yusuke1997/dblp-id-title huggingface dataset:
The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms
Towards Undistillable Models by Minimizing Conditional Mutual Information
FaAlGrad: Fairness through Alignment of Gradients across Different Subpopulations
Adversarial Surrogate Risk Bounds for Binary Classification
Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach
Nonparametric Learning of Two-Layer ReLU Residual Units
Neural Ordinary Differential Equations for Modeling Epidemic Spreading
The Missing U for Efficient Diffusion Models
Approximating 1-Wasserstein Distance with Trees
Node-Level Data Valuation on Graphs
Layer-diverse Negative Sampling for Graph Neural Networks
NuTime: Numerically Multi-Scaled Embedding for Large- Scale Time-Series Pretraining
A Lennard-Jones Layer for Distribution Normalization
Compositional Instruction Following with Language Models and Reinforcement Learning
Variational Autoencoding of Dental Point Clouds
DrGNN: Deep Residual Graph Neural Network with Contrastive Learning
Structured Uncertainty in the Observation Space of Variational Autoencoders
Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space
Meta Learning for Support Recovery of High-Dimensional Ising Models
ECG Representation Learning with Multi-Modal EHR Data
Stabilizing black-box model selection with the inflated argmax
Multitask Learning Can Improve Worst-Group Outcomes
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
Data Augmentation Policy Search for Long-Term Forecasting
MMD-Regularized Unbalanced Optimal Transport
Formulating Node Labelling as Node Classification or Link Prediction in Different Graph Representations
Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling
Double Machine Learning Based Structure Identification from Temporal Data
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
A Self-Representation Learning Method for Unsupervised Feature Selection using Feature Space Basis
Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
BM 2 : Coupled Schrödinger Bridge Matching
Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models
Towards Efficient Contrastive PAC Learning
Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities
Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models
Improving Adversarial Training for Two-player Competitive Games via Episodic Reward Engineering
Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction
Graph Knowledge Distillation to Mixture of Experts
SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis
Contextualize Me - The Case for Context in Reinforcement Learning
Learned feature representations are biased by complexity, learning order, position, and more
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks
Generalization Bounds with Logarithmic Negative-Sample Dependence for Adversarial Contrastive Learning
Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
APBench: A Unified Availability Poisoning Attack and Defenses Benchmark
Predicting Out-of-Domain Generalization with Neighborhood Invariance
Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models
Bandwidth Enables Generalization in Quantum Kernel Models
Reproducibility Study of "Languange-Image COnsistency"
Learning to Abstain From Uninformative Data
Distributed Multi-Agent Lifelong Learning
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion
Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence
Set Features for Anomaly Detection
Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning
SIESTA: Efficient Online Continual Learning with Sleep
Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis
Accelerated Training on Low-Power Edge Devices
Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality
Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics
Loss-to-Loss Prediction: Scaling Laws for All Datasets
Hybrid Active Learning with Uncertainty-Weighted Embeddings
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
On the Challenges and Opportunities in Generative AI
On Sparsity and Sub-Gaussianity in the Johnson- Lindenstrauss Lemma
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
A functional framework for nonsmooth autodiff with maxpooling functions
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
Deep Classifiers with Label Noise Modeling and Distance Awareness
Online Optimal Tracking of Linear Systems with Adversarial Disturbances
Language Models Speed Up Local Search for Finding Programmatic Policies
Learning from time-dependent streaming data with online stochastic algorithms
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Adaptive Resolution Residual Networks - Generalizing Across Resolutions Easily and Efficiently
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
Deep Double Descent via Smooth Interpolation
TextRegion: Text-Aligned Region Tokens from Frozen Image-Text Models
Multi-Attribute Constraint Satisfaction via Language Model Rewriting
TransFool: An Adversarial Attack against Neural Machine Translation Models
The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities
Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations
Lookahead Counterfactual Fairness
Offline Reinforcement Learning with Mixture of Deterministic Policies
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
Enhancing Molecular Conformer Generation via Fragment- Augmented Diffusion Pretraining
Holistic Molecular Representation Learning via Multi-view Fragmentation
Mean-Field RL for Large-Scale Unit-Capacity Pickup-and-Delivery Problems
Interpretable Node Representation with Attribute Decoding
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
Exploring Weak-to-Strong Generalization for CLIP-based Classification
Encoder-only Next Token Prediction
Semi-Supervised Semantic Segmentation via Marginal Contextual Information
Wasserstein Coreset via Sinkhorn Loss
3D Molecular Generation via Virtual Dynamics
Reward Distance Comparisons Under Transition Sparsity
NITO: Neural Implicit Fields for Resolution-free and Domain-Adaptable Topology Optimization
Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata
[Re] Reproducibility Study of "Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Design Editing for Offline Model-based Optimization
Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
Estimating Differential Equations from Temporal Point Processes
Supervised Knowledge May Hurt Novel Class Discovery Performance
How Does Code Pretraining Affect Language Model Task Performance?
Attribute Prediction as Multiple Instance Learning
Smoothed Differential Privacy
Reward Poisoning on Federated Reinforcement Learning
Hard-Negative Prototype-Based Regularization for Few-Shot Class-Incremental Learning
Language Models Are Better Than Humans at Next-token Prediction
CREW-Wildfire: Benchmarking Agentic Multi-Agent Collaborations at Scale
Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections
A Note On The Stability Of The Focal Loss
Online Continual Learning via Logit Adjusted Softmax
Graph Structure Learning with Interpretable Bayesian Neural Networks
Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter
Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey
INSPIRE: Incorporating Diverse Feature Preferences in Recourse
Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
Are Large Language Models Really Robust to Word-Level Perturbations?
Regularized Proportional Fairness Mechanism for Resource Allocation Without Money
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
For Robust Worst-Group Accuracy, Ignore Group Annotations
How far away are truly hyperparameter-free learning algorithms?
Accelerated Quality-Diversity through Massive Parallelism
Corrective Machine Unlearning
Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects
ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence
DIVINE: Diverse-Inconspicuous Feature Learning to Mitigate Abridge Learning
How good is Good-Turing for Markov samples?
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
An Adversarial Perspective on Machine Unlearning for AI Safety
Bags of Projected Nearest Neighbours: Competitors to Random Forests?
Reassessing Fairness: A Reproducibility Study of NIFA's Impact on GNN Models
LeanVec: Searching vectors faster by making them fit
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation
An Attribute-based Method for Video Anomaly Detection
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
Revisiting Active Learning in the Era of Vision Foundation Models
Sequential Best-Arm Identification with Application to P300 Speller
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks
Reproducibility study of FairAC
iHyperTime: Interpretable Time Series Generation with Implicit Neural Representations
A Dual-Perspective Approach to Evaluating Feature Attribution Methods
HandsOnVLM: Vision-Language Models for Hand-Object Interaction Prediction
Slicing the Gaussian Mixture Wasserstein Distance
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
On Convolutions, Intrinsic Dimension, and Diffusion Models
Unified Wisdom: Harnessing Collaborative Learning to Improve Efficacy of Knowledge Distillation
Time Series Alignment with Global Invariances
Simplifying Knowledge Transfer in Pretrained Models
Cooperative Online Learning with Feedback Graphs
Bayesian optimization with derivatives acceleration
C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning
Active Learning of Ordinal Embeddings: A User Study on Football Data
L-SVRG and L-Katyusha with Adaptive Sampling
Learning Graph Structure from Convolutional Mixtures
Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not
Learning Reward Machines from Partially Observed Policies
Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks
FoldDiff: Folding in Point Cloud Diffusion
Recall and Refine: A Simple but Effective Source-free Open- set Domain Adaptation Framework
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
Characterizing Vision Backbones for Dense Prediction with Dense Attentive Probing
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
DRDT3: Diffusion-Refined Decision Test-Time Training Model
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training
Graphon-Explainer: Generating Model-Level Explanations for Graph Neural Networks using Graphons
Graph Theory-Based Deep Graph Similarity Learning: A Unified Survey of Pipeline, Techniques, and Challenges
An Attentive Approach for Building Partial Reasoning Agents from Pixels
[RE] GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries
Deep Generalized Prediction Set Classifier and Its Theoretical Guarantees
Local Differential Privacy-Preserving Spectral Clustering for General Graphs
Conformal Prediction Intervals with Temporal Dependence
Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts
Efficient Open Set Single Image Test Time Adaptation of Vision Language Models
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits
Class-Discriminative Attention Maps for Vision Transformers
Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation
Momentum-Based Policy Gradient with Second-Order Information
CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion
COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
Characterizing the Convergence of Game Dynamics via Potentialness
Visual-Word Tokenizer: Beyond Fixed Sets of Tokens in Vision Transformers
Fairness Under Demographic Scarce Regime
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
Explanation Shift: How Did the Distribution Shift Impact the Model?
Finite-Time Analysis of Temporal Difference Learning with Experience Replay
Mixture Degree-Corrected Stochastic Block Model for Multi-Group Community Detection in Multiplex Graphs
LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling
Beyond Information Gain: An Empirical Benchmark for Low-Switching-Cost Reinforcement Learning
Constraining Generative Models for Engineering Design with Negative Data
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels
HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network
WaveBench: Benchmarking Data-driven Solvers for Linear Wave Propagation PDEs
Incremental Extractive Opinion Summarization Using Cover Trees
PerSEval: Assessing Personalization in Text Summarizers
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training
Masked multi-prediction for multi-aspect anomaly detection
Revisiting stochastic submodular maximization with cardinality constraint: A bandit perspective
On the Choice of Interpolation Scheme for Neural CDEs
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Gaussian-Smoothed Sliced Probability Divergences
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
Closing the gap between SVRG and TD-SVRG with Gradient Splitting
How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing
***FastDoc***: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomy
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities
[Re] Explaining Temporal Graph Models through an Explorer-Navigator Framework
Do Think Tags Really Help LLMs Plan? A Critical Evaluation of ReAct-Style Prompting
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images
Interpretable Mixture of Experts
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
Teacher-Guided Graph Contrastive Learning
VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment
SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions
Graph Pooling via Ricci Flow
MaskBit: Embedding-free Image Generation via Bit Tokens
Stealthy Backdoor Attack via Confidence-driven Sampling
Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning
Exponential Scaling of Factual Inconsistency in Data-to-Text Generation with Fine-Tuned LLMs
What's Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias
Vision-Language Instruction Tuning: A Review and Analysis
Reconciling Kaplan and Chinchilla Scaling Laws
Training Vision-Language Transformers from Captions
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications
On Perfect Clustering for Gaussian Processes
Understanding the learned look-ahead behavior of chess neural networks
Mini-Batch Optimization of Contrastive Loss
Time Series Domain Adaptation via Channel-Selective Representation Alignment
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Fail-Safe Adversarial Generative Imitation Learning
Correlation Clustering with Active Learning of Pairwise Similarities
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition
Hashing with Uncertainty Quantification via Sampling-based Hypothesis Testing
Cluster and Predict Latents Patches for Improved Masked Image Modeling
Differentially Private Image Classification from Features
Keep your distance: learning dispersed embeddings on $\mathbb{S}_{m}$
An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
Explaining Node Embeddings
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
LLaVA-Video: Video Instruction Tuning With Synthetic Data
Mind the Gap: Mitigating the Distribution Gap in Graph Few-shot Learning
Extreme Masking for Learning Instance and Distributed Visual Representations
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability
Improving Native CNN Robustness with Filter Frequency Regularization
On diffusion posterior sampling via sequential Monte Carlo for zero-shot scaffolding of protein motifs
TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement Learning
Bias Amplification Enhances Minority Group Performance
Multi-Output Distributional Fairness via Post-Processing
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Do better ImageNet classifiers assess perceptual similarity better?
Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization
NoiLin: Improving adversarial training and correcting stereotype of noisy labels
Joint Diffusion for Universal Hand-Object Grasp Generation
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
Contextual Combinatorial Multi-output GP Bandits with Group Constraints
Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning
Improving Predictor Reliability with Selective Recalibration
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments
Iterated Q -Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
Discovering Model Structure of Dynamical Systems with Combinatorial Bayesian Optimization
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
A Hierarchical Nearest Neighbour Approach to Contextual Bandits
Explaining Caption-Image Interactions in CLIP Models with Second-Order Attributions
A Variational Perspective on Generative Flow Networks
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
Addressing caveats of neural persistence with deep graph persistence
Is isotropy a good proxy for generalization in time series forecasting with transformers?
Label Embedding via Low-Coherence Matrices
Implicit Bias and Fast Convergence Rates for Self-attention
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
Vulnerability-Aware Instance Reweighting For Adversarial Training
DIGNet: Learning Decomposed Patterns in Representation Balancing for Treatment Effect Estimation
Mitigating Group Bias in Federated Learning: Beyond Local Fairness
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA
Conservative Prediction via Data-Driven Confidence Minimization
Are you using test log-likelihood correctly?
Fair Kernel Regression through Cross-Covariance Operators
Rotate the ReLU to Sparsify Deep Networks Implicitly
Learning to Switch Among Agents in a Team
AcademicEval: Live Long-Context LLM Benchmark
SuFP: Piecewise Bit Allocation Floating-Point for Robust Neural Network Quantization
k-NN as a Simple and Effective Estimator of Transferability
Revisiting Discrete Soft Actor-Critic
Enhancing Robustness to Class-Conditional Distribution Shift in Long-Tailed Recognition
The Score-Difference Flow for Implicit Generative Modeling
MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
Distributionally Robust Classification on a Data Budget
A Comprehensive Survey of Contamination Detection Methods in Large Language Models
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals