yusuke1997/dblp-id-title huggingface dataset:
Low-Rank and Sparse Structure Pursuit via Alternating Minimization
Your copula is a classifier in disguise: classification-based copula density estimation
A Unified Weight Learning Paradigm for Multi-view Learning
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
Domain Adaptation under Missingness Shift
Safe-Bayesian Generalized Linear Regression
On the Effect of Auxiliary Tasks on Representation Dynamics
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation
Differentiable Divergences Between Time Series
Unifying Importance Based Regularisation Methods for Continual Learning
An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Causal Q-Aggregation for CATE Model Selection
Scalable Gaussian Process Variational Autoencoders
An Optimal Algorithm for Stochastic and Adversarial Bandits
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
On Manifold Regularization
RankDistil: Knowledge Distillation for Ranking
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, AISTATS 1999, Fort Lauderdale, Florida, USA, January 3-6, 1999
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Convex Bounds on the Softmax Function with Applications to Robustness Verification
Learning from Data with Heterogeneous Noise using SGD
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
An Inverse-free Truncated Rayleigh-Ritz Method for Sparse Generalized Eigenvalue Problem
NYTRO: When Subsampling Meets Early Stopping
The Lie-Group Bayesian Learning Rule
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses
Loss Functions for Discriminative Training of Energy-Based Models
CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision
Model Selection for Topic Models via Spectral Decomposition
Online Algorithms with Costly Predictions
Mean Parity Fair Regression in RKHS
Overfitting Explained
A Contraction Approach to Model-based Reinforcement Learning
Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling
Ordered V -information Growth: A Fresh Perspective on Shared Information
No Regret Bound for Extreme Bandits
Robust variance-regularized risk minimization with concomitant scaling
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks
Scalable Variational Gaussian Process Classification
Deep Neural Networks Learn Non-Smooth Functions Effectively
A general class of surrogate functions for stable and efficient reinforcement learning
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems
Obfuscation via Information Density Estimation
AxlePro: Momentum-Accelerated Batched Training of Kernel Machines
Expectation Maximization of Forward Decoding Kernel Machines
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
Minimal enumeration of all possible total effects in a Markov equivalence class
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent
Riemann 2 : Learning Riemannian Submanifolds from Riemannian Data
Fitting ARMA Time Series Models without Identification: A Proximal Approach
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models
Improved Rate of First Order Algorithms for Entropic Optimal Transport
Low-Rank Spectral Learning
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
On Thompson Sampling for Smoother-than-Lipschitz Bandits
Efficient Lifting of MAP LP Relaxations Using k-Locality
Directional Optimism for Safe Linear Bandits
Toward Question-Asking Machines: The Logic of Questions and the Inquiry Calculus
Domain Adaptation and Entanglement: an Optimal Transport Perspective
GAIT: A Geometric Approach to Information Theory
Fisher Auto-Encoders
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
Low-rank MDPs with Continuous Action Spaces
LocoProp: Enhancing BackProp via Local Loss Optimization
Learning Constrained Structured Spaces with Application to Multi-Graph Matching
Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization
Multi-Armed Bandits with Cost Subsidy
Learning Natural Programs from a Few Examples in Real-Time
A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset
On Some Fast And Robust Classifiers For High Dimension, Low Sample Size Data
Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
A Comparative Evaluation of Sequential Feature Selection Algorithms
Robust Matrix Completion from Quantized Observations
Localized Lasso for High-Dimensional Regression
Flexible distribution-free conditional predictive bands using density estimators
Graph Connectivity in Noisy Sparse Subspace Clustering
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Parallel and Distributed MCMC via Shepherding Distributions
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks
Random Effect Bandits
The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits
Belief Propagation in Conditional RBMs for Structured Prediction
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources
Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration
Fast and Scalable Structural SVM with Slack Rescaling
A Uniform Convergence Bound for the Area Under the ROC Curve
ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics
Regression Uncertainty on the Grassmannian
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls
Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference
Optimal Algorithms for Latent Bandits with Cluster Structure
Locally Optimal Descent for Dynamic Stepsize Scheduling
MING: A Functional Approach to Learning Molecular Generative Models
Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms
Convex Invariance Learning
Thresholding Bandit Problem with Both Duels and Pulls
Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models
Transductive conformal inference with adaptive scores
Missing at Random in Graphical Models
Variational Gaussian Copula Inference
A Lower Bound on the Partition Function of Attractive Graphical Models in the Continuous Case
A Sub-Quadratic Exact Medoid Algorithm
Credit Assignment Techniques in Stochastic Computation Graphs
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning
Learning Laplacian Positional Encodings for Heterophilous Graphs
Formal Limitations on the Measurement of Mutual Information
An Information-Theoretic Justification for Model Pruning
Contrasting the landscape of contrastive and non-contrastive learning
Semi-Supervised Classification by Low Density Separation
On Tractability of Learning Bayesian Networks with Ancestral Constraints
Stein Boltzmann Sampling: A Variational Approach for Global Optimization
Nonlinear ICA of Temporally Dependent Stationary Sources
Can Functional Transfer Methods Capture Simple Inductive Biases?
Optimality of Approximate Inference Algorithms on Stable Instances
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits
Safe and Interpretable Estimation of Optimal Treatment Regimes
Learning k-qubit Quantum Operators via Pauli Decomposition
Robust Classification by Coupling Data Mollification with Label Smoothing
SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals
Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders
FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts
Optimising Distributions with Natural Gradient Surrogates
Discriminative Learning of Prediction Intervals
Solving Attention Kernel Regression Problem via Pre-conditioner
Hardness of Learning a Single Neuron with Adversarial Label Noise
Towards a mathematical theory for consistency training in diffusion models
Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis
DIET: Conditional independence testing with marginal dependence measures of residual information
Auditing Fairness under Unobserved Confounding
Learning piecewise Lipschitz functions in changing environments
Gaussian Sketching yields a J-L Lemma in RKHS
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
CLAR: Contrastive Learning of Auditory Representations
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees
Kernel Constrained Covariance for Dependence Measurement
Nonstationary Bandit Learning via Predictive Sampling
Finite-Time Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation
Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search
A simple sketching algorithm for entropy estimation over streaming data
Dynamic content based ranking
Kernel regression in high dimensions: Refined analysis beyond double descent
Linear Submodular Maximization with Bandit Feedback
Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models
Inducing Neural Collapse in Deep Long-tailed Learning
Efficient interventional distribution learning in the PAC framework
Budget-Constrained Bandits over General Cost and Reward Distributions
Dynamic Cutset Networks
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption
Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning
Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication
Understanding Inverse Scaling and Emergence in Multitask Representation Learning
Proximal Causal Inference for Synthetic Control with Surrogates
Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization
Dynamic DBSCAN with Euler Tour Sequences
Probability Inequalities for Kernel Embeddings in Sampling without Replacement
Federated Multi-armed Bandits with Personalization
Power of Hints for Online Learning with Movement Costs
Causal Effect Identification with Context-specific Independence Relations of Control Variables
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation
Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams
Approximating a RUM from Distributions on k-Slates
Stopping criterion for active learning based on deterministic generalization bounds
Estimating Network Structure from Incomplete Event Data
A Bregman Divergence View on the Difference-of-Convex Algorithm
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge
On the interplay between noise and curvature and its effect on optimization and generalization
Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs
DHMConv: Directed Hypergraph Momentum Convolution Framework
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning
Stochastic Optimization for Spectral Risk Measures
Unsupervised Sequential Sensor Acquisition
Efficient Estimation of a Gaussian Mean with Local Differential Privacy
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Common Learning Constraints Alter Interpretations of Direct Preference Optimization
The Fast Kernel Transform
Scalable marked point processes for exchangeable and non-exchangeable event sequences
Stochastic Recursive Variance-Reduced Cubic Regularization Methods
Efficient Algorithms for Extreme Bandits
Interpretable Random Forests via Rule Extraction
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models
A Dirichlet Process Mixture Model for Spherical Data
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning
Deep Neural Networks with Efficient Guaranteed Invariances
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms
Gain estimation of linear dynamical systems using Thompson Sampling
Strategies for Model Mixing in Generalized Linear Models
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent
Cooperative Inverse Decision Theory for Uncertain Preferences
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
GmGM: a fast multi-axis Gaussian graphical model
Data-dependent compression of random features for large-scale kernel approximation
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
A Provable Algorithm for Learning Interpretable Scoring Systems
Two-way Sparse Network Inference for Count Data
Transfer Learning with Gaussian Processes for Bayesian Optimization
Equitable and Optimal Transport with Multiple Agents
Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
Infinitely deep neural networks as diffusion processes
Context-Specific Likelihood Weighting
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation
Online Continuous DR-Submodular Maximization with Long-Term Budget Constraints
Variational Inference for continuous Sigmoidal Bayesian Networks
Nyström Method for Accurate and Scalable Implicit Differentiation
Probabilistic kernel regression models
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
Multivariate Quantile Function Forecaster
Communication Efficient Distributed Agnostic Boosting
Derivative-Free & Order-Robust Optimisation
Optimizing over a Restricted Policy Class in MDPs
Validated Variational Inference via Practical Posterior Error Bounds
Spectral Algorithms for Computing Fair Support Vector Machines
Unsupervised representation learning with recognition-parametrised probabilistic models
A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
Meta-Transportability of Causal Effects: A Formal Approach
Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback
Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data
Free-rider Attacks on Model Aggregation in Federated Learning
Structure of Nonlinear Node Embeddings in Stochastic Block Models
Provable Robustness of ReLU networks via Maximization of Linear Regions
Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model
Differentially Private Conditional Independence Testing
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Mixed Linear Regression via Approximate Message Passing
Equivariant Deep Dynamical Model for Motion Prediction
Learning Nash Equilibrium for General-Sum Markov Games from Batch Data
Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks
Differentiable Causal Backdoor Discovery
Complex Momentum for Optimization in Games
Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence Level
Lagrangian manifold Monte Carlo on Monge patches
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Linear Dynamics: Clustering without identification
Fast Noise Removal for k-Means Clustering
Conditionally Independent Multiresolution Gaussian Processes
Personalized Federated X-armed Bandit
Stochastic Multi-armed Bandits in Constant Space
HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning
Projection-Free Optimization on Uniformly Convex Sets
SoundSynp: Sound Source Detection from Raw Waveforms with Multi-Scale Synperiodic Filterbanks
On Approximate Non-submodular Minimization via Tree-Structured Supermodularity
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes
Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Generalized Boltzmann Machine with Deep Neural Structure
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
Online Page Migration with ML Advice
Differentially Private Regression with Gaussian Processes
The ELBO of Variational Autoencoders Converges to a Sum of Entropies
On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge
Restructuring Dynamic Causal Systems in Equilibrium
Mixed variational flows for discrete variables
Stochastic Extragradient: General Analysis and Improved Rates
Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics
Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANs
Sampling From a Schrödinger Bridge
Corruption-Tolerant Gaussian Process Bandit Optimization
Almost linear time differentially private release of synthetic graphs
Black Box Quantiles for Kernel Learning
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
Wasserstein regularization for sparse multi-task regression
Rejection sampling from shape-constrained distributions in sublinear time
HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step
Fixed-confidence guarantees for Bayesian best-arm identification
On Improving the Efficiency of the Iterative Proportional Fitting Procedure
How to Learn when Data Gradually Reacts to Your Model
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
Learning Prediction Intervals for Regression: Generalization and Calibration
Inference of Dynamic Graph Changes for Functional Connectome
USIM Gate: UpSampling Module for Segmenting Precise Boundaries concerning Entropy
Convergence of Gradient Descent on Separable Data
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems
Neural Contextual Bandits without Regret
Prophets, Secretaries, and Maximizing the Probability of Choosing the Best
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
Direct Acceleration of SAGA using Sampled Negative Momentum
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
Robust Active Label Correction
Contextual Blocking Bandits
Compositional Probabilistic and Causal Inference using Tractable Circuit Models
How to scale hyperparameters for quickshift image segmentation
Embedded Ensembles: infinite width limit and operating regimes
Bayesian Semi-structured Subspace Inference
Practical Bayesian optimization in the presence of outliers
Predictive variational Bayesian inference as risk-seeking optimization
Adversarially Robust Estimate and Risk Analysis in Linear Regression
Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs
Active multiple matrix completion with adaptive confidence sets
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems
Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators
Learning to Round for Discrete Labeling Problems
Quantifying intrinsic causal contributions via structure preserving interventions
Model-based Policy Optimization under Approximate Bayesian Inference
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes
Deep Classifier Mimicry without Data Access
Agnostic Multi-Robust Learning using ERM
Understanding GNNs and Homophily in Dynamic Node Classification
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality
A Locally Adaptive Bayesian Cubature Method
Stochastic System Monitoring and Control
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!
Recursive Autonomy Identification for Bayesian Network Structure Learning
A Novel Confidence-Based Algorithm for Structured Bandits
A Stein Goodness-of-test for Exponential Random Graph Models
Batch Bayesian Optimization via Local Penalization
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning
Semi-Supervised Prediction-Constrained Topic Models
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
Towards Balanced Representation Learning for Credit Policy Evaluation
Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields
Deformable Spectrograms
Variational Speech Separation of More Sources than Mixtures
Graph-based Complexity for Causal Effect by Empirical Plug-in
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression
Training Variational Autoencoders with Buffered Stochastic Variational Inference
Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms