yusuke1997/dblp-id-title huggingface dataset:
PlanE: Representation Learning over Planar Graphs
A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
RELLISUR: A Real Low-Light Image Super-Resolution Dataset
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
GAR: Generalized Autoregression for Multi-Fidelity Fusion
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
LLM Evaluators Recognize and Favor Their Own Generations
TD(0) Leads to Better Policies than Approximate Value Iteration
Efficient Exact Inference in Planar Ising Models
What can a Single Attention Layer Learn? A Study Through the Random Features Lens
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
Online learning in episodic Markovian decision processes by relative entropy policy search
P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting
Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion
Kodak Imagelink TM OCR Alphanumeric Handprint Module
DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Deep Learning of Invariant Features via Simulated Fixations in Video
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
MyoChallenge 2022: Learning contact-rich manipulation using a musculoskeletal hand
Speeding up the Parti-Game Algorithm
Banach Wasserstein GAN
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
Generalized 2 Linear 2 Models
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
Rule Based Rewards for Language Model Safety
Backpropagation for Energy-Efficient Neuromorphic Computing
OpenML Benchmarking Suites
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
Portmanteau Vocabularies for Multi-Cue Image Representation
Learning to Prove Theorems by Learning to Generate Theorems
Space-time Mixing Attention for Video Transformer
Neur2BiLO: Neural Bilevel Optimization
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization
The Doubly Balanced Network of Spiking Neurons: A Memory Model with High Capacity
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
MotionBooth: Motion-Aware Customized Text-to-Video Generation
Gradient Boosted Normalizing Flows
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Bayesian Batch Active Learning as Sparse Subset Approximation
Two-Stream Convolutional Networks for Action Recognition in Videos
Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers
Convergence guarantees for kernel-based quadrature rules in misspecified settings
Fast, smooth and adaptive regression in metric spaces
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
On Leave-One-Out Conditional Mutual Information For Generalization
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns
Adaptive Discriminative Generative Model and Its Applications
Characterization and Learning of Causal Graphs with Small Conditioning Sets
Learning Invariant Molecular Representation in Latent Discrete Space
The Hopfield Model with Multi-Level Neurons
Generative Affine Localisation and Tracking
A Dual Algorithm for Olfactory Computation in the Locust Brain
Learning to Assist Humans without Inferring Rewards
Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games
BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Extracting Interpretable Models from Matrix Factorization Models
Multi-Scale Adaptive Network for Single Image Denoising
Robustness to Label Noise Depends on the Shape of the Noise Distribution
Towards Deeper Deep Reinforcement Learning with Spectral Normalization
Continual Auxiliary Task Learning
A Mathematical Model of Axon Guidance by Diffusible Factors
A Weighted Probabilistic Neural Network
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis
Factorial LDA: Sparse Multi-Dimensional Text Models
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Shifted Chunk Transformer for Spatio-Temporal Representational Learning
Towards Theoretically Inspired Neural Initialization Optimization
GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
Autodecoding Latent 3D Diffusion Models
Robust Neural Network Regression for Offline and Online Learning
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
Accelerating Certified Robustness Training via Knowledge Transfer
Identifying Dendritic Processing
Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba
A Novel Approach for Constrained Optimization in Graphical Models
Band-Limited Gaussian Processes: The Sinc Kernel
Connective Cognition Network for Directional Visual Commonsense Reasoning
Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games
Structured Graph Learning Via Laplacian Spectral Constraints
Single-Stage Visual Relationship Learning using Conditional Queries
Kernel Quadrature with Randomly Pivoted Cholesky
A New Learning Algorithm for Blind Signal Separation
Precise asymptotic generalization for multiclass classification with overparameterized linear models
FAMO: Fast Adaptive Multitask Optimization
Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond
Iterative Neural Autoregressive Distribution Estimator NADE-k
Splines, Rational Functions and Neural Networks
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Addressing Failure Prediction by Learning Model Confidence
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
A Spectral Theory of Neural Prediction and Alignment
Shifting Weights: Adapting Object Detectors from Image to Video
Where to Pay Attention in Sparse Training for Feature Selection?
Gradual Domain Adaptation without Indexed Intermediate Domains
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging
On Non-Linear operators for Geometric Deep Learning
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
ARMA Nets: Expanding Receptive Field for Dense Prediction
Test-Time Training with Masked Autoencoders
A Formulation for Minimax Probability Machine Regression
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Tonal Music as a Componential Code: Learning Temporal Relationships between and within Pitch and Timing Components
Reverse engineering learned optimizers reveals known and novel mechanisms
Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects?
Dynamics of Training
Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking
Investment Learning with Hierarchical PSOMs
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Point process models for sequence detection in high-dimensional neural spike trains
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Conditional Meta-Learning of Linear Representations
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning
HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Advice Querying under Budget Constraint for Online Algorithms
Local Bayesian optimization via maximizing probability of descent
Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm
Sample Efficient Path Integral Control under Uncertainty
Online Metric Learning and Fast Similarity Search
ICA-based Clustering of Genes from Microarray Expression Data
Fast Abductive Learning by Similarity-based Consistency Optimization
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion
Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Tsetlin Machine for Solving Contextual Bandit Problems
Reinforcement Learning under Model Mismatch
Implicit encoding of prior probabilities in optimal neural populations
Combining ICA and Top-Down Attention for Robust Speech Recognition
MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Instance-Conditioned GAN
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
The Infinite Hierarchical Factor Regression Model
Large-Scale Sparse Principal Component Analysis with Application to Text Data
t-logistic regression
Lattice partition recovery with dyadic CART
Conservative Q-Learning for Offline Reinforcement Learning
GlyphControl: Glyph Conditional Control for Visual Text Generation
Instance-Based Relevance Feedback for Image Retrieval
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Adapting to function difficulty and growth conditions in private optimization
Bayesian Sparse Factor Models and DAGs Inference and Comparison
Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
Optimizing Cortical Mappings
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Explicit Explore-Exploit Algorithms in Continuous State Spaces
FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Parameter Inference with Bifurcation Diagrams
Sample Propagation
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
Semantic Exploration from Language Abstractions and Pretrained Representations
Mitigating the Effect of Incidental Correlations on Part-based Learning
A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector Quantiser
Discovering Discrete Distributed Representations
Bayesian Exponential Family PCA
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
On ranking via sorting by estimated expected utility
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
Preconditioned Spectral Descent for Deep Learning
Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection
Label Delay in Online Continual Learning
ViSt3D: Video Stylization with 3D CNN
Directional-Unit Boltzmann Machines
Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC
Expectation Particle Belief Propagation
Reference-Based POMDPs
A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
Reducing the variance in online optimization by transporting past gradients
Modeling and Optimization Trade-off in Meta-learning
Stein Π-Importance Sampling
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
Clamping Variables and Approximate Inference
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Pixels to Graphs by Associative Embedding
A simple normative network approximates local non-Hebbian learning in the cortex
On the Interplay between Social Welfare and Tractability of Equilibria
Quantized Variational Inference
Trace Lasso: a trace norm regularization for correlated designs
Maximal Margin Labeling for Multi-Topic Text Categorization
Graph Denoising Diffusion for Inverse Protein Folding
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Graphical Generative Adversarial Networks
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD
Non-convex online learning via algorithmic equivalence
Fast and Memory-Efficient Video Diffusion Using Streamlined Inference
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $\alpha$-NeuS
"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders"
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
High Order Neural Networks for Efficient Associative Memory Design
OneActor: Consistent Subject Generation via Cluster-Conditioned Guidance
Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Improved Sample Complexity Bounds for Diffusion Model Training
Semi-supervised Sequence Learning
No-regret Learning in Price Competitions under Consumer Reference Effects
CrowdSpeech and Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription
A first-order primal-dual method with adaptivity to local smoothness
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks
Re-evaluating evaluation
Hypersolvers: Toward Fast Continuous-Depth Models
Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Unsupervised Learning From Incomplete Measurements for Inverse Problems
Robust Portfolio Optimization
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene
Bayesian Inference of Temporal Task Specifications from Demonstrations
On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
Interfacing Foundation Models' Embeddings
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy
Curriculum Learning for Vision-and-Language Navigation
Sequential Adaptation of Radial Basis Function Networks
Bayesian Belief Polarization
Explanation-Based Neural Network Learning for Robot Control
Do causal predictors generalize better to new domains?
Monash Time Series Forecasting Archive
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness
Prediction and Change Detection
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Integrated perception with recurrent multi-task neural networks
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training
Dynamic Stochastic Synapses as Computational Units
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL
Co-Tuning for Transfer Learning
Adaptive Conformal Inference Under Distribution Shift
IllumiNeRF: 3D Relighting Without Inverse Rendering
Binarized Diffusion Model for Image Super-Resolution
Gradient Rewiring for Editable Graph Neural Network Training
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Active Learning of Model Evidence Using Bayesian Quadrature
Diffused Task-Agnostic Milestone Planner
Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models
Approximation algorithms for stochastic clustering
A Compositional Atlas for Algebraic Circuits
BertaQA: How Much Do Language Models Know About Local Culture?
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision
Fast Sparse Group Lasso
Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling
Network Pruning via Transformable Architecture Search
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
An Efficient Framework for Clustered Federated Learning
Bandits with Knapsacks beyond the Worst Case
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Backpropagation-Friendly Eigendecomposition
Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement
VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors
Combinatorial Bandits Revisited
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation
Heavy-Tailed Symmetric Stochastic Neighbor Embedding
Bootstrapping Top-down Information for Self-modulating Slot Attention
Baselines for Chest X-Ray Report Generation
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Explicitly disentangling image content from translation and rotation with spatial-VAE
PointCNN: Convolution On X-Transformed Points
Diverse Shape Completion via Style Modulated Generative Adversarial Networks
Geometric Dataset Distances via Optimal Transport
Mutual exclusivity as a challenge for deep neural networks
FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
A Bayesian Spatial Scan Statistic
Across-animal odor decoding by probabilistic manifold alignment
ReMI: A Dataset for Reasoning with Multiple Images
On Learning and Refutation in Noninteractive Local Differential Privacy
No-regret Online Learning over Riemannian Manifolds
Cocktail Party Processing via Structured Prediction
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Optimal Order Simple Regret for Gaussian Process Bandits
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
How Linear are Auditory Cortical Responses?
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method
Variational Annealing on Graphs for Combinatorial Optimization
Fast Instrument Learning with Faster Rates
Human-Guided Complexity-Controlled Abstractions
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation
SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt Types
Exploring Geometry of Blind Spots in Vision models
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
Approximating Concavely Parameterized Optimization Problems
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
Extending Video Masked Autoencoders to 128 frames
PAC Learning Linear Thresholds from Label Proportions
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Spontaneous and Information-Triggered Segments of Series of Human Brain Electric Field Maps
Deep Energy-based Modeling of Discrete-Time Physics
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
Do Input Gradients Highlight Discriminative Features?
On Elicitation Complexity
Towards Conceptual Compression
Understanding and Improving Layer Normalization
Implicit Regularization of Accelerated Methods in Hilbert Spaces
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
β-DPO: Direct Preference Optimization with Dynamic β
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers
Interpretable Prototype-based Graph Information Bottleneck
Efficiency of the First-Price Auction in the Autobidding World
Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers
Convex Relaxation of Mixture Regression with Efficient Algorithms
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
A Deep Architecture for Matching Short Texts
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
A General Framework for Symmetric Property Estimation
CompRess: Self-Supervised Learning by Compressing Representations
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Open Rule Induction
Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding
What are the best Systems? New Perspectives on NLP Benchmarking
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
GREED: A Neural Framework for Learning Graph Distance Functions
A Statistical Online Inference Approach in Averaged Stochastic Approximation
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Salient Boundary Detection using Ratio Contour
Benchmarking Deep Learning Interpretability in Time Series Predictions
Learning via Wasserstein-Based High Probability Generalisation Bounds
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
Learning to Decouple the Lights for 3D Face Texture Modeling