yusuke1997/dblp-id-title huggingface dataset:
RelBench: A Benchmark for Deep Learning on Relational Databases
Transelliptical Graphical Models
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System
Multi-modal Queried Object Detection in the Wild
Efficient Reinforcement Learning by Discovering Neural Pathways
Training Spiking Neural Networks with Local Tandem Learning
Dual Mechanisms for Neural Binding and Segmentation
Sparse Online Learning via Truncated Gradient
Certified Defense to Image Transformations via Randomized Smoothing
Language Through a Prism: A Spectral Approach for Multiscale Language Representations
R-divergence for Estimating Model-oriented Distribution Discrepancy
Good Classification Measures and How to Find Them
Kernel Methods for Implicit Surface Modeling
Scaling and Generalization in Neural Networks: A Case Study
Towards Lower Bounds on the Depth of ReLU Neural Networks
A Randomized Algorithm for Pairwise Clustering
On the Equivalence between Neural Network and Support Vector Machine
Large Pre-trained time series models for cross-domain Time series analysis tasks
Deconvolutional Networks on Graph Data
A sampling-based circuit for optimal decision making
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising
The Importance of Sampling inMeta-Reinforcement Learning
Multiple Cause Vector Quantization
Multi-Task Learning for Contextual Bandits
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
A Better Way to Pretrain Deep Boltzmann Machines
On Efficiency in Hierarchical Reinforcement Learning
Learning a Distance Metric from Relative Comparisons
Spatiotemporal Residual Networks for Video Action Recognition
Learning 1D Causal Visual Representation with De-focus Attention Networks
What Variables Affect Out-of-Distribution Generalization in Pretrained Models?
DataComp: In search of the next generation of multimodal datasets
Learning Higher-Order Graph Structure with Features by Structure Penalty
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data
Ensemble sampling for linear bandits: small ensembles suffice
Convolution Kernels for Natural Language
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
Supervised Bipartite Graph Inference
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
Robust Pre-Training by Adversarial Contrastive Learning
Inference in continuous-time change-point models
DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework
S 2 FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
Efficient Algorithms for Smooth Minimax Optimization
realSEUDO for real-time calcium imaging analysis
Conformalized Fairness via Quantile Regression
Stagewise Training Accelerates Convergence of Testing Error Over SGD
LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis
Studies of a Model for the Development and Regeneration of Eye-Brain Maps
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
VISIT: A Neural Model of Covert Visual Attention
LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
CCCP is Frank-Wolfe in disguise
Low Tensor Rank Learning of Neural Dynamics
Transfer Learning for Latent Variable Network Models
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
A Variational Mean-Field Theory for Sigmoidal Belief Networks
Gradient Information for Representation and Modeling
Radial Basis Function Networks and Complexity Regularization in Function Learning
Beltrami Flow and Neural Diffusion on Graphs
Shaping Visual Representations with Language for Few-shot Classification
Constrained Synthesis with Projected Diffusion Models
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
Byzantine-tolerant federated Gaussian process regression for streaming data
ANPL: Towards Natural Programming with Interactive Decomposition
Keeping Flexible Active Contours on Track using Metropolis Updates
Learning Generalized Linear Programming Value Functions
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
On the Expressive Power of Restricted Boltzmann Machines
Do Less, Get More: Streaming Submodular Maximization with Subsampling
Relational Concept Bottleneck Models
Simple and Fast Distillation of Diffusion Models
Spectral Learning of Shared Dynamics Between Generalized-Linear Processes
Soft-Label Integration for Robust Toxicity Classification
QVAE-Mole: The Quantum VAE with Spherical Latent Variable Learning for 3-D Molecule Generation
Learning the Architecture of Sum-Product Networks Using Clustering on Variables
AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases
Long short-term memory and Learning-to-learn in networks of spiking neurons
Learning from Data of Variable Quality
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
A Critical Comparison of Models for Orientation and Ocular Dominance Columns in the Striate Cortex
Selective Attention for Handwritten Digit Recognition
Adaptive GNN for Image Analysis and Editing
Skill-it! A data-driven skills framework for understanding and training language models
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence
Evaluating and Inducing Personality in Pre-trained Language Models
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
Learning from a Sample in Online Algorithms
Analyzing human feature learning as nonparametric Bayesian inference
What Truly Matters in Trajectory Prediction for Autonomous Driving?
Geodesic Graph Neural Network for Efficient Graph Representation Learning
Ad Auctions for LLMs via Retrieval Augmented Generation
Mixture Matrix Completion
On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories
Forward-backward retraining of recurrent neural networks
Deterministic Annealing Variant of the EM Algorithm
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
On the Convergence of Step Decay Step-Size for Stochastic Optimization
PaCo: Parameter-Compositional Multi-task Reinforcement Learning
Is Early Vision Optimized for Extracting Higher-order Dependencies?
Online Prediction with Selfish Experts
A mixture model for the evolution of gene expression in non-homogeneous datasets
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
WeiPer: OOD Detection using Weight Perturbations of Class Projections
Practical Low-Rank Communication Compression in Decentralized Deep Learning
Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
Spatial Normalized Gamma Processes
Kernelized Sorting
In-Place Zero-Space Memory Protection for CNN
Computerized Adaptive Testing via Collaborative Ranking
Fast Proxy Experiment Design for Causal Effect Identification
An algorithm for L1 nearest neighbor search via monotonic embedding
A Necessary and Sufficient Stability Notion for Adaptive Generalization
Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series
Pruning's Effect on Generalization Through the Lens of Training and Regularization
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning
Distribution Learnability and Robustness
Proceedings of the NIPS 2016 Time Series Workshop, co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, December 9, 2016
Finding and Listing Front-door Adjustment Sets
Event-3DGS: Event-based 3D Reconstruction Using 3D Gaussian Splatting
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
A Connectionist Model of the Owl's Sound Localization System
A Bayesian model for identifying hierarchically organised states in neural population activity
Cone-Constrained Principal Component Analysis
Knowledge-Adaptation Priors
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning
Eigen-Distortions of Hierarchical Representations
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness
Selective inference for group-sparse linear models
Induction of Multiscale Temporal Structure
Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
Guided Policy Search via Approximate Mirror Descent
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
Action-modulated midbrain dopamine activity arises from distributed control policies
Importance Resampling for Off-policy Prediction
Deep Contract Design via Discontinuous Networks
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Faster approximate subgraph counts with privacy
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
Proximal Point Imitation Learning
Stochastic convex optimization with bandit feedback
Consonant Recognition by Modular Construction of Large Phonemic Time-Delay Neural Networks
Lifting Weak Supervision To Structured Prediction
Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments
The limits of squared Euclidean distance regularization
Generalization in multitask deep neural classifiers: a statistical physics approach
RD$^2$: Reward Decomposition with Representation Decomposition
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Inner Product-based Neural Network Similarity
MLP-Mixer: An all-MLP Architecture for Vision
AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning
Global Convergence of Federated Learning for Mixed Regression
Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval
Improvements to the Sequence Memoizer
Multi-view Matrix Factorization for Linear Dynamical System Estimation
ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction
Improving Convergence in Hierarchical Matching Networks for Object Recognition
Self-Critical Reasoning for Robust Visual Question Answering
Online Ad Procurement in Non-stationary Autobidding Worlds
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search
Autonomous Helicopter Flight via Reinforcement Learning
Variational Delayed Policy Optimization
Better Exploration with Optimistic Actor Critic
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
Accelerating Transformers with Spectrum-Preserving Token Merging
Meta-Learning with Self-Improving Momentum Target
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
The Sample-Communication Complexity Trade-off in Federated Q-Learning
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization
Interaction Networks for Learning about Objects, Relations and Physics
On the Limitations of Fractal Dimension as a Measure of Generalization
Fast Model DeBias with Machine Unlearning
Distributed Dual Averaging In Networks
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Lookahead Optimizer: k steps forward, 1 step back
Automorphic Equivalence-aware Graph Neural Network
Efficient Exploration and Value Function Generalization in Deterministic Systems
Adaptive dropout for training deep neural networks
Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box
Advancing Model Pruning via Bi-level Optimization
Learning to Shape In-distribution Feature Space for Out-of-distribution Detection
Phase transitions in when feedback is useful
Training Deep Models Faster with Robust, Approximate Importance Sampling
Matrix Completion from Noisy Entries
An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science
Efficient Submodular Optimization under Noise: Local Search is Robust
A V1 Model of Pop Out and Asymmetty in Visual Search
Causal language modeling can elicit search and reasoning capabilities on logic puzzles
Efficient Statistical Assessment of Neural Network Corruption Robustness
Computing Full Conformal Prediction Set with Approximate Homotopy
Adaptively Exploiting d-Separators with Causal Bandits
Delay and Cooperation in Nonstochastic Linear Bandits
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
Linear Classification and Selective Sampling Under Low Noise Conditions
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
On the Analysis of Multi-Channel Neural Spike Data
Relational Pattern Benchmarking on the Knowledge Graph Link Prediction Task
Directional Pruning of Deep Neural Networks
Using Vocabulary Knowledge in Bayesian Multinomial Estimation
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
Inferring learning rules from animal decision-making
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
Learning Robust Decision Policies from Observational Data
Communication Bounds for the Distributed Experts Problem
PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
Joint 3D Estimation of Objects and Scene Layout
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
A Neuromorphic Multi-chip Model of a Disparity Selective Complex Cell
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Induced Attention Invariance: Defending VQA Models against Adversarial Attacks
A Lyapunov-based Approach to Safe Reinforcement Learning
Learning Chaotic Dynamics in Dissipative Systems
iVideoGPT: Interactive VideoGPTs are Scalable World Models
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
PromptIR: Prompting for All-in-One Image Restoration
Grokking of Implicit Reasoning in Transformers: A Mechanistic Journey to the Edge of Generalization
Optimal Tagging with Markov Chain Optimization
Sublinear Time Low-Rank Approximation of Distance Matrices
Efficient Sketches for Training Data Attribution and Studying the Loss Landscape
Self-Correcting Bayesian Optimization through Bayesian Active Learning
The Use of Classifiers in Sequential Inference
Ultrafast Monte Carlo for Statistical Summations
Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
ST$_k$: A Scalable Module for Solving Top-k Problems
Improving Language Plasticity via Pretraining with Active Forgetting
Deep Structural Causal Models for Tractable Counterfactual Inference
Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning
Near Optimal Sketching of Low-Rank Tensor Regression
Adaptive Cross-Modal Few-shot Learning
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs
Neural Krylov Iteration for Accelerating Linear System Solving
A Complete Variational Tracker
Group Additive Structure Identification for Kernel Nonparametric Regression
Near-Optimal Sample Complexity Bounds for Constrained MDPs
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Temporally Consistent Atmospheric Turbulence Mitigation with Neural Representations
Self-Supervised Bug Detection and Repair
Visualizing the PHATE of Neural Networks
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
Humans Learn Using Manifolds, Reluctantly
Convergent Learning: Do different neural networks learn the same representations?
Dyadic Classification Trees via Structural Risk Minimization
Double Quantization for Communication-Efficient Distributed Optimization
ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
Fair Ranking with Noisy Protected Attributes
DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs
Learning the Solution to the Aperture Problem for Pattern Motion with a Hebb Rule
Learning Interpretable Low-dimensional Representation via Physical Symmetry
Speech Modelling Using Subspace and EM Techniques
A Smoother Way to Train Structured Prediction Models
On Calibration and Out-of-Domain Generalization
Efficient and Parsimonious Agnostic Active Learning
GLIPv2: Unifying Localization and Vision-Language Understanding
Pretraining Representations for Data-Efficient Reinforcement Learning
Structured Transforms for Small-Footprint Deep Learning
PacGAN: The power of two samples in generative adversarial networks
Deep Poisson Factor Modeling
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
Potential-Based Agnostic Boosting
Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm
f-Divergence Variational Inference
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
Self-Distilled Depth Refinement with Noisy Poisson Fusion
SimGen: Simulator-conditioned Driving Scene Generation
Nonnegative Tensor Completion via Integer Optimization
Additive Gaussian Processes
Aligning Model Properties via Conformal Risk Control
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
Generalized Prioritized Sweeping
Shaping the distribution of neural responses with interneurons in a recurrent circuit model
UP-NeRF: Unconstrained Pose Prior-Free Neural Radiance Field
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
Learnability of Linear Thresholds from Label Proportions
Rethinking No-reference Image Exposure Assessment from Holism to Pixel: Models, Datasets and Benchmarks
Who Does What? A Novel Algorithm to Determine Function Localization
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing
Combining Dimensions and Features in Similarity-Based Representations
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
Private Hypothesis Selection
Collecting Telemetry Data Privately
Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions
Hallo3D: Multi-Modal Hallucination Detection and Mitigation for Consistent 3D Content Generation
A Simple Framework for Generalization in Visual RL under Dynamic Scene Perturbations
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Learning Representations for Time Series Clustering
POMDPs in Continuous Time and Discrete Spaces
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy
Learning Large-scale Neural Fields via Context Pruned Meta-Learning
Finite-Sample Maximum Likelihood Estimation of Location
Bayesian Probabilistic Co-Subspace Addition
Gradient Inversion with Generative Image Prior
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control
Multi-Object Hallucination in Vision Language Models
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
Deep Statistical Solvers
Family Discovery
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
Sparse Representation for Signal Classification
Attention-based Neural Cellular Automata
Blind Separation of Filtered Sources Using State-Space Approach
Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Optimization Algorithm Design via Electric Circuits
Distributionally Robust Imitation Learning
Neural Networks Trained to Solve Differential Equations Learn General Representations
Test Time Adaptation via Conjugate Pseudo-labels
Parameterized Explainer for Graph Neural Network
Real-Time and Accurate Self-Supervised Monocular Depth Estimation on Mobile Device
Generalization Properties of Learning with Random Features
BiDM: Pushing the Limit of Quantization for Diffusion Models
Training Methods for Adaptive Boosting of Neural Networks
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system
Deep Learning on a Data Diet: Finding Important Examples Early in Training
Non-linear Metric Learning
LAVIB: A Large-scale Video Interpolation Benchmark
Learning Parametric Sparse Models for Image Super-Resolution
Online Adaptation for Consistent Mesh Reconstruction in the Wild
"Compressive neural representation of sparse, high-dimensional probabilities"
Supervised Learning with Similarity Functions
Sublinear Time Orthogonal Tensor Decomposition
Generative 3D Part Assembly via Dynamic Graph Learning
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
Guiding Large Language Models via Directional Stimulus Prompting
Learning Subsequential Structure in Simple Recurrent Networks
A Real Time Clustering CMOS Neural Engine
The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving