yusuke1997/dblp-id-title huggingface dataset:
Semantic-Syntactic Discrepancy in Images (SSDI): Learning Meaning and Order of Features from Natural Images
Mace: A flexible framework for membership privacy estimation in generative models
Direct Molecular Conformation Generation
Efficient CDF Approximations for Normalizing Flows
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
From Differential Privacy to Bounds on Membership Inference: Less can be More
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
Higher Order Transformers With Kronecker-Structured Attention
Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction
[Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games
Online Min-max Problems with Non-convexity and Non-stationarity
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Node Embeddings via Neighbor Embeddings
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
Fairness and Disentanglement: A Critical Review of Predominant Bias in Neural Networks
Latent Space Energy-based Neural ODEs
Independence Testing for Temporal Data
Learning Counterfactually Invariant Predictors
Dynamics of the accelerated t-SNE
Planning with Consistency Models for Model-Based Offline Reinforcement Learning
Almost Sure Convergence of Stochastic Gradient Methods under Gradient Domination
Threshold Moving for Online Class Imbalance Learning with Dynamic Evolutionary Cost Vector
Probabilistic neural operators for functional uncertainty quantification
GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
Physics-Aware Spatiotemporal Causal Graph Network for Forecasting with Limited Data
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
On Averaging ROC Curves
Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation
Visual Prompt Based Personalized Federated Learning
Rethinking the Value of Training-Free Structured Pruning of LLMs
Latent Trajectory: A New Framework for Deep Actor-Critic Reinforcement Learning with Uncertainty Quantification
On the theoretical limit of gradient descent for Simple Recurrent Neural Networks with finite precision
Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition
Enhancing Diversity in Text-to-Image Generation without Compromising Fidelity
Decomposed Direct Preference Optimization for Structure-Based Drug Design
Greedier is Better: Selecting Multiple Neighbors per Iteration for Sparse Subspace Clustering
TRA: Better Length Generalisation with Threshold Relative Attention
A Systematic Evaluation of the Planning and Scheduling Abilities of the Reasoning Model o1
Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance
Lyra: Orchestrating Dual Correction in Automated Theorem Proving
DINOv2: Learning Robust Visual Features without Supervision
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
LASP: Linear Attention Sequence Parallelism
GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision
AdaFlood: Adaptive Flood Regularization
Importance Weighting for Aligning Language Models under Deployment Distribution Shift
Unlearning Personal Data from a Single Image
Improved seeding strategies for k-means and k-GMM
Sparse-to-Sparse Training of Diffusion Models
Machine Learning with Physics Knowledge for Prediction: A Survey
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
©Plug-in Authorization for Human Copyright Protection in Text-to-Image Model
Mathematical Characterization of Better-than-Random Multiclass Models
Robust and Efficient Quantization-aware Training via Coreset Selection
On a continuous time model of gradient descent dynamics and instability in deep learning
SANTA: Source Anchoring Network and Target Alignment for Continual Test Time Adaptation
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Budgeted-Bandits with Controlled Restarts with Applications in Learning and Computing
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models
TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding
GIT: A Generative Image-to-text Transformer for Vision and Language
Multi-Modal Foundation Models for Computational Pathology: A Survey
Simple and Scalable Strategies to Continually Pre-train Large Language Models
AGALE: A Graph-Aware Continual Learning Evaluation Framework
BIGRoC: Boosting Image Generation via a Robust Classifier
D2 Actor Critic: Diffusion Actor Meets Distributional Critic
Application of Bagged Copula-GP: Confirming Neural Dependency on Pupil Dilation
Partition-Based Active Learning for Graph Neural Networks
Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models
Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
Variational excess risk bound for general state space models
Say My Name: a Model's Bias Discovery Framework
Exact Recovery Guarantees for Parameterized Nonlinear System Identification Problem under Sparse Disturbances or Semi-Oblivious Attacks
Clustering using Approximate Nearest Neighbour Oracles
Pathologies of Predictive Diversity in Deep Ensembles
Revealing an Overlooked Challenge in Class-Incremental Graph Learning
BBCaL: Black-box Backdoor Detection under the Causality Lens
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond
Revisiting Feature Prediction for Learning Visual Representations from Video
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data
New Evaluation Metrics Capture Quality Degradation due to LLM Watermarking
Bayesian Computation Meets Topology
Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies
Limitation of Characterizing Implicit Regularization by Data-independent Functions
Uncovering Unique Concept Vectors through Latent Space Decomposition
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
Stable and Interpretable Unrolled Dictionary Learning
LEGO-Learn: Label-Efficient Graph Open-Set Learning
Conditional Permutation Invariant Flows
Learning to reconstruct signals from binary measurements alone
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning
Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction
Efficient Training of Multi-task Neural Solver for Combinatorial Optimization
Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations
Attending to Graph Transformers
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
Manifold Contrastive Learning with Variational Lie Group Operators
Reproducibility Study of "Improving Interpretation Faithfulness For Vision Transformers"
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Reset-free Reinforcement Learning with World Models
Blockwise Self-Supervised Learning at Scale
On the Interdependence between Data Selection and Architecture Optimization in Deep Active Learning
EHRDiff : Exploring Realistic EHR Synthesis with Diffusion Models
Learning Equivalence Classes of Bayesian Network Structures with GFlowNet
Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Evaluation of Best-of-N Sampling Strategies for Language Model Alignment
Invariance & Causal Representation Learning: Prospects and Limitations
Optimization and Generalization Guarantees for Weight Normalization
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study
Forecasting Company Fundamentals
Identifying Spurious Correlations using Counterfactual Alignment
Robust Preference Optimization through Reward Model Distillation
IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks
Equivariant Mesh Attention Networks
Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel
Mixed Sparsity Training: Achieving 4× FLOP Reduction for Transformer Pretraining
Solving Multi-agent Path Finding as an LLM Benchmark: How, How Good and Why
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models
Rational Tuning of LLM Cascades via Probabilistic Modeling
Improved identification accuracy in equation learning via comprehensive R2-elimination and Bayesian model selection
Beyond Loss Functions: Exploring Data-Centric Approaches with Diffusion Model for Domain Generalization
Low Compute Unlearning via Sparse Representations
Continual Learning by Modeling Intra-Class Variation
FedDAG: Federated DAG Structure Learning
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
Proximal Mean Field Learning in Shallow Neural Networks
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning
Partial Optimal Transport for Support Subset Selection
Loss Landscape Degeneracy and Stagewise Development in Transformers
Benchmarking Progress to Infant-Level Physical Reasoning in AI
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
Language Models Are Good Tabular Learners
ADAPT to Robustify Prompt Tuning Vision Transformers
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning
Coordinate Transform Fourier Neural Operators for Symmetries in Physical Modelings
M3CoL: Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification
Concept Siever : Towards Controllable Erasure of Concepts from Diffusion Models without Side-effect
Contrastive Search Is What You Need For Neural Text Generation
Explaining Bayesian Neural Networks
Audio-Visual Dataset Distillation
Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
Federated Learning under Partially Disjoint Data via Manifold Reshaping
Deep End-to-end Causal Inference
LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
Regret Bounds for Noise-Free Cascaded Kernelized Bandits
Dynamic Structure Estimation from Bandit Feedback using Nonvanishing Exponential Sums
Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment
How iteration composition influences convergence and stability in deep learning
Don't Judge Before You CLIP: A Unified Approach for Perceptual Tasks
A Probabilistic Model behind Self- Supervised Learning
Learning Is a Kan Extension
Sparse, Efficient and Explainable Data Attribution with DualXDA
Flexible Infinite-Width Graph Convolutional Neural Networks
Set-Based Training for Neural Network Verification
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Superposition as Lossy Compression - Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability
Tight conditions for when the NTK approximation is valid
Evaluating Long Range Dependency Handling in Code Generation LLMs
Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision
Global Contrastive Learning for Long-Tailed Classification
Recurrent Natural Policy Gradient for POMDPs
Self-Supervision is All You Need for Solving Rubik's Cube
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning
Bayesian Causal Bandits with Backdoor Adjustment Prior
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
Concept-Driven Continual Learning
Does equivariance matter at scale?
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
Universal Neurons in GPT2 Language Models
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
PixelWorld: Towards Perceiving Everything as Pixels
Robust Multi-Agent Reinforcement Learning with State Uncertainty
Federated Learning with Reduced Information Leakage and Computation
Efficient Knowledge Injection in LLMs via Self-Distillation
Return-Aligned Decision Transformer
A Note on the Convergence of Denoising Diffusion Probabilistic Models
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation
[Re] Classwise-Shapley values for data valuation
Closed-Form Diffusion Models
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning
Making Reliable and Flexible Decisions in Long-tailed Classification
Active Diffusion Subsampling
An analysis of the noise schedule for score-based generative models
Open Problems in Technical AI Governance
Amortized Bayesian Decision Making for simulation-based models
A Rigorous Study Of The Deep Taylor Decomposition
Undetectable Steganography for Language Models
Online model selection by learning how compositional kernels evolve
SCNode: Spatial and Contextual Coordinates for Graph Representation Learning
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations
Using unsupervised learning to detect broken symmetries, with relevance to searches for parity violation in nature
If your data distribution shifts, use self-learning
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
Controlled Model Debiasing through Minimal and Interpretable Updates
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
Setting the Record Straight on Transformer Oversmoothing
Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation
The Cross-entropy of Piecewise Linear Probability Density Functions
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
RESTOR: Knowledge Recovery in Machine Unlearning
Towards LifeSpan Cognitive Systems
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric
Prior Learning in Introspective VAEs
ABC: Achieving Better Control of Visual Embeddings using VLLMs
Sparsity regularization via tree-structured environments for disentangled representations
Physical Reasoning and Object Planning for Household Embodied Agents
Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models
Learning Robust Representations for Visual Reinforcement Learning via Task-Relevant Mask Sampling
Federated High-Dimensional Online Decision Making
Feature Distillation Improves Zero-Shot Transfer from Synthetic Images
νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints
A Survey of State Representation Learning for Deep Reinforcement Learning
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Strategies for Pretraining Neural Operators
Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
Costs and Benefits of Fair Regression
Streaming Heteroscedastic Probabilistic PCA with Missing Data
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
Tree Search for Language Model Agents
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
Learning to Rank Features to Enhance Graph Neural Networks for Graph Classification
World Models via Policy-Guided Trajectory Diffusion
Multi-annotator Deep Learning: A Probabilistic Framework for Classification
When Are Bias-Free ReLU Networks Effectively Linear Networks?
ResiDual Transformer Alignment with Spectral Decomposition
A Unified Hallucination Mitigation Framework for Large Vision-Language Models
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning
VideoGLUE: Video General Understanding Evaluation of Foundation Models
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Optimization with Access to Auxiliary Information
Dual Caption Preference Optimization for Diffusion Models
Identifying latent distances with Finslerian geometry
Can AI-Generated Text be Reliably Detected? Stress Testing AI Text Detectors Under Various Attacks
Identification of Average Outcome under Interventions in Confounded Additive Noise Models
Geometry-Aware visualization of high dimensional Symmetric Positive Definite matrices
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
Generating Symbolic World Models via Test-time Scaling of Large Language Models
Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
Data Leakage in Federated Averaging
Persona-aware Generative Model for Code-mixed Language
Training with Mixed-Precision Floating-Point Assignments
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
InvariantStock: Learning Invariant Features for Mastering the Shifting Market
Federated Minimax Optimization with Client Heterogeneity
Learned-Database Systems Security
DynaConF: Dynamic Forecasting of Non-Stationary Time Series
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
Thoughts and Lessons on Using Visual Foundation Models for Manipulation
Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter?
InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write
Learning Representations for Pixel-based Control: What Matters and Why?
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
Evaluating explainability techniques on discrete-time graph neural networks
Predicting sub-population specific viral evolution
Outcome-based Reinforcement Learning to Predict the Future
Sequential Query Encoding for Complex Query Answering on Knowledge Graphs
Cometh: A continuous-time discrete-state graph diffusion model
Non-Uniform Smoothness for Gradient Descent
Enabling Users to Falsify Deepfake Attacks
Proximal Policy Distillation
Weight-balancing fixes and flows for deep learning
Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Convergence of denoising diffusion models under the manifold hypothesis
Private Fine-tuning of Large Language Models with Zeroth-order Optimization
ReDistill: Residual Encoded Distillation for Peak Memory Reduction of CNNs
Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Explaining Confident Black-Box Predictions
Self-Supervised Graph Representation Learning for Neuronal Morphologies
Large Action Models: From Inception to Implementation
FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation
A Study of the Effects of Transfer Learning on Adversarial Robustness
Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models
Data Matters Most: Auditing Social Bias in Contrastive Vision-Language Models
Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks
Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization
Tree Ensembles for Contextual Bandits
Gradient Inversion Attack on Graph Neural Networks
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
Scaling and Distilling Transformer Models for sEMG
Robust Guided Diffusion for Offline Black-Box Optimization
A stochastic gradient descent algorithm with random search directions
Exploring the Learning Mechanisms of Neural Division Modules
Degradation Attacks on Certifiably Robust Neural Networks
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
Sparse-Input Neural Network using Group Concave Regularization
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
Linear Bandits with Memory
Long-context LLMs Struggle with Long In-context Learning
Mixture of Experts for Image Classification: What's the Sweet Spot?
Goal Recognition Design for General Behavioral Agents using Machine Learning
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Introspective Experience Replay: Look Back When Surprised
Enhanced gradient-based MCMC in discrete spaces
Counterfactual Learning with Multioutput Deep Kernels
An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces
Oblique Bayesian Additive Regression Trees
Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs
Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models
TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm
The Choice of Normalization Influences Shrinkage in Regularized Regression
Discriminative reconstruction via simultaneous dense and sparse coding
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space
Data-Dependent Generalization Bounds for Neural Networks with ReLU
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
Vision-Language Dataset Distillation
M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations
Generative Proto-Sequence: Sequence-Level Decision Making for Long-Horizon Reinforcement Learning