yusuke1997/dblp-id-title huggingface dataset:
I Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy
Decoding EEG With Spiking Neural Networks on Neuromorphic Hardware
DSI2I: Dense Style for Unpaired Exemplar-based Image-to- Image Translation
CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder
Meta-Learning via Classifier(-free) Diffusion Guidance
Enabling Automatic Differentiation with Mollified Graph Neural Operators
SelfEval: Leveraging discriminative nature of generative models for evaluation
Meta-Learning to Teach Semantic Prompts for Open Domain Generalization in Vision-Language Models
Graph Personalized Federated Learning via Client Network Learning
ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control
An Empirical Study of the Accuracy-Robustness Trade-off and Training Efficiency in Robust Self-Supervised Learning
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
Decomposing The Dark Matter of Sparse Autoencoders
Transferring Reasoning Capabilities between LLMs operating via Curriculum Learning Policy
From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford Algebra and Convexity
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models
Quantile Activation: Correcting a failure mode of traditional ML models
Separability Analysis for Causal Discovery in Mixture of DAGs
Full-Rank Unsupervised Node Embeddings for Directed Graphs via Message Aggregation
Appropriate Balance of Diversification and Intensification Improves Performance and Efficiency of Adversarial Attacks
Pitfalls in Evaluating Inference-time Methods for Improving LLM Reliability
Batch Training for Streaming Time Series: A Transferable Augmentation Framework to Combat Distribution Shifts
Momentum Capsule Networks
Decoder Denoising Pretraining for Semantic Segmentation
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models
FORTRESS: Fast, Tuning-Free Retrieval Ensemble for Scalable LLM Safety
The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging
Unveiling Multiple Descents in Unsupervised Autoencoders
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach
Robustness through Data Augmentation Loss Consistency
Causal Reinforcement Learning: A Survey
Why should autoencoders work?
Privacy Preserving Reinforcement Learning for Population Processes
Multi-Accurate CATE is Robust to Unknown Covariate Shifts
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity
Improved Overparametrization Bounds for Global Convergence of SGD for Shallow Neural Networks
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices
Meta-Learning under Task Shift
Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
Uncertainty-aware Reward Design Process
Coreset-Driven Re-Labeling: Tackling Noisy Annotations with Noise-Free Gradients
A Revenue Function for Comparison-Based Hierarchical Clustering
Understanding AdamW through Proximal Methods and Scale-Freeness
Unsupervised Discovery and Composition of Object Light Fields
Learning a Decision Tree Algorithm with Transformers
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
Solution Augmentation for ARC Problems Using GFlowNet: A Probabilistic Exploration Approach
Referential communication in heterogeneous communities of pre-trained visual deep networks
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation
NeoBERT: A Next Generation BERT
DTRNet: Precisely Correcting Selection Bias in Individual-Level Continuous Treatment Effect Estimation by Reweighted Disentangled Representation
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks
LEA: Learning Latent Embedding Alignment Model for fMRI Decoding and Encoding
MESSI: A Multi-Elevation Semantic Segmentation Image Dataset of an Urban Environment
Dataset Condensation with Color Compensation
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games
Link Prediction with Relational Hypergraphs
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Comparing Deterministic and Soft Policy Gradients for Optimizing Gaussian Mixture Actors
VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning
Analyzing the Impact of Learnable Softmax Temperature in Contrastive Visual-Textual Alignment Systems: Benefits, Drawbacks, and Alternative Approaches
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Directional Convergence Near Small Initializations and Saddles in Two-Homogeneous Neural Networks
Attacking Perceptual Similarity Metrics
On the infinite-depth limit of finite-width neural networks
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
A Survey on Large Language Model Acceleration based on KV Cache Management
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Graph Neural Networks Designed for Different Graph Types: A Survey
The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation
DSpar: An Embarrassingly Simple Strategy for Efficient GNN training and inference via Degree-based Sparsification
Sparse Decomposition of Graph Neural Networks
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Scaling Vision-and-Language Navigation With Offline RL
Joint Generative Modeling of Grounded Scene Graphs and Images via Diffusion Models
Black-Box Batch Active Learning for Regression
Are Population Graphs Really as Powerful as Believed?
TSMixer: An All-MLP Architecture for Time Series Forecast-ing
An Analytical Model for Overparameterized Learning Under Class Imbalance
Estimating the Event-Related Potential from Few EEG Trials
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization
Targeted Active Learning for Bayesian Decision-Making
Weight Expansion: A New Perspective on Dropout and Generalization
Piecewise-Stationary Dueling Bandits
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection
Attentive Walk-Aggregating Graph Neural Networks
Stochastic Douglas-Rachford Splitting for Regularized Empirical Risk Minimization: Convergence, Mini-batch, and Implementation
On the Dual Problem of Convexified Convolutional Neural Networks
Fast Training of Diffusion Models with Masked Transformers
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
UMP-Net: Uncertainty-Aware Mixture of Prompts Network for Efficient Instruction Tuning
Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing
Target Propagation via Regularized Inversion for Recurrent Neural Networks
COMMA: A Communicative Multimodal Multi-Agent Benchmark
DP-LFlow: Differentially Private Latent Flow for Scalable Sensitive Image Generation
Time-Uniform Confidence Spheres for Means of Random Vectors
A Stochastic Optimization Framework for Fair Risk Minimization
How does over-squashing affect the power of GNNs?
A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Directed Graph Generation with Heat Kernels
Aggregating Algorithm and Axiomatic Loss Aggregation
Emergent representations in networks trained with the Forward-Forward algorithm
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Calibrated Selective Classification
XPL: A Cross-Model framework for Semi-Supervised Prompt Learning in Vision-Language Models
Can Kernel Methods Explain How the Data Affects Neural Collapse?
sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification
Interpretable LLM-based Table Question Answering
Large-width asymptotics and training dynamics of α-Stable ReLU neural networks
Oscillations Make Neural Networks Robust to Quantization
Learning the Transformer Kernel
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Change Point Detection in the Frequency Domain with Statistical Reliability
YoooP: You Only Optimize One Prototype per Class for Non-Exemplar Incremental Learning
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
An Analysis of Model Robustness across Concurrent Distribution Shifts
SelfXit: An Unsupervised Early Exit Mechanism for Deep Neural Networks
Neural Networks beyond explainability: Selective inference for sequence motifs
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
Flow-Attentional Graph Neural Networks
BELLA: Black-box model Explanations by Local Linear Approximations
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Preference Discerning with LLM-Enhanced Generative Retrieval
Step-Controlled DPO: Leveraging Stepwise Errors for Enhancing Mathematical Reasoning of Language Models
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks
Using Motion Cues to Supervise Single-frame Body Pose & Shape Estimation in Low Data Regimes
Flow map matching with stochastic interpolants: A mathematical framework for consistency models
Mixed Nash for Robust Federated Learning
Minimax Lower Bounds for Estimating Distributions on Low-dimensional Spaces
CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark
Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
A Note on "Assessing Generalization of SGD via Disagreement"
Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior
FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
Noise-free Loss Gradients: A Surprisingly Effective Baseline for Coreset Selection
Angular Regularization for Positive-Unlabeled Learning on the Hypersphere
A Theoretical Framework for Zeroth-Order Budget Convex Optimization
Guided Discrete Diffusion for Electronic Health Record Generation
Local Distribution-Based Adaptive Oversampling for Imbalanced Regression
Inverse Kernel Decomposition
Gradient-guided discrete walk-jump sampling for biological sequence generation
An Embedding is Worth a Thousand Noisy Labels
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization?
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction
Dynamic Schwartz-Fourier Neural Operator for Enhanced Expressive Power
Interpretable Additive Tabular Transformer Networks
Event-Triggered Time-Varying Bayesian Optimization
Domain-invariant Feature Exploration for Domain Generalization
On the Reproducibility of: "Learning Perturbations to Explain Time Series Predictions"
Intriguing Properties of Hyperbolic Embeddings in Vision-Language Models
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Scale Equalization for Multi-Level Feature Fusion
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness
Biased Dueling Bandits with Stochastic Delayed Feedback
Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions
Equivariant MuZero
Spectral Self-supervised Feature Selection
IndicFake Meets SAFARI-LLM: Unifying Semantic and Acoustic Intelligence for Multilingual Deepfake Detection
Optimal Transport for Domain Adaptation through Gaussian Mixture Models
GSR: A Generalized Symbolic Regression Approach
PriViT: Vision Transformers for Private Inference
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices
Signed Graph Neural Networks: A Frequency Perspective
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive Discretization
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence
Local Function Complexity for Active Learning via Mixture of Gaussian Processes
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks
Expressive Pooling for Graph Neural Networks
Gaussian mixture layers for neural networks
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors
The Robustness Limits of SoTA Vision Models to Natural Variation
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
A Simulation Environment and Reinforcement Learning Method for Waste Reduction
APR-CNN: Convolutional Neural Networks for the Adaptive Particle Representation of Large Microscopy Images
Tracing Facts or just Copies? A critical investigation of the Competitions of Mechanisms in Large Language Models
LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration
MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models
The Unreasonable Effectiveness of Gaussian Score Approximation for Diffusion Models and its Applications
An Efficient One-Class SVM for Novelty Detection in IoT
Calibrating and Improving Graph Contrastive Learning
Gradient Scarcity in Graph Learning with Bilevel Optimization
Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments
On the Choice of Learning Rate for Local SGD
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
Understanding Class Bias Amplification in Graph Representation Learning
Holistic Evaluation of Language Models
Combating Inter-Task Confusion and Catastrophic Forgetting by Metric Learning and Re-Using a Past Trained Model
On the numerical reliability of nonsmooth autodiff: a MaxPool case study
Hyperspherical Prototype Node Clustering
Image retrieval outperforms diffusion models on data augmentation
PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling
Benchmarks and Algorithms for Offline Preference-Based Reward Learning
Policy Optimization via Adv2: Adversarial Learning on Advantage Functions
Zero-Order One-Point Gradient Estimate in Consensus-Based Distributed Stochastic Optimization
GUARD: A Safe Reinforcement Learning Benchmark
Recent Link Classification on Temporal Graphs Using Graph Profiler
Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary
Causally-guided Regularization of Graph Attention Improves Generalizability
Tighter sparse variational Gaussian processes
An approximate sampler for energy-based models with divergence diagnostics
Learning k -Level Structured Sparse Neural Networks Using Group Envelope Regularization
Memory-Modular Classification: Learning to Generalize with Memory Replacement
Posterior Sampling for Reinforcement Learning on Graphs
Cross Entropy versus Label Smoothing: A Neural Collapse Perspective
Influence Learning in Complex Systems
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
A comparison between humans and AI at recognizing objects in unusual poses
On the Convergence of SVGD in KL divergence via Approximate gradient flow
Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Task Weighting in Meta-learning with Trajectory Optimisation
Inference from Real-World Sparse Measurements
Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification
A Stochastic Proximal Polyak Step Size
A General-Purpose Multi-Modal OOD Detection Framework
Variational Neural Stochastic Differential Equations with Change Points
Mixed-View Panorama Synthesis using Geospatially Guided Diffusion
Certified Deductive Reasoning with Language Models
Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
Membership Inference Attacks and Privacy in Topic Modeling
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
Distributionally Robust Coreset Selection under Covariate Shift
Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates
Dynamic Subgoal-based Exploration via Bayesian Optimization
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
RefDeblur: Blind Motion Deblurring with Self-Generated Reference Image
Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
HARE: Human-in-the-Loop Algorithmic Recourse
Attention Normalization Impacts Cardinality Generalization in Slot Attention
Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models
HEAT: Hyperedge Attention Networks
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection
The Performance Of The Unadjusted Langevin Algorithm Without Smoothness Assumptions
Part-aware Prompted Segment Anything Model for Adaptive Segmentation
Hierarchical Language Model Design For Interpretable Graph Reasoning
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
Spectral learning of Bernoulli linear dynamical systems models for decision-making
Optimizing Intermediate Representations of Generative Models for Phase Retrieval
Bit-by-Bit: Investigating the Vulnerabilities of Binary Neural Networks to Adversarial Bit Flipping
FB-MOAC: A Reinforcement Learning Algorithm for Forward-Backward Markov Decision Processes
Improving Generalization with Approximate Factored Value Functions
Neural Monge Map estimation and its applications
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
PaDPaF: Partial Disentanglement with Partially-Federated GANs
SR-Reward: Taking The Path More Traveled
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Unsupervised Domain Adaptation by Learning Using Privileged Information
Class Incremental Learning from First Principles: A Review
One-Shot Federated Distillation Using Monoclass Teachers: A Study of Knowledge Fragmentation and Out-of-Distribution Supervision
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning
The AI Hippocampus: How Far are We From Human Memory?
Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation
Dimension reduction via score ratio matching
Stacking Variational Bayesian Monte Carlo
Probabilistic Autoencoder
Towards Optimization-Friendly Binary Neural Network
Estimating class separability of text embeddings with persistent homology
Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier
Generalization as Dynamical Robustness-The Role of Riemannian Contraction in Supervised Learning
A replica analysis of under-bagging
Continual Learning in Open-vocabulary Classification with Complementary Memory Systems
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning
Normed Spaces for Graph Embedding
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation
Variational Stochastic Gradient Descent for Deep Neural Networks
Learning Network Granger causality using Graph Prior Knowledge
Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?
Node Duplication Improves Cold-start Link Prediction
Score-Based Multimodal Autoencoder
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Boomerang: Local sampling on image manifolds using diffusion models
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases
Counterfactual Learning of Stochastic Policies with Continuous Actions
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
Semantic similarity prediction is better than other semantic similarity measures
Dextr: Zero-Shot Neural Architecture Search with Singular Value Decomposition and Extrinsic Curvature
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis
Removing Structured Noise using Diffusion Models
A Strong Baseline for Molecular Few-Shot Learning
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Workflow Discovery from Dialogues in the Low Data Regime
Lognormal Mutations and their Use in Detecting Surreptitious Fake Images
Inverting Gradient Attacks Makes Powerful Data Poisoning