yusuke1997/dblp-id-title huggingface dataset:
Byzantine-Robust and Hessian-Free Federated Bilevel Optimization
Rewiring with Positional Encodings for Graph Neural Networks
Learning Interpolations between Boltzmann Densities
Variational Elliptical Processes
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Towards context and domain-aware algorithms for scene analysis
PCF Learned Sort: a Learning Augmented Sort Algorithm with $\mathcal{O}(n \log\log n)$ Expected Complexity
Are Convex Optimization Curves Convex?
Quasipseudometric Value Functions with Dense Rewards
Simple Steps to Success: A Method for Step-Based Counterfactual Explanations
E(n)-equivariant Graph Neural Cellular Automata
Reward Guided Latent Consistency Distillation
Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning
Exploring the Limitations of Layer Synchronization in Spiking Neural Networks
DeformTime: capturing variable dependencies with deformable attention for time series forecasting
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
Fast and Effective Weight Update for Pruned Large Language Models
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
Does Entity Abstraction Help Generative Transformers Reason?
Universal Graph Continual Learning
Sparse Modal Regression with Mode-Invariant Skew Noise
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection
Tackling Provably Hard Representative Selection via Graph Neural Networks
Certified Robustness against Sparse Adversarial Perturbations via Data Localization
XAudit : A Learning-Theoretic Look at Auditing with Explanations
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization
CNN Interpretability with Multivector Tucker Saliency Maps for Self-Supervised Models
On the Paradox of Certified Training
Training-free Graph Neural Networks and the Power of Labels as Features
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D
RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression
GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search
Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation"
Permissive Information-Flow Analysis for Large Language Models
AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability in Algorithmic Environments
Merging by Matching Models in Task Parameter Subspaces
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization
Synchrony-Gated Plasticity with Dopamine Modulation for Spiking Neural Networks
Amdahl's Law for LLMs: A Throughput-Centric Analysis of Extreme LLM Quantization
Fair Representation in Submodular Subset Selection: A Pareto Optimization Approach
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
Unlocking the matrix form of the Quaternion Fourier Transform and Quaternion Convolution: Properties, connections, and application to Lipschitz constant bounding
A Reproducibility Study of Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks
Score-Based Denoising Diffusion Models for Photon-Starved Image Restoration Problems
Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC)
Bidirectional View based Consistency Regularization for Semi-Supervised Domain Adaptation
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
Offset Unlearning for Large Language Models
Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-Collapse
How Much Pre-training Is Enough to Discover a Good Subnetwork?
Collaborative Compressors in Distributed Mean Estimation with Limited Communication Budget
Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks
Variance Dichotomy in Feature Spaces of Facial Recognition Systems is a Weak Defense against Simple Weight Manipulation Attacks
Evaluating Graph Generative Models with Graph Kernels: What Structural Characteristics Are Captured?
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models
Interpreting Neurons in Deep Vision Networks with Language Models
An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Offline Reinforcement Learning via Tsallis Regularization
Towards shutdownable agents via stochastic choice
Reproducibility Study of 'SLICE: Stabilized LIME for Consistent Explanations for Image Classification'
Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration
Bayesian Transferability Assessment for Spiking Neural Networks
SIRE: SE(3) Intrinsic Rigidity Embeddings
The Kernel Perspective on Dynamic Mode Decomposition
Sensitivity-Aware Amortized Bayesian Inference
Transframer: Arbitrary Frame Prediction with Generative Models
Credal Bayesian Deep Learning
Evaluating Compositional Scene Understanding in Multimodal Generative Models
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration
Group Fair Federated Learning via Stochastic Kernel Regularization
GraphPrivatizer: Improved Structural Differential Privacy for Graph Neural Networks
Lookback for Learning to Branch
Global Optimization Algorithm through High-Resolution Sampling
CAREER: A Foundation Model for Labor Sequence Data
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling
Multi-conditioned Graph Diffusion for Neural Architecture Search
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining
When Should Reinforcement Learning Use Causal Reasoning?
Smoothed Robustness Analysis: Bridging worst- and average-case robustness analyses via smoothed analysis
A Generalization Bound for Nearly-Linear Networks
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow
Hodge-Aware Convolutional Learning on Simplicial Complexes
Fixed-Budget Best-Arm Identification in Sparse Linear Bandits
Improved Localized Machine Unlearning Through the Lens of Memorization
Meta-learning Population-based Methods for Reinforcement Learning
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection
Federated Generalized Novel Category Discovery with Prompts Tuning
On the Role of Discrete Representation in Sparse Mixture of Experts
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs
Learning Actionable Counterfactual Explanations in Large State Spaces
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models
Preconditioned Neural Posterior Estimation for Likelihood-free Inference
Wolf: Dense Video Captioning with a World Summarization Framework
Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code
Proportional Fairness in Federated Learning
Complementary Sparsity: Accelerating Sparse CNNs with High Accuracy on General-Purpose Computing Platforms
Early Classification of Time Series: A Survey and Benchmark
Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiation
A Survey on Verifiable Cross-Silo Federated Learning
No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning
Asynchronous Training Schemes in Distributed Learning with Time Delay
A Unified View of Masked Image Modeling
Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers
Let There be Direction in Hypergraph Neural Networks
Learning to Be Cautious
A User's Guide to Sampling Strategies for Sliced Optimal Transport
Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction
What do larger image classifiers memorise?
Multitask Online Mirror Descent
Distributed and Secure Kernel-Based Quantum Machine Learning
Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation
Alternators For Sequence Modeling
Reinforcement Learning for Node Selection in Branch-and-Bound
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
Neural Causal Structure Discovery from Interventions
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-scale Neural Network Optimization
Modeling Human Beliefs about AI Behavior for Scalable Oversight
SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks
Fast Slate Policy Optimization: Going Beyond Plackett-Luce
Revisiting Topic-Guided Language Models
Releasing Graph Neural Networks with Differential Privacy Guarantees
Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance
Revisiting Deep Hybrid Models for Out-of-Distribution Detection
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Chasing Better Deep Image Priors between Over- and Under-parameterization
Automated Design of Metaheuristic Algorithms: A Survey
Continual Pre-training of MoEs: How robust is your router?
Neural Deconstruction Search for Vehicle Routing Problems
Learning by Self-Explaining
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
Augmented Language Models: a Survey
Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization
In-distribution adversarial attacks on object recognition models using gradient-free search
Affordable Generative Agents
ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations
Using Skew to Assess the Quality of GAN-generated Image Features
Efficient Multi-Agent Cooperation Learning through Teammate Lookahead
FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing flows and the Feynman-Kac Formula
Exploring Efficient Few-shot Adaptation for Vision Transformers
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation
Sample, estimate, aggregate: A recipe for causal discovery foundation models
A persistent homology-based algorithm for unsupervised anomaly detection in time series
Zero-shot Node Classification with Graph Contrastive Embedding Network
The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain
Adaptive Training Distributions with Scalable Online Bilevel Optimization
Dynamic Online Ensembles of Basis Expansions
Disentangled and Self-Explainable Node Representation Learning
Non-backtracking Graph Neural Networks
A Survey on the Possibilities & Impossibilities of AI-generated Text Detection
Autoencoding Hyperbolic Representation for Adversarial Generation
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
SparseDiff: Sparse Discrete Diffusion for Scalable Graph Generation
Learning to Optimize Quasi-Newton Methods
Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention
RLTF: Reinforcement Learning from Unit Test Feedback
Pre-trained Perceptual Features Improve Differentially Private Image Generation
On the Gradient Formula for learning Generative Models with Regularized Optimal Transport Costs
Large Language Models can be Guided to Evade AI-generated Text Detection
Teacher's pet: understanding and mitigating biases in distillation
The Survival Bandit Problem
Evaluating the Evaluators: Are Validation Methods for Few-Shot Learning Fit for Purpose?
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
Meta Continual Learning on Graphs with Experience Replay
Cognitive Architectures for Language Agents
Unified Risk Analysis for Weakly Supervised Learning
Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning
Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems
VSCoDe: Visual-Augmentation Selection for Contrastive Decoding
Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization
SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
InduCE: Inductive Counterfactual Explanations for Graph Neural Networks
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
Re-Thinking Inverse Graphics With Large Language Models
IM-Context: In-Context Learning for Imbalanced Regression Tasks
Diverse Condensed Data Generation via Class Preserving Distribution Matching
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
Change Point Detection on A Separable Model for Dynamic Networks
Learning the Language of Protein Structure
RIZE: Adaptive Regularization for Imitation Learning
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models
Towards Multi-spatiotemporal-scale Generalized PDE Modeling
Reproducibility Study of "Explaining RL Decisions with Trajectories"
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
PCPs: Patient Cardiac Prototypes to Probe AI-based Medical Diagnoses, Distill Datasets, and Retrieve Patients
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages
Efficient Exploration in Multi-Agent Reinforcement Learning via Farsighted Self-Direction
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
A Ranking Game for Imitation Learning
Granger Causal Interaction Skill Chains
Convergence of linear programming hierarchies for Gibbs states of spin systems
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity
On the Robustness of Dataset Inference
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Risk-Controlling Model Selection via Guided Bayesian Optimization
Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks
Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin
"Studying How to Efficiently and Effectively Guide Models with Explanations" - A Reproducibility Study
Leopard: A Vision Language Model for Text-Rich Multi- Image Tasks
Know Yourself and Know Your Neighbour : A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork
Text Descriptions are Compressive and Invariant Representations for Visual Learning
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks
COIN++: Neural Compression Across Modalities
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
Future-aware Safe Active Learning of Time Varying Systems using Gaussian Processes
[Re] On the Reproducibility of Post-Hoc Concept Bottleneck Models
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods
Selective Prediction via Training Dynamics
A Survey on the Honesty of Large Language Models
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
The Kernel Density Integral Transformation
Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks
Data-Driven Discovery of PDEs via the Adjoint Method
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
GeNIe: Generative Hard Negative Images Through Diffusion
Language-assisted Feature Representation and Lightweight Active Learning For On-the-Fly Category Discovery
Influential Bandits: Pulling an Arm May Change the Environment
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
Identifying Causal Structure in Dynamical Systems
Finding and Only Finding Differential Nash Equilibria by Both Pretending to be a Follower
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Neural Collapse: A Review on Modelling Principles and Generalization
Maximizing Global Model Appeal in Federated Learning
Data Distillation: A Survey
Identification of Negative Transfers in Multitask Learning Using Surrogate Models
Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Domain Invariant Adversarial Learning
Byzantine-Resilient Decentralized Multi-Armed Bandits
Unlabelled Compressive Sensing under Sparse Permutation and Prior Information
Representations and Computations in Transformers that Support Generalization on Structured Tasks
Understanding Fairness Surrogate Functions in Algorithmic Fairness
Learning Task-Aware Abstract Representations for Meta-Reinforcement Learning
DNOD: Deformable Neural Operators for Object Detection in SAR Images
Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving
Single-pass Detection of Jailbreaking Input in Large Language Models
Invertible Hierarchical Generative Model for Images
Mental Modelling of Reinforcement Learning Agents by Language Models
Towards Understanding Adversarial Transferability in Federated Learning
Diffusion Models for Constrained Domains
Straggler-Resilient Personalized Federated Learning
Directed Graph Transformers
Catastrophic overfitting can be induced with discriminative non-robust features
Selective Pre-training for Private Fine-tuning
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search
The Graph Cut Kernel for Ranked Data
Unifying physical systems' inductive biases in neural ODE using dynamics constraints
MobileCLIP2: Improving Multi-Modal Reinforced Training
Large-Scale Targeted Cause Discovery via Learning from Simulated Data
Shedding Light on Problems with Hyperbolic Graph Learning
Lifelong Learning in StyleGAN through Latent Subspaces
DFML: Decentralized Federated Mutual Learning
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
PSC: Posterior Sampling-Based Compression
Scaling Laws for Predicting Downstream Performance in LLMs
LASE: Learned Adjacency Spectral Embeddings
Uniformly Distributed Feature Representations for Fair and Robust Learning
Pairwise Learning with Adaptive Online Gradient Descent
Bayesian Optimization with Informative Covariance
Better Theory for SGD in the Nonconvex World
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
Off-Policy Evaluation with Out-of-Sample Guarantees
Deformation Robust Roto-Scale-Translation Equivariant CNNs
Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
Systematically and efficiently improving k -means initialization by pairwise-nearest-neighbor smoothing
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
Do Parameters Reveal More than Loss for Membership Inference?
Node Classification With Reject Option
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
Efficient Distillation of Classifier-Free Guidance using Adapters
Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs
Enhancing Vision-Language Model with Unmasked Token Alignment
Orthogonal Random Features: Explicit Forms and Sharp Inequalities
UCB Exploration for Fixed-Budget Bayesian Best Arm Identification
SQL-PaLM: Improved large language model adaptation for Text-to-SQL
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference
Efficient Diffusion Models: A Survey
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
Shared Imagination: LLMs Hallucinate Alike
HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm
Learning to Rank with Top-$K$ Fairness
Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data
Faithful Interpretation for Graph Neural Networks
Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness
Contrastive Class Anchor Learning for Open Set Object Recognition in Driving Scenes
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts
Understanding Linearity of Cross-Lingual Word Embedding Mappings
Dependency Structure Search Bayesian Optimization for Decision Making Models
Named Tensor Notation