yusuke1997/dblp-id-title huggingface dataset:
Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification
Stochastic gradient updates yield deep equilibrium kernels
Reliable and Responsible Foundation Models
f -Divergence Policy Optimization in Fully Decentralized Cooperative MARL
Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts
Diagnostic Tool for Out-of-Sample Model Evaluation
Lower Ricci Curvature for Efficient Community Detection
Adaptive Mesh Quantization for Neural PDE Solvers
LocalFormer: Mitigating Over-Globalising in Transformers on Graphs with Localised Training
A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices
VColRL: Learn to solve the Vertex Coloring Problem using Reinforcement Learning
Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor
Investigating the impact of missing value handling on Boosted trees and Deep learning for Tabular data: A Claim Reserving case study
Doubly Robust Uncertainty Quantification for Quantile Treatment Effects in Sequential Decision Making
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
Long-term Forecasting with TiDE: Time-series Dense Encoder
From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks
Contextual Combinatorial Bandits With Changing Action Sets Via Gaussian Processes
Optimization Dynamics of Equivariant and Augmented Neural Networks
Accelerated Deep Active Learning with Graph-based Sub- Sampling
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Graph Potential Field Neural Network for Massive Agents Group-wise Path Planning
Transfer Learning in ℓ 1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis
On the Low-Rank Parametrization of Reward Models for Controlled Language Generation
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
FragFormer: A Fragment-based Representation Learning Framework for Molecular Property Prediction
Respecting the limit: Bayesian optimization with a bound on the optimal value
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization
Adaptive Compression for Communication-Efficient Distributed Training
Reproducibility study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"
Task-Relevant Feature Selection with Prediction Focused Mixture Models
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm
Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning
Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions
Robust Stochastic Optimization via Gradient Quantile Clipping
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
Improving CLIP Counting Accuracy via Parameter-Efficient Fine-Tuning
Deep Autoregressive Models as Causal Inference Engines
Minorization-Maximization for Learning Determinantal Point Processes
Improved Group Robustness via Classifier Retraining on Independent Splits
Your Policy Regularizer is Secretly an Adversary
Single-positive Multi-label Learning with Label Cardinality
Linear algebra with transformers
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
On the Regularization of Learnable Embeddings for Time Series Forecasting
Gradient Masked Averaging for Federated Learning
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks
Bayesian Quantification with Black-Box Estimators
A Local Polyak-Łojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
Hyperparameters in Continual Learning: A Reality Check
DisCo: Improving Compositional Generalization in Visual Reasoning through Distribution Coverage
Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning
Adaptive Clipping for Differential Private Federated Learning in Interpolation Regimes
Selective Classification Under Distribution Shifts
Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images
Adversarially Robust Spiking Neural Networks Through Conversion
About the Cost of Central Privacy in Density Estimation
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization
A note on the $k$-means clustering for missing data
Adversarial Bandits Against Arbitrary Strategies
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
Towards a General Transfer Approach for Policy-Value Networks
V1T: large-scale mouse V1 response prediction using a Vision Transformer
MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series
M 3 PL: Identifying and Exploiting View Bias of Prompt Learning
Temporal Test-Time Adaptation with State-Space Models
Differentially Private Latent Diffusion Models
Collaborative Algorithms for Online Personalized Mean Estimation
Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation
TensorVAE: a simple and efficient generative model for conditional molecular conformation generation
VisionAD, a software package of performant anomaly detection algorithms, and Proportion Localised, an interpretable metric
On the Sample Complexity of Lipschitz Constant Estimation
Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?
FairGrad: Fairness Aware Gradient Descent
Disciplined Saddle Programming
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Improving Single-round Active Adaptation: A Prediction Variability Perspective
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games
Rewarding the Rare: Maverick-Aware Shapley Valuation in Federated Learning
Prioritized Federated Learning: Leveraging Non-Priority Clients for Targeted Model Improvement
Uncertainty in Graph Neural Networks: A Survey
MemLLM: Finetuning LLMs to Use Explicit Read-Write Memory
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation
Lie Symmetry Net: Preserving Conservation Laws in Modelling Financial Market Dynamics via Differential Equations
PopulAtion Parameter Averaging (PAPA)
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling
Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring
Adaptive Group Robust Ensemble Knowledge Distillation
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality
Variance-aware decision making with linear function approximation under heavy-tailed rewards
Black-Box Prompt Learning for Pre-trained Language Models
Towards generalizing deep-audio fake detection networks
Measuring Orthogonality in Representations of Generative Models
An Information-Theoretic Lower Bound on the Generalization Error of Autoencoders
An optimal control perspective on diffusion-based generative modeling
Dual-windowed Vision Transformer with Angular Self- Attention
Reproducibility Study: Equal Improvability: A New Fairness Notion Considering the Long-Term Impact
Continual Learning: Applications and the Road Forward
GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A
On the Adversarial Robustness of Vision Transformers
Functional Linear Regression of Cumulative Distribution Functions
Learning-to-defer for sequential medical decision-making under uncertainty
On Learning Representations for Tabular Data Distillation
A Fisher-Rao gradient flow for entropic mean-field min-max games
A note on regularised NTK dynamics with an application to PAC-Bayesian training
OmniInput: An Evaluation Framework for Deep Learning Models on Internet-Scale Data
Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making
Beyond Labeling Oracles - What does it mean to steal ML models?
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
Extracting Local Reasoning Chains of Deep Neural Networks
PROXI: Challenging the GNNs for Link Prediction
Enhancing Sample Generation of Diffusion Models using Noise Level Correction
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression
Stochastic Bandits for Egalitarian Assignment
Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm
Kernel Space Conditional Distribution Alignment for Improving Group Fairness in Deepfake Detection
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Correcting Flaws in Common Disentanglement Metrics
Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation
Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction
A second-order-like optimizer with adaptive gradient scaling for deep learning
Robust Multimodal Learning via Cross-Modal Proxy Tokens
Lightweight Learner for Shared Knowledge Lifelong Learning
Reinventing Policy Iteration under Time Inconsistency
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
The Sparse Matrix-Based Random Projection: A Study of Binary and Ternary Quantization
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization
Error Bounds for Flow Matching Methods
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
Statistical Error Bounds for GANs with Nonlinear Objective Functionals
Enhancing Parameter Efficiency and Generalization in Large Models: A Regularized and Masked Low-Rank Adaptation Approach
Linear Weight Interpolation Leads to Transient Performance Gains
Logistic-Normal Likelihoods for Heteroscedastic Label Noise
Can You Win Everything with A Lottery Ticket?
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Adversarial Robustness of Graph Transformers
Reproducibility Study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework"
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models
Fair and Useful Cohort Selection
Dual PatchNorm
Revisiting adversarial training for the worst-performing class
Mean-Field Langevin Dynamics : Exponential Convergence and Annealing
Numerical Accounting in the Shuffle Model of Differential Privacy
CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
[Re] CUDA: Curriculum of Data Augmentation for Long-tailed Recognition
Learning Object-Centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition
Separable Self-attention for Mobile Vision Transformers
Investigating Continual Pretraining in Large Language Models: Insights and Implications
Improving Robust Generalization with Diverging Spanned Latent Space
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
AmbientFlow: Invertible generative models from incomplete, noisy measurements
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation
HypUC: Hyperfine Uncertainty Calibration with Gradient- boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms
Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning
Perception Stitching: Zero-Shot Perception Encoder Transfer for Visuomotor Robot Policies
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning
Robust Distortion-free Watermarks for Language Models
Out-of-Distribution Learning with Human Feedback
On the link between conscious function and general intelligence in humans and machines
Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs
HDCS: Hierarchy Discovery and Critic Shaping for Reinforcement Learning with Automaton Specification
HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection
Generative Feature Training of Thin 2-Layer Networks
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Data-Centric Defense: Shaping Loss Landscape with Augmentations to Counter Model Inversion
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis
CREW: Facilitating Human-AI Teaming Research
Deep Koopman Learning using Noisy Data
A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks
Studying Exploration in RL: An Optimal Transport Analysis of Occupancy Measure Trajectories
Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model
Multi-model Online Conformal Prediction with Graph-Structured Feedback
Federated TD Learning with Linear Function Approximation under Environmental Heterogeneity
On the Convergence and Calibration of Deep Learning with Differential Privacy
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
Generative Adversarial Neural Operators
Integrating Rankings into Quantized Scores in Peer Review
Conditional Image Synthesis with Diffusion Models: A Survey
Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations
Containing a spread through sequential learning: to exploit or to explore?
From Persona to Personalization: A Survey on Role-Playing Language Agents
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
k-Mixup Regularization for Deep Learning via Optimal Transport
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
Regret Bounds for Satisficing in Multi-Armed Bandit Problems
Synthesizing world models for bilevel planning
Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization
Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents
Meta-Learning Approach for Joint Multimodal Signals with Multimodal Iterative Adaptation
On the Statistical Complexity of Estimation and Testing under Privacy Constraints
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
The Harmonic Indel Distance
Kernel Normalized Convolutional Networks
IRWE: Inductive Random Walk for Joint Inference of Identity and Position Network Embedding
Capsule Network Projectors are Equivariant and Invariant Learners
The Cold Posterior Effect Indicates Underfitting, and Cold Posteriors Represent a Fully Bayesian Method to Mitigate It
On Adaptivity in Quantum Testing
Multi-Grid Tensorized Fourier Neural Operator for High- Resolution PDEs
Learning distributed representations with efficient SoftMax normalization
Faking Interpolation Until You Make It
Invariant Structure Learning for Better Generalization and Causal Explainability
A Short Survey on Importance Weighting for Machine Learning
Flipped Classroom: Effective Teaching for Time Series Forecasting
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
Merging Text Transformer Models from Different Initializations
Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting
PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
Jacobian-based Causal Discovery with Nonlinear ICA
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
ViewFusion: Learning Composable Diffusion Models for Novel View Synthesis
Linearized Relative Positional Encoding
Finding and Fixing Spurious Patterns with Explanations
Inverse Scaling: When Bigger Isn't Better
How does overparametrization affect performance on minority groups?
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling
Highway Graph to Accelerate Reinforcement Learning
Teaching Models to Express Their Uncertainty in Words
Efficient Inference With Model Cascades
Probing Predictions on OOD Images via Nearest Categories
Mesh-Informed Neural Operator : A Transformer Generative Approach
Greedy Bayesian Posterior Approximation with Deep Ensembles
Reusable Options through Gradient-based Meta Learning
Novel Class Discovery for Long-tailed Recognition
Assisted Learning for Organizations with Limited Imbalanced Data
Single-Shot Plug-and-Play Methods for Inverse Problems
Faster Convergence of Local SGD for Over-Parameterized Models
Synthesizing Libraries of Programs with Auxiliary Functions
Reproducibility Study of "Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation"
Meta-Learning for Graphs with Heterogeneous Node Attribute Spaces for Few-Shot Edge Predictions
Diffusion Models with Deterministic Normalizing Flow Priors
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
3D-Aware Video Generation
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion
Towards Stability of Autoregressive Neural Operators
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Privacy Budget Tailoring in Private Data Analysis
Activate and Adapt: A Two-Stage Framework for Open-Set Model Adaptation
The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Approximations to worst-case data dropping: unmasking failure modes
Variance Reduced Smoothed Functional REINFORCE Policy Gradient Algorithms
Physics informed neural networks for elliptic equations with oscillatory differential operators
Fine-tuning can cripple your foundation model; preserving features may be the solution
Sparse Contextual CDF Regression
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
Test-Time Adaptation with Source Based Auxiliary Tasks
One-Step Distributional Reinforcement Learning
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps
Neural Graph Reasoning: A Survey on Complex Logical Query Answering
Multivariate Dense Retrieval: A Reproducibility Study under a Memory-limited Setup
Convergence Analysis and Trajectory Comparison of Gradient Descent for Overparameterized Deep Linear Networks
Music Foundation Model as Generic Booster for Music Downstream Tasks
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
The Stack: 3 TB of permissively licensed source code
Understanding Self-supervised Contrastive Learning through Supervised Objectives
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and as Non-Linear Diffusion
Weakly Supervised Object Segmentation by Background Conditional Divergence
Reward-based Autonomous Online Learning Framework for Resilient Cooperative Target Monitoring using a Swarm of Robots
CoDeC: Communication-Efficient Decentralized Continual Learning
Improved motif-scaffolding with SE(3) flow matching
The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification
Global Convergence Rate of Deep Equilibrium Models with General Activations
Self-Supervised Visual Representation Learning for Medical Image Analysis: A Comprehensive Survey
Structural Pruning of Pre-trained Language Models via Neural Architecture Search
Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?
System-2 Mathematical Reasoning via Enriched Instruction Tuning
Learning from Natural Language Feedback
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
Why Fine-grained Labels in Pretraining Benefit Generalization?
Scaling Laws of Distributed Random Forests
Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
On the Equivalence of Graph Convolution and Mixup
Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning
Single Image Test-Time Adaptation for Segmentation
Sparse MoEs meet Efficient Ensembles
Analysis of generalization capacities of Neural Ordinary Differential Equations
Contaminated Online Convex Optimization
A geometrical connection between sparse and low-rank matrices and its application to manifold learning
Neuron-based explanations of neural networks sacrifice completeness and interpretability
Incorporating Inductive Biases to Energy-based Generative Models
Learning Identity-Preserving Transformations on Data Manifolds
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits
Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval
On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks
Expert Routing with Synthetic Data for Domain Incremental Learning
Do not trust what you trust: Miscalibration in Semisupervised Learning
Active Prompt Learning with Vision-Language Model Priors
Asymptotic Analysis of Conditioned Stochastic Gradient Descent
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
Vision Learners Meet Web Image-Text Pairs
Approximating Naive Bayes on Unlabelled Categorical Data
Agreement-Based Cascading for Efficient Inference
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
Fair Feature Importance Scores for Interpreting Decision Trees