yusuke1997/dblp-id-title huggingface dataset:
Reinforcement Learning from Human Feedback with Active Queries
Support-Set Context Matters for Bongard Problems
Algorithmic fairness with monotone likelihood ratios
New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs
An empirical study of implicit regularization in deep offline RL
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
Before Forgetting, There's Learning: Representation Learning Challenges in Online Unsupervised Continual Learning
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
Personalised Federated Learning On Heterogeneous Feature Spaces
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
Towards Robust Scale-Invariant Mutual Information Estimators
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize
EdiBERT: a generative model for image editing
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation
Large Language Models Synergize with Automated Machine Learning
Fusion of Global and Local Knowledge for Personalized Federated Learning
Enhancing Cost Efficiency in Active Learning with Candidate Set Query
Exploring the potential of Direct Feedback Alignment for Continual Learning
Robust Symbolic Regression for Dynamical System Identification
Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework
Buffer-based Gradient Projection for Continual Federated Learning
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics
OpenCon: Open-world Contrastive Learning
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields
Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems
CroissantLLM: A Truly Bilingual French-English Language Model
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs
Diffusion-RainbowPA: Improvements Integrated Preference Alignment for Diffusion-based Text-to-Image Generation
ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulations
Federated Learning with Convex Global and Local Constraints
Finding Competence Regions in Domain Generalization
Decoupled Sequence and Structure Generation for Realistic Antibody Design
Minimax Posterior Contraction Rates for Unconstrained Distribution Estimation on [0, 1] d under Wasserstein Distance
Masked Autoencoders are PDE Learners
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer
The Time-Energy Model: Selective Time-Series Forecasting Using Energy-Based Models
Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Pre-trained Vision-Language Models Learn Discoverable Visual Concepts
Qualifying Knowledge and Knowledge Sharing in Multilingual Models
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
Hierarchical VAE with a Diffusion-based VampPrior
Video Diffusion Models: A Survey
Causal Feature Selection via Orthogonal Search
Training Dynamics of Learning 3D-Rotational Equivariance
The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models
Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance
Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures
Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach
Analysis of Classifier-Free Guidance Weight Schedulers
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning
Structural Causal Circuits: Probabilistic Circuits Climbing All Rungs of Pearl's Ladder of Causation
Expected Worst Case Regret via Stochastic Sequential Covering
Training Graph Neural Networks Subject to a Tight Lipschitz Constraint
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization
Sortability of Time Series Data
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
Understanding Emergent In-Context Learning from a Kernel Regression Perspective
CAE v2: Context Autoencoder with CLIP Latent Alignment
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?
Adjacency Search Embeddings
Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation
Table Foundation Models: on knowledge pre-training for tabular learning
Non-Stationary Contextual Pricing with Safety Constraints
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
An elementary concentration bound for Gibbs measures arising in statistical learning theory
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization
AT4TS : Autotune for Time Series Foundation Models
The Analysis of the Expected Change in the Classification Probability of the Predicted Label
Sparse Neural Architectures via Deterministic Ramanujan Graphs
Explicit Group Sparse Projection with Applications to Deep Learning and NMF
A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful
Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation
Task Arithmetic Through The Lens Of One-Shot Federated Learning
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning
An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
Recurrent Inertial Graph-Based Estimator (RING): A Single Pluripotent Inertial Motion Tracking Solution
Towards Size-Independent Generalization Bounds for Deep Operator Nets
Execution-based Code Generation using Deep Reinforcement Learning
EL-Clustering: Combining Upper- and Lower-Bounded Clusterings for Equitable Load Constraints
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning
Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training
When resampling/reweighting improves feature learning in imbalanced classification? A toy-model study
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Federated Graph Learning with Graphless Clients
Two-Step Offline Preference-Based Reinforcement Learning on Explicitly Constrained Policies
CXAD: Contrastive Explanations for Anomaly Detection: Algorithms, Complexity Results and Experiments
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Trusted Aggregation (TAG): Backdoor Defense in Federated Learning
Teaching Diffusion Models to Ground Alpha Matte
Learning in complex action spaces without policy gradients
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks
Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics
Bounded Space Differentially Private Quantiles
Maximally Expressive GNNs for Outerplanar Graphs
HiFE: Hierarchical Feature Ensemble Framework for Few-shot Hypotheses Adaptation
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Generalizable Representation Learning for fMRI-based Neurological Disorder Identification
A limitation on black-box dynamics approaches to Reinforcement Learning
Learning to Prompt Your Domain for Federated Vision-Language Models
LC-PLM: Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers
Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks
Data-Free Diversity-Based Ensemble Selection for One-Shot Federated Learning
Registers in Small Vision Transformers: A Reproducibility Study of Vision Transformers Need Registers
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Ridge Regression and Wide Neural Networks
Multi-Fidelity Active Learning with GFlowNets
Activation sharding for scalable training of large models
Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity
Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions
Semantic Alignment for Prompt-Tuning in Vision Language Models
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
Switching Latent Bandits
Optimized Tradeoffs for Private Prediction with Majority Ensembling
On the Efficacy of Differentially Private Few-shot Image Classification
Learning to correct spectral methods for simulating turbulent flows
Learning Hierarchical Relational Representations through Relational Convolutions
Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information
Bandits with Mean Bounds
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series
Expressiveness of Parametrized Distributions over DAGs for Causal Discovery
On Noise Abduction for Answering Counterfactual Queries: A Practical Outlook
Meta-Learning Sparse Compression Networks
Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models
Non-Stationary Dueling Bandits Under a Weighted Borda Criterion
Toward a Complete Criterion for Value of Information in Insoluble Decision Problems
A general framework of Riemannian adaptive optimization methods with a convergence analysis
Gaussian Processes with Bayesian Inference of Covariate Couplings
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching
Generalized Orders of Magnitude for Scalable, Parallel, High-Dynamic-Range Computation
In-context Learning with Retrieved Demonstrations for Language Models: A Survey
Score-based Explainability for Graph Representations
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models
Using Confounded Data in Latent Model-Based Reinforcement Learning
[RE] GNNBoundary: Finding Boundaries and Going Beyond Them
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion
Emergent Corpus Pre-training Benefits Vision Language Models
Jet: A Modern Transformer-Based Normalizing Flow
Variance Reduction of Stochastic Hypergradient Estimation by Mixed Fixed-Point Iteration
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
QDC: Quantum Diffusion Convolution Kernels on Graphs
Conformalized Credal Regions for Classification with Ambiguous Ground Truth
Function Basis Encoding of Numerical Features in Factorization Machines
Piecewise Constant Spectral Graph Neural Network
Continual learning via probabilistic exchangeable sequence modelling
Unmasking Trees for Tabular Data
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Geometrical aspects of lattice gauge equivariant convolutional neural networks
AP: Selective Activation for De-sparsifying Pruned Networks
Pretraining a Neural Operator in Lower Dimensions
Online Tensor Max-Norm Regularization via Stochastic Optimization
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context
Incorporating Unlabelled Data into Bayesian Neural Networks
MarDini: Masked Auto-regressive Diffusion for Video Generation at Scale
DDLP: Unsupervised Object-centric Video Prediction with Deep Dynamic Latent Particles
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality
Minimax Multi-Target Conformal Prediction with Applications to Imaging Inverse Problems
Layerwise complexity-matched learning yields an improved model of cortical area V2
TLDR: Twin Learning for Dimensionality Reduction
Recursive SNE: Fast Prototype-Based t-SNE for Large-Scale and Online Data
Riemann-Lebesgue Forest for Regression
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Cyclophobic Reinforcement Learning
Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
End-to-end Training for Text-to-Image Synthesis using Dual-Text Embeddings
PASS: Pruning Attention Heads with Almost-sure Sparsity Targets
RS-Reg: Probabilistic and Robust Certified Regression through Randomized Smoothing
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State
Policy Gradient with Kernel Quadrature
Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations
Algorithms and Theory for Supervised Gradual Domain Adaptation
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis
Adaptive Hyperparameter Selection for Differentially Private Gradient Descent
Heterogeneous graph adaptive flow network
Unleashing the Power of Visual Prompting At the Pixel Level
Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
Metamorphic Forward Adaptation Network: Dynamically Adaptive and Modular Multi-layer Learning
Data Valuation in the Absence of a Reliable Validation Set
CoNNect: Connectivity-Based Regularization for Structural Pruning of Neural Networks
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation
Unsupervised Domain Adaptation via Minimized Joint Error
Revisiting Hidden Representations in Transfer Learning for Medical Imaging
Tweedie Moment Projected Diffusions for Inverse Problems
Length independent generalization bounds for deep SSM architectures via Rademacher contraction and stability constraints
Recurrent networks, hidden states and beliefs in partially observable environments
Collaboration with Dynamic Open Ad Hoc Team via Team State Modelling
Improved baselines for vision-language pre-training
Towards Understanding Dual BN In Hybrid Adversarial Training
Deconfounding Imitation Learning with Variational Inference
Models of human preference for learning reward functions
Transformers in Uniform TC 0
Sparsified State-Space Models are Efficient Highway Networks
Bias/Variance is not the same as Approximation/Estimation
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-MDP
Prompt Engineering Techniques for Language Model Reasoning Lack Replicability
Weighted L1 and L0 Regularization Using Proximal Operator Splitting Methods
Active Sequential Two-Sample Testing
Test-time Contrastive Concepts for Open-world Semantic Segmentation with Vision-Language Models
Selective Concept Bottleneck Models Without Predefined Concepts
Learning under Imitative Strategic Behavior with Unforeseeable Outcomes
Linear Convergence of Decentralized FedAvg for PL Objectives: The Interpolation Regime
Spectral Clustering and Labeling for Crowdsourcing with Inherently Distinct Task Types
How Far Are We From AGI: Are LLMs All We Need?
SMILE: Sample-to-feature Mixup for Efficient Transfer Learning
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
No $D_{train}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning
Object-aware Cropping for Self-Supervised Learning
MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection
Nomic Embed: Training a Reproducible Long Context Text Embedder
State-wise Constrained Policy Optimization
Fairness Through Matching
Simple and Nearly-Optimal Sampling for Rank-1 Tensor Completion via Gauss-Jordan
Transport with Support: Data-Conditional Diffusion Bridges
Improving Black-box Robustness with In-Context Rewriting
Discrete Graph Auto-Encoder
REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin Splitting
Toward Linearly Regularizing the Geometric Bottleneck of Linear Generalized Attention
Generalizable and Robust Spectral Method for Multi-view Representation Learning
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks
Equivariant Graph Learning for High-density Crowd Trajectories Modeling
Evolution of Discriminator and Generator Gradients in GAN Training: From Fitting to Collapse
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
Variational Disentanglement for Domain Generalization
Concave Utility Reinforcement Learning with Zero-Constraint Violations
GIT-Net: Generalized Integral Transform for Operator Learning
Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems
SEAL: Simultaneous Label Hierarchy Exploration And Learning
Indiscriminate Data Poisoning Attacks on Neural Networks
Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning
A Single Transformer for Scalable Vision-Language Modeling
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error
Semi-Supervised Single Domain Generalization with Label-Free Adversarial Data Augmentation
Combining Machine Learning Defenses without Conflicts
Generalizable Spectral Embedding with an Application to UMAP
Nonlinear Behaviour of Critical Points for a Simple Neural Network
ComFe: An Interpretable Head for Vision Transformers
Separable Operator Networks
To Be Greedy, or Not to Be - That Is the Question for Population Based Training Variants
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
Deep Active Learning in the Open World
Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection
Intrinsic Dimension for Large-Scale Geometric Learning
MC Layer Normalization for calibrated uncertainty in Deep Learning
Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
Contextual Policies Enable Efficient and Interpretable Inverse Reinforcement Learning for Populations
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
Learned Thresholds Token Merging and Pruning for Vision Transformers
A Review of the Applications of Deep Learning-Based Emergent Communication
Robust Hybrid Learning With Expert Augmentation
Interactive Large Language Models for Reliable Answering under Incomplete Context
Solving Robust MDPs through No-Regret Dynamics
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
LEAD: Min-Max Optimization from a Physical Perspective
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions
Federated Learning under Covariate Shifts with Generalization Guarantees
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Predicting the Encoding Error of SIRENs
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness
Ensembles of Classifiers: a Bias-Variance Perspective
An Unconditional Representation of the Conditional Score in Infinite Dimensional Linear Inverse Problems
Pushing the Limits of Gradient Descent for Efficient Learning on Large Images
Transformers trained on proteins can learn to attend to Euclidean distance
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression
Best-of-Both-Worlds Linear Contextual Bandits
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization
Extended Deep Submodular Functions
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations
Personalized Privacy Amplification via Importance Sampling
Towards Unbiased Calibration using Meta-Regularization
Synaptic Interaction Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks
Adapt then Unlearn: Exploring Parameter Space Semantics for Unlearning in Generative Adversarial Networks
Improving Generalization of Complex Models under Unbounded Loss Using PAC-Bayes Bounds
IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation
Distilling Datasets Into Less Than One Image
Repositioning the Subject within Image
From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models
Neural Spatiotemporal Point Processes: Trends and Challenges
Universal Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning
Reproducibility study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework"
DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning
How to choose the right transfer learning protocol? A qualitative analysis in a controlled set-up
Uncovering Sets of Maximum Dissimilarity on Random Process Data
On the effects of similarity metrics in decentralized deep learning under distribution shift
On the Origins of the Block Structure Phenomenon in Neural Network Representations
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration