yusuke1997/dblp-id-title huggingface dataset:
Exploration and apprenticeship learning in reinforcement learning
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC
Syllables and other String Kernel Extensions
Approximating Learned Search Control Knowledge
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model
Secure Federated Correlation Test and Entropy Estimation
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Online Clustering of Dueling Bandits
Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
A Receptor Skeleton for Capsule Neural Networks
Sequential Disentanglement by Extracting Static Information From A Single Sequence Element
Provably Learning Object-Centric Representations
Toward a Unified Theory of Gradient Descent under Generalized Smoothness
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Composite Functional Gradient Learning of Generative Adversarial Models
Label-Noise Robust Domain Adaptation
Computation-Risk Tradeoffs for Covariance-Thresholded Regression
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Conditional Graph Information Bottleneck for Molecular Relational Learning
Beam Search based MAP Estimates for the Indian Buffet Process
Sparse Extreme Multi-label Learning with Oracle Property
Finite-Time Regret Bounds for the Multiarmed Bandit Problem
A Hybrid Method for Feature Generation
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
On Greedy Maximization of Entropy
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
Optimizing Black-box Metrics with Iterative Example Weighting
Learning by Analyzing Fortuitous Occurrences
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
State Space Gaussian Processes with Non-Gaussian Likelihood
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Optimized expected information gain for nonlinear dynamical systems
Interactive Bayesian Hierarchical Clustering
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Graph Resistance and Learning from Pairwise Comparisons
Contextual Bandits with Large Action Spaces: Made Practical
Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
Subobject-level Image Tokenization
Stochastic Gradient Monomial Gamma Sampler
Machine Learning for Nondestructive Evaluation
Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention
Feature selection in a kernel space
Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning
MathConstruct: Challenging LLM Reasoning with Constructive Proofs
Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Learning Policies for Partially Observable Environments: Scaling Up
ControlVAE: Controllable Variational Autoencoder
Forecasting Sequential Data Using Consistent Koopman Autoencoders
An adaptive algorithm for finite stochastic partial monitoring
A Comparative Study on Feature Selection in Text Categorization
On Symmetric and Asymmetric LSHs for Inner Product Search
Extrapolative Controlled Sequence Generation via Iterative Refinement
Removing Batch Normalization Boosts Adversarial Training
Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM Dynamics
Towards Making Unlabeled Data Never Hurt
An RKHS for multi-view learning and manifold co-regularization
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
Approximately Correct Label Distribution Learning
Enhancing Target-unspecific Tasks through a Features Matrix
PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering
TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision
Safety-Polarized and Prioritized Reinforcement Learning
Deduplicating Training Data Mitigates Privacy Risks in Language Models
The Case for Learned Provenance-based System Behavior Baseline
Multiview Triplet Embedding: Learning Attributes in Multiple Maps
A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making
A Variational Information Theoretic Approach to Out-of-Distribution Detection
RaFM: Rank-Aware Factorization Machines
Compact Matrix Quantum Group Equivariant Neural Networks
Neural Status Registers
Population-Based Black-Box Optimization for Biological Sequence Design
Offline Opponent Modeling with Truncated Q-driven Instant Policy Refinement
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Embarrassingly Parallel GFlowNets
David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training
Federated Learning with Label Distribution Skew via Logits Calibration
Provable Guarantees for Gradient-Based Meta-Learning
Optimality of Belief Propagation for Crowdsourced Classification
Deep learning via semi-supervised embedding
Batch Policy Learning under Constraints
Matrix Information Theory for Self-Supervised Learning
P(all-atom) Is Unlocking New Path For Protein Design
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
Learning Infinite-horizon Average-reward Markov Decision Process with Constraints
Sequence kernels for predicting protein essentiality
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas
Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations
Implicit Riemannian Optimism with Applications to Min-Max Problems
Reinforcement learning in the presence of rare events
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits
Understanding the Impact of Adversarial Robustness on Accuracy Disparity
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
What Makes for End-to-End Object Detection?
Ad Hoc Teamwork via Offline Goal-Based Decision Transformers
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations
Diffusion Language Models Are Versatile Protein Learners
Low Bias Bagged Support Vector Machines
ROC confidence bands: an empirical evaluation
Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Decision Mixer: Integrating Long-term and Local Dependencies via Dynamic Token Selection for Decision-Making
Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
Mean Estimation in the Add-Remove Model of Differential Privacy
Structured Prediction of Network Response
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum
Language-guided Skill Learning with Temporal Variational Inference
Large Graph Construction for Scalable Semi-Supervised Learning
Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion
K2IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
Total Variation Distance Meets Probabilistic Inference
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
FairPFN: A Tabular Foundation Model for Causal Fairness
Pessimistic decision tree pruning based Continuous-time
Quantum Theory and Application of Contextual Optimal Transport
Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
Ameliorate Spurious Correlations in Dataset Condensation
MEMORYLLM: Towards Self-Updatable Large Language Models
Distributional Diffusion Models with Scoring Rules
Optimal Eye Surgeon: Finding image priors through sparse generators at initialization
Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps
From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering
Clustering via Self-Supervised Diffusion
EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
∞-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation
Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Critical windows: non-asymptotic theory for feature emergence in diffusion models
Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points
Robust Formulations for Handling Uncertainty in Kernel Matrices
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers
Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances
Error-Bounded Correction of Noisy Labels
AutoEval Done Right: Using Synthetic Data for Model Evaluation
Domain adaptation from multiple sources via auxiliary classifiers
Metric-Fair Classifier Derandomization
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Mandoline: Model Evaluation under Distribution Shift
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy
Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss
Lipschitz normalization for self-attention layers with application to graph neural networks
Improving Shared Rules in Multiple Category Domain Theories
Estimating labels from label proportions
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
One-Step Estimator for Permuted Sparse Recovery
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
A Statistical Perspective on Algorithmic Leveraging
New results on information theoretic clustering
Cooperative Graph Neural Networks
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods
Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions
Parametric Visual Program Induction with Function Modularization
CF-OPT: Counterfactual Explanations for Structured Prediction
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
A New Frontier of Kernel Design for Structured Data
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
An Experimental Evaluation of Coevolutive Concept Learning
Hierarchical Models for Screening of Iron Deficiency Anemia
Efficient and Consistent Adversarial Bipartite Matching
PAC Subset Selection in Stochastic Multi-armed Bandits
GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency
Tuning-Free Stochastic Optimization
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach
Sampling is as easy as keeping the consistency: convergence guarantee for Consistency Models
Incremental Randomized Sketching for Online Kernel Learning
Message Passing for Collective Graphical Models
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
Evaluating Bias During Pac-Learning
Bayes optimal classification for decision trees
Predictive low-rank decomposition for kernel methods
An end-to-end approach for the verification problem: learning the right distance
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
On-line discovery of temporal-difference networks
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
Fairness Interventions as (Dis)Incentives for Strategic Manipulation
Inverse Reinforcement Learning without Reinforcement Learning
WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems
Network Global Testing by Counting Graphlets
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
Tensor Analyzers
Independence Tests for Language Models
A Psychological Theory of Explainability
Exponential Family Matrix Completion under Structural Constraints
From Word Embeddings To Document Distances
How Jellyfish Characterise Alternating Group Equivariant Neural Networks
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning
Combining model-based and instance-based learning for first order regression
Globally Induced Forest: A Prepruning Compression Scheme
Cooperative Multi-Agent Bandits with Heavy Tails
WaveFlow: A Compact Flow-based Model for Raw Audio
Single Pass Entrywise-Transformed Low Rank Approximation
Sparse Gaussian Processes with Spherical Harmonic Features
VideoPoet: A Large Language Model for Zero-Shot Video Generation
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
Deep networks for robust visual recognition
Overcoming Feature Space Bias in a Reactive Environment
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Causal structure-based root cause analysis of outliers
Statistical models for partial membership
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
Optimal Minimal Margin Maximization with Boosting
A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
IM-Unpack: Training and Inference with Arbitrarily Low Precision Integers
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
On Measure Concentration of Random Maximum A-Posteriori Perturbations
Restricted Boltzmann machines for collaborative filtering
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
Safe screening rules for L0-regression from Perspective Relaxations
Mind the Box: l 1 -APGD for Sparse Adversarial Attacks on Image Classifiers
On the Relationship between Sum-Product Networks and Bayesian Networks
Symmetry-Aware GFlowNets
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation
Graph2Tac: Online Representation Learning of Formal Math Concepts
Variational Annealing of GANs: A Langevin Perspective
Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization
Signal recovery from Pooling Representations
The Effect of Natural Distribution Shift on Question Answering Models
NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors
Gaussian process classification for segmenting and annotating sequences
A Generalization of ViT/MLP-Mixer to Graphs
Spectral clustering and transductive learning with multiple views
Continuous-time Model-based Reinforcement Learning
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
Bayesian Differential Privacy for Machine Learning
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity
Theoretical insights for diffusion guidance: A case study for Gaussian mixture models
AI Alignment with Changing and Influenceable Reward Functions
Hierarchical kernel stick-breaking process for multi-task image analysis
Generalizing the Order of Goals as an Approach to Generalizing Number
Unsupervised Domain Adaptation by Backpropagation
Selective Dyna-Style Planning Under Limited Model Capacity
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
Spectral Learning from a Single Trajectory under Finite-State Policies
On Hypothesis Transfer Learning of Functional Linear Models
Hierarchically Classifying Documents Using Very Few Words
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Beyond CCA: Moment Matching for Multi-View Models
Differentially Private Optimization on Large Model at Small Cost
Scalable Training of Inference Networks for Gaussian-Process Models
Learning Opinions in Social Networks
Learning Representations without Compositional Assumptions
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Tilting the playing field: Dynamical loss functions for machine learning
Coarse-To-Fine Tensor Trains for Compact Visual Representations
Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison
GaussianPro: 3D Gaussian Splatting with Progressive Propagation
Bayesian Averaging of Classifiers and the Overfitting Problem
Privacy Attacks on Image AutoRegressive Models
Using Genetic Search to Refine Knowledge-based Neural Networks
Doubly Stochastic Variational Bayes for non-Conjugate Inference
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning
Online Learning with Knapsacks: the Best of Both Worlds
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Diff-MoE: Diffusion Transformer with Time-Aware and Space-Adaptive Experts
Target-Aware Generative Augmentations for Single-Shot Adaptation
Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation
Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
Topological Point Cloud Clustering
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Nonparametric Involutive Markov Chain Monte Carlo
Convex Phase Retrieval without Lifting via PhaseMax
Proper Losses for Discrete Generative Models
Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Cross-view Masked Diffusion Transformers for Person Image Synthesis
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Finding k in Latent k- polytope
Quantized Decentralized Stochastic Learning over Directed Graphs
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
Understanding Inter-Concept Relationships in Concept-Based Models
Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning
Alternating Minimizations Converge to Second-Order Optimal Solutions
Two Routes to Scalable Credit Assignment without Weight Symmetry
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environments
Reinforcement Learning for Integer Programming: Learning to Cut
Discrepancy Minimization in Input-Sparsity Time
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
Gradient-free Online Learning in Continuous Games with Delayed Rewards
CaM: Cache Merging for Memory-efficient LLMs Inference
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Optimal transport mapping via input convex neural networks
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
Finding Relevant Information via a Discrete Fourier Expansion
Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models
Estimation and Quantization of Expected Persistence Diagrams
Learning Flat Latent Manifolds with VAEs
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction
Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning
Workshop summary: Numerical mathematics in machine learning
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling
Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
Stochastic methods for l 1 regularized loss minimization
Test-Time Degradation Adaptation for Open-Set Image Restoration
An Efficient Pruner for Large Language Model with Theoretical Guarantee
High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
The CLRS Algorithmic Reasoning Benchmark
A Bayesian nonparametric procedure for comparing algorithms
Improving Recognition Effectiveness of Noisy Texture Concepts
Hierarchical classification: combining Bayes with SVM
An Improved Algorithm for Incremental Induction of Decision Trees
On the Practicality of Deterministic Epistemic Uncertainty