
A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be
Optimal data collection for informative rankings expose well-connected graphs
Large Scale Transductive SVMs
Deterministic Annealing for Multiple-Instance Learning
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
Path Length Bounds for Gradient Descent and Flow
Inference and Learning in Networks of Queues
Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression
Model-Free Representation Learning and Exploration in Low-Rank MDPs
Nonparametric Estimation of Conditional Information and Divergences
Structured Sparsity via Alternating Direction Methods
In Search of Non-Gaussian Components of a High-Dimensional Distribution
Exact Asymptotics for Linear Quadratic Adaptive Control
Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling
Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection
Bayesian Generalized Kernel Models
Early Stopping for Iterative Regularization with General Loss Functions
Transfer in Reinforcement Learning via Shared Features
Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
A conditional game for comparing approximations
On Mahalanobis Distance in Functional Settings
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries
MMD Aggregated Two-Sample Test
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory
Classes of Kernels for Machine Learning: A Statistics Perspective
Approximate parameter inference in a stochastic reaction-diffusion model
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Seeded graph matching for correlated Erdös-Rényi graphs
Integrating Grammatical Inference into Robotic Planning
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning
Inference In High-dimensional Single-Index Models Under Symmetric Designs
Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters
On Tail Decay Rate Estimation of Loss Function Distributions
An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference
Joint harmonic functions and their supervised connections
The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem
Kernel Approximation Methods for Speech Recognition
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
Minimax rates for homology inference
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
Learning Polyhedral Classifiers Using Logistic Function
On the mutual nearest neighbors estimate in regression
From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions
The Locally Weighted Bag of Words Framework for Document Representation
Community models for networks observed through edge nominations
Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Clustering and Structural Robustness in Causal Diagrams
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
Dynamic Policy Programming with Function Approximation
Tree-based Node Aggregation in Sparse Graphical Models
On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations
Lifted Bregman Training of Neural Networks
Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis
Topologically penalized regression on manifolds
Spectral Algorithms for Community Detection in Directed Networks
Bandit Learning in Decentralized Matching Markets
Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing
Frames, Reproducing Kernels, Regularization and Learning
Learning Probabilistic Models: An Expected Utility Maximization Approach
Half Transductive Ranking
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA
An Information Criterion for Variable Selection in Support Vector Machines
Sparse and low-rank multivariate Hawkes processes
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
KoPA: Automated Kronecker Product Approximation
Detecting Network Cliques with Radon Basis Pursuit
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples
EP-GIG Priors and Applications in Bayesian Sparse Learning
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models
A Unified Theory of Diversity in Ensemble Learning
Matrix Completion with Noisy Entries and Outliers
Learning of Bi-ω Languages from Factors
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Comparison of Granger Causality and Phase Slope Index
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions
The Indian Buffet Process: An Introduction and Review
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
Support Vector Machines Under Adversarial Label Noise
Incorporating Functional Knowledge in Neural Networks
Submatrix localization via message passing
Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators