yusuke1997/dblp-id-title huggingface dataset:
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity
Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics
Modularity aided consistent attributed graph clustering via coarsening
AttentionBreaker: Adaptive Evolutionary Optimization for Unmasking Vulnerabilities in LLMs through Bit-Flip Attacks
Enhancing Contrastive Clustering with Negative Pair-guided Regularization
Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs
FusionProt: Fusing Sequence and Structural Information for Unified Protein Representation Learning
Stochastic Direct Search Methods for Blind Resource Allocation
Inductive Global and Local Manifold Approximation and Projection
Towards Large Scale Transfer Learning for Differentially Private Image Classification
Action Poisoning Attacks on Linear Contextual Bandits
An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role of Training Noise
Gradient GA: Gradient Genetic Algorithm For Drug Molecular Design
Meta-Learning Adaptive Loss Functions
Addressing Attribute Bias with Adversarial Support-Matching
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Constrained Parameter Inference as a Principle for Learning
On Time Series Clustering with Graph Neural Networks
Improving Diffusion Models for Scene Text Editing with Dual Encoders
Getting aligned on representational alignment
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
Image Compression with Product Quantized Masked Image Modeling
AlignFix: Fixing Adversarial Perturbations by Agreement Checking for Adversarial Robustness against Black-box Attacks
Improved Variational Bayesian Phylogenetic Inference using Mixtures
Long Context Transfer from Language to Vision
DreamEdit: Subject-driven Image Editing
Exponential tilting of subweibull distributions
Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning
Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models
Controlling Federated Learning for Covertness
Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification
Using representation balancing to learn conditional-average dose responses from clustered data
Necessary and Sufficient Watermark for Large Language Models
Making Translators Privacy-aware on the User's Side
Can Optimization Trajectories Explain Multi-Task Transfer?
A Unified Domain Adaptation Framework with Distinctive Divergence Analysis
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons
InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers
E-Valuating Classifier Two-Sample Tests
Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement
On Intriguing Layer-Wise Properties of Robust Overfitting in Adversarial Training
Completeness and Coherence Learning for Fast Arbitrary Style Transfer
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
Robust Offline Imitation Learning from Diverse Auxiliary Data
Using Platt's scaling for calibration after undersampling - limitations and how to address them
DP-ImgSyn: Dataset Alignment for Obfuscated, Differentially Private Image Synthesis
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Data pruning and neural scaling laws: fundamental limitations of score-based algorithms
FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models
Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery
Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors
Slicing Unbalanced Optimal Transport
Online Control-Informed Learning
Nonconvex-nonconcave min-max optimization on Riemannian manifolds
Online Reference Tracking For Linear Systems with Unknown Dynamics and Unknown Disturbances
(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum
DNR-Pruning: Sparsity-Aware Pruning via Dying Neuron Reactivation in Convolutional Neural Networks
ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
No Need for Ad-hoc Substitutes: The Expected Cost is a Principled All-purpose Classification Metric
The (Un)Scalability of Informed Heuristic Function Estimation in NP-Hard Search Problems
Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks
Unifying Linear-Time Attention via Latent Probabilistic Modelling
Synthesizing Minority Samples for Long-tailed Classification via Distribution Matching
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
Generating Less Certain Adversarial Examples Improves Robust Generalization
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative model for Quasar spectra
Efficient Model-Agnostic Multi-Group Equivariant Networks
Stochastic Primal-Dual Double Block-Coordinate for Two- way Partial AUC Maximization
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Deep Kernel Learning of Nonlinear Latent Force Models
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
Accelerating Batch Active Learning Using Continual Learning Techniques
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
Structured Low-Rank Tensors for Generalized Linear Models
Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers
Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis
Modular Federated Contrastive Learning with Twin Normalization for Resource-limited Clients
A Proximal Operator for Inducing 2:4-Sparsity
Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning
Learning Multiscale Non-stationary Causal Structures
Worst-case Feature Risk Minimization for Data-Efficient Learning
CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making
An Information Theoretic Approach to Machine Unlearning
Temperature check: theory and practice for training models with softmax-cross-entropy losses
INR-V: A Continuous Representation Space for Video-based Generative Tasks
Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection
Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning
The 2024 Foundation Model Transparency Index
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
Multi-LoRA Composition for Image Generation
Subgraph Permutation Equivariant Networks
Improving Consistency in Large Language Models through Chain of Guidance
Assisting Human Decisions in Document Matching
LBMamba: Locally Bi-directional Mamba
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
Instance-Aware Graph Prompt Learning
On the special role of class-selective neurons in early training
Unsupervised Learning of Neurosymbolic Encoders
As large as it gets - Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters
Learning Linear Polytree Structural Equation Model
Benchmarking and Analyzing Unsupervised Network Representation Learning and the Illusion of Progress
In search of projectively equivariant networks
Bayesian Neighborhood Adaptation for Graph Neural Networks
Equivariant Symmetry Breaking Sets
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification
LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models
Celo: Training Versatile Learned Optimizers on a Compute Diet
Implicit Regularization of AdaDelta
Batched Nonparametric Bandits via k-Nearest Neighbor UCB
Unveiling Adversarially Robust Graph Lottery Tickets
Semantic Self-adaptation: Enhancing Generalization with a Single Sample
Clustering units in neural networks: upstream vs downstream information
Fourier Sensitivity and Regularization of Computer Vision Models
Bridging performance gap between minimal and maximal SVM models
Demonstration-Guided Multi-Objective Reinforcement Learning
Conciliator steering: Imposing user preference in multi-objective reinforcement learning
Double Horizon Model-Based Policy Optimization
Optimal Threshold Labeling for Ordinal Regression Methods
Learning Augmentation Distributions using Transformed Risk Minimization
Robust and Data-efficient Q-learning by Composite Value-estimation
Lurie Networks with Robust Convergent Dynamics
What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?
MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning
Can Pruning Improve Certified Robustness of Neural Networks?
Estimating Potential Outcome Distributions with Collaborating Causal Networks
Mastering SAM Prompts: A Large-Scale Empirical Study in Segmentation Refinement for Scientific Imaging
Deflated Dynamics Value Iteration
Sparse Coding with Multi-layer Decoders using Variance Regularization
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
Random Walk Diffusion for Efficient Large-Scale Graph Generation
Offline Deep Reinforcement Learning for Visual Distractions via Domain Adversarial Training
Bridging the Gap Between Target Networks and Functional Regularization
Approximate Policy Iteration with Bisimulation Metrics
EDM-TTS: Efficient Dual-Stage Masked Modeling for Alignment-Free Text-to-Speech Synthesis
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
Did I do that? Blame as a means to identify controlled effects in reinforcement learning
Towards Fair Video Summarization
A Self-Supervised Framework for Function Learning and Extrapolation
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Efficient pooling of predictions via kernel embeddings
Conformal prediction under ambiguous ground truth
Differentiable Model Compression via Pseudo Quantization Noise
A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models
Fast&Fair: Training Acceleration and Bias Mitigation for GNNs
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
Not All Causal Inference is the Same
Cluster Tree for Nearest Neighbor Search
Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study
A True-to-the-model Axiomatic Benchmark for Graph-based Explainers
On the Utility of Existing Fine-Tuned Models on Data-Scarce Domains
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
Blending Two Styles: Generating Inter-domain Images with MiddleGAN
Exploring End-to-end Differentiable Neural Charged Particle Tracking - A Loss Landscape Perspective
SPFormer: Enhancing Vision Transformer with Superpixel Representation
Personalized Federated Learning via Low-Rank Matrix Optimization
Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
Differentially private partitioned variational inference
Latte: Latent Diffusion Transformer for Video Generation
Explicitly Disentangled Representations in Object-Centric Learning
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Depth Scaling in Graph Neural Networks: Understanding the Flat Curve Behavior
Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients
U-Statistics for Importance-Weighted Variational Inference
Secure Domain Adaptation with Multiple Sources
An Efficient Training Algorithm for Models with Block-wise Sparsity
State-Constrained Offline Reinforcement Learning
Transformer Architecture Search for Improving Out-of-Domain Generalization in Machine Translation
Modular Deep Learning
Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo-Line-Search Learning Rate
Private GANs, Revisited
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores
Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions
Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations
Understanding Noise-Augmented Training for Randomized Smoothing
Blind Biological Sequence Denoising with Self-Supervised Set Learning
Deep Plug-and-Play Clustering with Unknown Number of Clusters
A Survey on Transferability of Adversarial Examples Across Deep Neural Networks
Dynamic Pricing in the Linear Valuation Model using Shape Constraints
Benchmarks for Physical Reasoning AI
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities
The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance
Density of states in neural networks: an in-depth exploration of learning in parameter space
Attentional-Biased Stochastic Gradient Descent
The 2023 Foundation Model Transparency Index
Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
The Diffusion Process as a Correlation Machine: Linear Denoising Insights
A Case for Library-Level $k$-Means Binning in Histogram Gradient-Boosted Trees
Dirichlet Mechanism for Differentially Private KL Divergence Minimization
Dynamics Adapted Imitation Learning
Structural Learning in Artificial Neural Networks: A Neural Operator Perspective
Uncertainty Quantification in Retrieval Augmented Question Answering
Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models
A Comprehensive Survey on Knowledge Distillation
Evaluating Posterior Probabilities: Decision Theory, Proper Scoring Rules, and Calibration
Revisiting Data Augmentation for Ultrasound Images
Neural Task Synthesis for Visual Programming
Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
MGPATH: A Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot Whole Slide Pathology Classification
Ensemble and Mixture-of-Experts DeepONets For Operator Learning
SPONGE: Competing Sparse Language Representations for Effective Knowledge Transfer
Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach
Distributional GFlowNets with Quantile Flows
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Detecting danger in gridworlds using Gromov's Link Condition
Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques
CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
Improving Differentially Private SGD via Randomly Sparsified Gradients
Auto-Lambda: Disentangling Dynamic Task Relationships
Leveraging Demonstrations with Latent Space Priors
SimPLR: A Simple and Plain Transformer for Efficient Object Detection and Segmentation
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity
Distributed Quasi-Newton Method for Fair and Fast Federated Learning
Normality-Guided Distributional Reinforcement Learning for Continuous Control
Partially Frozen Random Networks Contain Compact Strong Lottery Tickets
Towards Provable Log Density Policy Gradient
Towards fully covariant machine learning
Pre-Training Representations of Binary Code Using Contrastive Learning
Task Diversity Shortens the In-Context Learning Plateau
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation
Discretization Invariant Networks for Learning Maps between Neural Fields
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
FLR: Label-Mixture Regularization for Federated Learning with Noisy Labels
Rollout Total Correlation for Deep Reinforcement Learning
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
Prismer: A Vision-Language Model with Multi-Task Experts
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed
MagicPose4D: Crafting Articulated Models with Appearance and Motion Control
Learning Tree-Structured Composition of Data Augmentation
Genetic-Evolutionary Graph Neural Networks: A Paradigm for Improved Graph Representation Learning
Adaptively Robust and Sparse K -means Clustering
Routers in Vision Mixture of Experts: An Empirical Study
Event Tables for Efficient Experience Replay
Neural varifolds: an aggregate representation for quantifying the geometry of point clouds
Efficient Reasoning Models: A Survey
Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness
Amphibian: A Meta-Learning Framework for Rehearsal-Free, Fast Online Continual Learning
[Re] Improving Interpretation Faithfulness for Vision Transformers
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
Exploring validation metrics for offline model-based optimisation with diffusion models
Reweighting Improves Conditional Risk Bounds
Neural Likelihood Approximation for Integer Valued Time Series Data
LLM-Guided Self-Supervised Tabular Learning With Task-Specific Pre-text Tasks
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
The inexact power augmented Lagrangian method for constrained nonconvex optimization
StarCoder: may the source be with you!
Achieving Risk Control in Online Learning Settings
CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
The Vendi Score: A Diversity Evaluation Metric for Machine Learning
The Slingshot Effect: A Late-Stage Optimization Anomaly in Adaptive Gradient Methods
Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
A Generalist Agent
Dual Representation Learning for Out-of-distribution Detection
Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion
Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)
TP‑Blend: Textual‑Prompt Attention Pairing for Precise Object‑Style Blending in Diffusion Models
Relative Phase Equivariant Deep Neural Systems for Physical Layer Communications
Information Theoretic Guarantees For Policy Alignment In Large Language Models
Consistent Collaborative Filtering via Tensor Decomposition
Mechanistic Interpretability for AI Safety - A Review
Hessian Free Efficient Single Loop Iterative Differentiation Methods for Bi-Level Optimization Problems
Synthetic Data is Sufficient for Zero-Shot Visual Generalization from Offline Data
Out-of-Distribution Optimality of Invariant Risk Minimization
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
MaxCutBench: Revisiting and Benchmarking Graph Neural Networks for Maximum Cut
Computationally-efficient initialisation of GPs: The generalised variogram method
Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition
Beyond Boundaries: A Novel Data-Augmentation Discourse for Open Domain Generalization
Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns
Retiring ΔDP: New Distribution-Level Metrics for Demographic Parity
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape
The Low-Rank Simplicity Bias in Deep Networks
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms
Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs
AutoDocSegmenter: A Geometric Approach towards Self-Supervised Document Segmentation
Provably Personalized and Robust Federated Learning
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets
Diversity-Driven View Subset Selection for Indoor Novel View Synthesis
On the Robustness of Neural Collapse and the Neural Collapse of Robustness
A Neural Material Point Method for Particle-based Emulation
Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression
Scaling Channel-Adaptive Self-Supervised Learning
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
Path Development Network with Finite-dimensional Lie Group
Unbiased Loss Functions for Multilabel Classification with Missing Labels
The Initialization Determines Whether In-Context Learning Is Gradient Descent
A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection
TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimation
Path-Specific Counterfactual Fairness via Dividend Correction
On Memorization in Diffusion Models
Hyperbolic Random Forests
Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence
Early Stopping for Deep Image Prior
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers
Fairness and robustness in anti-causal prediction
Robust Model Selection of Gaussian Graphical Models
Decision-Focused Surrogate Modeling for Mixed-Integer Linear Optimization
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits
RefinedFields: Radiance Fields Refinement for Planar Scene Representations
Finite-Time Analysis of Decentralized Single-Timescale Actor-Critic
Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
Nonstationary Reinforcement Learning with Linear Function Approximation
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers
On Using Certified Training towards Empirical Robustness
Pseudo-Differential Neural Operator: Generalize Fourier Neural operator for Learning Solution Operators of Partial Differential Equations
Learn the Time to Learn: Replay Scheduling in Continual Learning
On Characterizing the Trade-off in Invariant Representation Learning
Do Concept Bottleneck Models Respect Localities?
Abstraction for Bayesian Reinforcement Learning in Factored POMDPs
A Survey on Fairness Without Demographics