yusuke1997/dblp-id-title huggingface dataset:
Special Issue on Algorithms for Sequential Analysis
Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach
Finding All Solutions and Instances of Numberlink and Slitherlink by ZDDs
Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
A Finite Regime Analysis of Information Set Decoding Algorithms
How to Inspect and Measure Data Quality about Scientific Publications: Use Case of Wikipedia and CRIS Databases
A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model
Engine Optimization Model for Accurate Prediction of Friction Model in Marine Dual-Fuel Engine
WIRE: A Weighted Item Removal Method for Unsupervised Rank Aggregation
Infrared Image Deblurring Based on Lp-Pseudo-Norm and High-Order Overlapping Group Sparsity Regularization
Autonomous Intersection Management by Using Reinforcement Learning
Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations
A Systematic Review of Bio-Inspired Metaheuristic Optimization Algorithms: The Untapped Potential of Plant-Based Approaches
Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring
Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications
Design Optimization of Interfacing Attachments for the Deployable Wing of an Unmanned Re-Entry Vehicle
Adaptive Versioning in Transactional Memory Systems
Poroelastic Medium with Non-Penetrating Crack Driven by Hydraulic Fracture: FEM Approximation Using HHT-α and Semi-Smooth Newton Methods
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
Comparing the information content of probabilistic representation spaces
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
Phase-driven Generalizable Representation Learning for Nonstationary Time Series Classification
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models
A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches
Learning Using a Single Forward Pass
Choosing the parameter of the Fermat distance: navigating geometry and noise
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
Effective Latent Differential Equation Models via Attention and Multiple Shooting
What Makes ImageNet Look Unlike LAION
Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models
Semantic Mapping in Indoor Embodied AI - A Survey on Advances, Challenges, and Future Directions
Stacking Diverse Architectures to Improve Machine Translation
Model-free reinforcement learning with noisy actions for automated experimental control in optics
Diffusion on Graph: Augmentation of Graph Structure for Node Classification
A Survey on Out-of-Distribution Detection in NLP
Emergent Symbol-like Number Variables in Artificial Neural Networks
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters
Contrastive Attraction and Contrastive Repulsion for Representation Learning
NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities
Customizing Spider Silk: Generative Models with Mechanical Property Conditioning for Protein Engineering
How to Upscale Neural Networks with Scaling Law?
Differentially Private Optimizers Can Learn Adversarially Robust Models
Deep Unlearning: Fast and Efficient Gradient-free Class Forgetting
Decentralized Decoupled Training for Federated Long-Tailed Learning
Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners
Personalized Algorithmic Recourse with Preference Elicitation
On the Optimization and Generalization of Multi-head Attention
Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs
How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning
Dataset Distillation via Curriculum Data Synthesis in Large Data Era
Multiplayer Information Asymmetric Contextual Bandits
Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows
Coresets from Trajectories: Selecting Data via Correlation of Loss Differences
Understanding LLM Embeddings for Regression
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
Unimodal Likelihood Models for Ordinal Data
Test-Time Adaptation for Visual Document Understanding
Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry
Reinforcement Teaching
Defending Against Unforeseen Failure Modes with Latent Adversarial Training
Streamlining Language Models via Semantic Basis Analysis
Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection
Overcoming the Stability Gap in Continual Learning
Risk-controlling Prediction with Distributionally Robust Optimization
Sample-efficient decoding of visual stimuli from fMRI through inter-individual functional alignment
Incorporating Sum Constraints into Multitask Gaussian Processes
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization
ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting
Diversity-Enhanced and Classification-Aware Prompt Learning for Few-Shot Learning via Stable Diffusion
RECLIP: Resource-efficient CLIP by Training with Small Images
Dropped Scheduled Task: Mitigating Negative Transfer in Multi-task Learning using Dynamic Task Dropping
Comparative Generalization Bounds for Deep Neural Networks
Differentially Private Stochastic Expectation Propagation
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
Where to Intervene: Action Selection in Deep Reinforcement Learning
Variational Causal Dynamics: Discovering Modular World Models from Interventions
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Label Smoothing is a Pragmatic Information Bottleneck
Group Fairness in Reinforcement Learning
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off
Assessing Robustness via Score-Based Adversarial Image Generation
Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory
Discrete Audio Tokens: More Than a Survey!
Latent State Models of Training Dynamics
Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding
Empirical Bayes Trend Filtering Through a Variational Inference Framework
On the Adversarial Robustness of Camera-based 3D Object Detection
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
Solving Nonconvex-Nonconcave Min-Max Problems exhibiting Weak Minty Solutions
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
A Max-Min Approach to the Worst-Case Class Separation Problem
Adversarial Imitation Learning from Visual Observations using Latent Information
A Vector Bernstein Inequality for Self-Normalized Martingales
On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces
Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience
Clustering-Based Validation Splits for Model Selection under Domain Shift
Hard Work Does Not Always Pay Off: On the Robustness of NAS to Data Poisoning
Fairness with respect to Stereotype Predictors: Impossibilities and Best Practices
Differentially Private Diffusion Models
Temporally Rich Deep Learning Models for Magnetoencephalography
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
Voyager: An Open-Ended Embodied Agent with Large Language Models
Monotone deep Boltzmann machines
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks
Contrastive Learning with Consistent Representations
Image Reconstruction via Deep Image Prior Subspaces
UniCtrl: Improving the Spatiotemporal Consistency of Text-to-Video Diffusion Models via Training-Free Unified Attention Control
A reproducibility study of "User-item fairness tradeoffs in recommendations"
PAVI: Plate-Amortized Variational Inference
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Differentially Private Clustered Federated Learning
Replay-enhanced Continual Reinforcement Learning
Overcoming Order in Autoregressive Graph Generation for Molecule Generation
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
Successor Clusters: A Behavior Basis for Unsupervised Zero-Shot Reinforcement Learning
Group Fairness in Reinforcement Learning via Multi-Objective Rewards
Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
U-NO: U-shaped Neural Operators
Text-to-Image Generation Via Energy-Based CLIP
Stability and Generalization in Free Adversarial Training
Generalizability of Adversarial Robustness Under Distribution Shifts
Conservative Evaluation of Offline Policy Learning
Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors
[Re] Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty
The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design
Convergence Analysis of Fractional Gradient Descent
Model Guidance via Robust Feature Attribution
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration
Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations
Learning to Incentivize Improvements from Strategic Agents
Bayesian Methods for Constraint Inference in Reinforcement Learning
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
A Multilinear Least-Squares Formulation for Sparse Tensor Canonical Correlation Analysis
Does Representation Similarity Capture Function Similarity?
Projected Randomized Smoothing for Certified Adversarial Robustness
Transformer for Partial Differential Equations' Operator Learning
Latent mixed-effect models for high-dimensional longitudinal data
Distributed Architecture Search Over Heterogeneous Distributions
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Neighborhood Gradient Mean: An Efficient Decentralized Learning Method for Non-IID Data
Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective
Where are we with calibration under dataset shift in image classification?
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space
Understanding convolution on graphs via energies
Foundation Models Meet Federated Learning: A One-shot Feature-sharing Method with Privacy and Performance Guarantees
Domain-Generalizable Multiple-Domain Clustering
No Identity, no problem: Motion through detection for people tracking
GenOL: Generating Diverse Examples for Name-only Online Learning
ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models
Universal Functional Regression with Neural Operator Flows
One by One, Continual Coordinating with Humans via Hyper-Teammate Identification
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Improving GFlowNets for Text-to-Image Diffusion Alignment
Prior Specification for Exposure-based Bayesian Matrix Factorization
Low-rank Momentum Factorization for Memory Efficient Training
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models
Reinforcement Learning from Bagged Reward
PRIMO: Private Regression in Multiple Outcomes
A Practical Investigation of Spatially-Controlled Image Generation with Transformers
Autoregressive Models in Vision: A Survey
Entropy-Regularized Process Reward Model
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations
UnIVAL: Unified Model for Image, Video, Audio and Language Tasks
Solving the Cold Start Problem on One's Own as an End User via Preference Transfer
Bounding generalization error with input compression: An empirical study with infinite-width networks
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks
Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models
Stabilizing the Kumaraswamy Distribution
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing
When low-vision task meets dense prediction tasks with less data: an auxiliary self-trained geometry regularization
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach
A Bag of Tricks for Few-Shot Class-Incremental Learning
Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models
Client-only Distributed Markov Chain Monte Carlo Sampling over a Network
Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation
Graph Cuts with Arbitrary Size Constraints Through Optimal Transport
Decoding-based Regression
Defense Against Reward Poisoning Attacks in Reinforcement Learning
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
Calibrating Deep Ensemble through Functional Variational Inference
ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Physics Informed Distillation for Diffusion Models
On the Importance of Uncertainty in Decision-Making with Large Language Models
A Bias Correction Mechanism for Distributed Asynchronous Optimization
Neural networks can be FLOP-efficient integrators of 1D oscillatory integrands
Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
A Proximal Algorithm for Sampling
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
Wrapped β-Gaussians with compact support for exact probabilistic modeling on manifolds
Memorisation in Machine Learning: A Survey of Results
AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm
SolidGen: An Autoregressive Model for Direct B-rep Synthesis
Stochastic Block Model-Aware Topological Neural Networks for Graph Link Prediction
What Matters for Model Merging at Scale?
Revisiting Discover-then-Name Concept Bottleneck Models: A Reproducibility Study
Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty
Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure
Heterophily-informed Message Passing
Learning Deformable Body Interactions With Adaptive Spatial Tokenization
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
Disentangled Embedding through Style and Mutual Information for Domain Generalization
CoCa: Contrastive Captioners are Image-Text Foundation Models
UniZero: Generalized and Efficient Planning with Scalable Latent World Models
LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation
Zero-shot CLIP Class Forgetting via Text-image Space Adaptation
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits
SaFARi: State-Space Models for Frame-Agnostic Representation
Reinforcement Learning for Causal Discovery without Acyclicity Constraints
Improved Convergence of Score-Based Diffusion Models via Prediction-Correction
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
On the stability of gradient descent with second order dynamics for time-varying cost functions
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Long Short-Term Imputer: Handling Consecutive Missing Values in Time Series
Continual Adaptation of Vision Transformers for Federated Learning
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse
AEAP: A Reinforcement Learning Actor Ensemble Algorithm with Adaptive Pruning
T2L: Efficient Zero-Shot Action Recognition with Temporal Token Learning
Exploring Generative Neural Temporal Point Process
Mamba State-Space Models Are Lyapunov-Stable Learners
Variational Learning ISTA
Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture
ModernTCN Revisited: A Critical Look at the Experimental Setup in General Time Series Analysis
Feudal Graph Reinforcement Learning
Uncovering Strong Lottery Tickets in Graph Transformers: A Path to Memory Efficient and Robust Graph Learning
Diversity-Preserving K-Armed Bandits, Revisited
Cycle Conditioning for Robust Representation Learning from Categorical Data
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Text to Stealthy Adversarial Face Masks
∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data
CLIP-QDA: An Explainable Concept Bottleneck Model
Augmented Invertible Koopman Autoencoder for long-term time series forecasting
Meta-learning Optimizers for Communication-Efficient Learning
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Class-wise Generalization Error: an Information-Theoretic analysis
Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
Masked Capsule Autoencoders
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors
A probabilistic Taylor expansion with Gaussian processes
Enhancing Plaque Segmentation in CCTA with Prompt- based Diffusion Data Augmentation
DiffNat : Exploiting the Kurtosis Concentration Property for Image quality improvement
Graph-based Confidence Calibration for Large Language Models
Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control
Equivalent Linear Mappings of Large Language Models
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons
VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming
Efficient Hardware Scaling and Diminishing Returns in Large-Scale Training of Language Models
Generalized Prediction Set with Bandit Feedback
Fully Automatic Neural Network Reduction for Formal Verification
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer
Identifying Macro Causal Effects in a C-DMG over ADMGs
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
How Can Knowledge of a Task's Modular Structure Improve Generalization and Training Efficiency?
Controllable Generative Modeling via Causal Reasoning
Can Masked Autoencoders Also Listen to Birds?
From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Relating graph auto-encoders to linear models
Gated Domain Units for Multi-source Domain Generalization
SynCode: LLM Generation with Grammar Augmentation
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
Bayesian Quadrature for Neural Ensemble Search
RIFLE: Imputation and Robust Inference from Low Order Marginals
BP(λ): Online Learning via Synthetic Gradients
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Improving Text-to-Image Consistency via Automatic Prompt Optimization
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning
On the Properties and Estimation of Pointwise Mutual Information Profiles
Uniform Noise Distribution and Compact Clusters: Unveiling the Success of Self-Supervised Learning in Label Noise
Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives
Certified Robustness to Data Poisoning in Gradient-Based Training
A density estimation perspective on learning from pairwise human preferences
Adaptive Incentive Design for Markov Decision Processes with Unknown Rewards
LLM-Select: Feature Selection with Large Language Models
Adam-family Methods with Decoupled Weight Decay in Deep Learning
A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection
Offline Reinforcement Learning with Additional Covering Distributions
Optimistic Optimization of Gaussian Process Samples
Fooling Contrastive Language-Image Pre-Trained Models with CLIPMasterPrints
ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Rethinking Knowledge Transfer in Learning Using Privileged Information
Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions
Uncertainty-Based Experience Replay for Task-Agnostic Continual Reinforcement Learning
Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules
Enhancing Compositional Generalization via Compositional Feature Alignment
Evaluating Human-Language Model Interaction
Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning