yusuke1997/dblp-id-title huggingface dataset:
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
Sanity Checks for Saliency Maps
Regret of Queueing Bandits
Relating Brain Functional Connectivity to Anatomical Connections: Model Selection
Similarity-based Learning via Data Driven Embeddings
A Connectionist Expert System that Actually Works
Bayesian Layers: A Module for Neural Network Uncertainty
A Bayesian Nonparametric View on Count-Min Sketch
Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Guiding a Diffusion Model with a Bad Version of Itself
Adaptive Online Learning in Dynamic Environments
PAPR: Proximity Attention Point Rendering
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel
Trajectory Diffusion for ObjectGoal Navigation
Video compression dataset and benchmark of learning-based video-quality metrics
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness
Active learning of neural response functions with Gaussian processes
Thought of Search: Planning with Language Models Through The Lens of Efficiency
How to Scale Your EMA
ClusterFomer: Clustering As A Universal Visual Learner
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
k-Means Clustering of Lines for Big Data
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual
Online Clustering of Moving Hyperplanes
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
LightSpeed: Light and Fast Neural Light Fields on Mobile Devices
Scalable Generalized Linear Bandits: Online Computation and Hashing
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Divergences, surrogate loss functions and experimental design
TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models
Don't take it lightly: Phasing optical random projections with unknown operators
On Learning Fairness and Accuracy on Multiple Subgroups
REx: Data-Free Residual Quantization Error Expansion
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in LLMs
Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
Trust Your 𝛁: Gradient-based Intervention Targeting for Causal Discovery
Interactive Submodular Bandit
Bayesian Monte Carlo
Differentially Private n-gram Extraction
Empirical Risk Minimization with Approximations of Probabilistic Grammars
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments
The Unscented Particle Filter
Loss Landscape Characterization of Neural Networks without Over-Parametrization
Road Network Representation Learning with the Third Law of Geography
Rank-DETR for High Quality Object Detection
Neural Network Modeling of Speech and Music Signals
Federated Multi-Task Learning
Fast yet Safe: Early-Exiting with Risk Control
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
Neural Architecture Dilation for Adversarial Robustness
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
COMBO: Conservative Offline Model-Based Policy Optimization
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
The Convergence of Sparsified Gradient Methods
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
Two steps to risk sensitivity
Heuristic Domain Adaptation
Practical and Consistent Estimation of f-Divergences
Selective Integration: A Model for Disparity Estimation
New Liftable Classes for First-Order Probabilistic Inference
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Spectral Methods for Supervised Topic Models
The Tufts fNIRS Mental Workload Dataset & Benchmark for Brain-Computer Interfaces that Generalize
AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation
A Unified Convergence Theorem for Stochastic Optimization Methods
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
Representation Noising: A Defence Mechanism Against Harmful Finetuning
Approximating the Top Eigenvector in Random Order Streams
Context Shift Reduction for Offline Meta-Reinforcement Learning
Image Generation using Continuous Filter Atoms
Understanding and Improving Early Stopping for Learning with Noisy Labels
Whos In the Picture
RoPINN: Region Optimized Physics-Informed Neural Networks
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
How Oscillatory Neuronal Responses Reflect Bistability and Switching of the Hidden Assembly Dynamics
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Approximability of Probability Distributions
Test-Time Personalization with a Transformer for Human Pose Estimation
Learning to Configure Separators in Branch-and-Cut
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Learning DAGs from Data with Few Root Causes
Nash Regret Guarantees for Linear Bandits
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths
Stable Nonconvex-Nonconcave Training via Linear Interpolation
Analogy-Based Reasoning with Memory Networks for Future Prediction
Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learner
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning
Circa: Stochastic ReLUs for Private Deep Learning
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics
Exploring Algorithmic Fairness in Robust Graph Covering Problems
A model of transparent motion and non-transparent motion aftereffects
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training
Modeling Conversational Dynamics as a Mixed-Memory Markov Process
KFNN: K-Free Nearest Neighbor For Crowdsourcing
Long-term Causal Effects via Behavioral Game Theory
Algorithmic stability and generalization of an unsupervised feature selection algorithm
Revenue Optimization with Approximate Bid Predictions
Learning with Incremental Iterative Regularization
Leveraging Clinical Data to Enhance Localization of Brain Atrophy
Learning to Search in Branch and Bound Algorithms
Artemis: Towards Referential Understanding in Complex Videos
Parameter-free HE-friendly Logistic Regression
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach
Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
An Optimization Network for Matrix Inversion
Learning Individually Inferred Communication for Multi-Agent Cooperation
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models
Domain Adaptive Imitation Learning with Visual Observation
What does guidance do? A fine-grained analysis in a simple setting
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Convergence rates of a partition based Bayesian multivariate density estimation method
Message Passing Inference with Chemical Reaction Networks
Learning outside the Black-Box: The pursuit of interpretable models
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions
FP8 Quantization: The Power of the Exponent
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
Designing Application-Specific Neural Networks Using the Genetic Algorithm
Neural Network Analysis of Event Related Potentials and Electroencephalogram Predicts Vigilance
Multiple-Instance Learning via Disjunctive Programming Boosting
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
On the Universality of Online Mirror Descent
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
Fairness-Aware Estimation of Graphical Models
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum
VAST: Value Function Factorization with Variable Agent Sub-Teams
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Levenshtein Transformer
Computational Aspects of Bayesian Persuasion under Approximate Best Response
A Practical, Progressively-Expressive GNN
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
Optimizing Conditional Value-At-Risk of Black-Box Functions
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Efficiently avoiding saddle points with zero order methods: No gradients required
Computing Approximate 𝓁 p Sensitivities
Adaptive Denoising via GainTuning
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Dimensionality Reduction and Prior Knowledge in E-Set Recognition
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Fair Scheduling for Time-dependent Resources
RKHS-SHAP: Shapley Values for Kernel Methods
Random Conic Pursuit for Semidefinite Programming
Generative Retrieval Meets Multi-Graded Relevance
Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
Contextual Grounding of Natural Language Entities in Images
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
How hard is to distinguish graphs with graph neural networks?
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
Continual Learning in Low-rank Orthogonal Subspaces
Semi-crowdsourced Clustering with Deep Generative Models
Exponential Separation between Two Learning Models and Adversarial Robustness
KerGM: Kernelized Graph Matching
Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions
Exponential Family Graph Matching and Ranking
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
VMamba: Visual State Space Model
Theoretical Analysis of Heuristic Search Methods for Online POMDPs
Counterexample-Guided Learning of Monotonic Neural Networks
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
Classification in Non-Metric Spaces
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach
ε-fractional core stability in Hedonic Games
Non-asymptotic Convergence of Training Transformers for Next-token Prediction
Learning feed-forward one-shot learners
Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models
TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control
Towards Robust Detection of Adversarial Examples
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Continuous Latent Process Flows
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network
A Computational Mechanism to Account for Averaged Modified Hand Trajectories
InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion
Stability and Noise in Biochemical Switches
Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Stacked Density Estimation
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Quadratic Quantum Variational Monte Carlo
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms
A Scalable Machine Learning Approach to Go
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
A Unifying Framework for Online Optimization with Long-Term Constraints
3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs
Policy Space Diversity for Non-Transitive Games
Learning Multiagent Communication with Backpropagation
Adaptive Mixture of Probabilistic Transducers
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Pseudo-Spherical Contrastive Divergence
Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
Sample Complexity of Interventional Causal Representation Learning
Risk Sensitive Particle Filters
GUIDE: Real-Time Human-Shaped Agents
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning
Multi-Swap k-Means++
Analysis of Learning from Positive and Unlabeled Data
Active Learning for Parameter Estimation in Bayesian Networks
MobILE: Model-Based Imitation Learning From Observation Alone
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Layer-Adaptive State Pruning for Deep State Space Models
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
MorphTE: Injecting Morphology in Tensorized Embeddings
Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks
Learning non-Markovian Decision-Making from State-only Sequences
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Semi-infinitely Constrained Markov Decision Processes
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes
Self-supervised Transformation Learning for Equivariant Representations
Verifiably Robust Conformal Prediction
A General Purpose Image Processing Chip: Orientation Detection
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
Compositional Visual Generation with Energy Based Models
OctField: Hierarchical Implicit Functions for 3D Modeling
Regularization with Dot-Product Kernels
DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
The Curse of Highly Variable Functions for Local Kernel Machines
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
Bayesian Predictive Profiles With Applications to Retail Transaction Data
Fast Discriminative Visual Codebooks using Randomized Clustering Forests
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Causal Effect Inference for Structured Treatments
Directional convergence and alignment in deep learning
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Single Channel Speech Separation Using Factorial Dynamics
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Multi-Agent Coordination via Multi-Level Communication
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks
On the Representation Power of Set Pooling Networks
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Euclidean Embedding of Co-Occurrence Data
Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
Certified Defenses for Data Poisoning Attacks
Model Complexity, Goodness of Fit and Diminishing Returns
Breaking the curse of dimensionality in structured density estimation
OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking
Easy Learning from Label Proportions
implicit Online Learning with Kernels
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
A Neural Network for Real-Time Signal Processing
Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference
Statistical Convergence of Kernel CCA
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces
Multiclass Total Variation Clustering
On Fairness and Calibration
On the inability of Gaussian process regression to optimally learn compositional functions
Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
Balanced Graph Matching
Utilitarian Algorithm Configuration
Functional Models of Selective Attention and Context Dependency
DPSCREEN: Dynamic Personalized Screening
The Intelligible and Effective Graph Neural Additive Network
On the Tradeoff Between Robustness and Fairness
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
Deep Submodular Functions: Definitions and Learning
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games
Adaptive Batch Size for Safe Policy Gradients
Impossibility Results for Grammar-Compressed Linear Algebra
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
UDC-SIT: A Real-World Dataset for Under-Display Cameras
Circuit Model of Short-Term Synaptic Dynamics
How Does Variance Shape the Regret in Contextual Bandits?
Posterior Collapse of a Linear Latent Variable Model
Bias Detection via Signaling
Fast AutoAugment
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning
The Method of Quantum Clustering
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
TAP Gibbs Free Energy, Belief Propagation and Sparsity
Efficient Training of Retrieval Models using Negative Cache
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Analysis of Temporal-Diffference Learning with Function Approximation
Human Rademacher Complexity
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Coresets for Decision Trees of Signals
GOMAA-Geo: GOal Modality Agnostic Active Geo-localization
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
Ask not what AI can do, but what AI should do: Towards a framework of task delegability
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
L2T-DLN: Learning to Teach with Dynamic Loss Network
Expected Probabilistic Hierarchies
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References
Learning Topology with the Generative Gaussian Graph and the EM Algorithm
Risk Bounds for Randomized Sample Compressed Classifiers
Flexible information routing in neural populations through stochastic comodulation
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
Efficient Minimax Strategies for Square Loss Games
Sparse Overcomplete Latent Variable Decomposition of Counts Data
Static Analysis of Binary Executables Using Structural SVMs
Language Generation in the Limit
Input-Output Equivalence of Unitary and Contractive RNNs
First-Order Methods for Linearly Constrained Bilevel Optimization
Learning Networks of Stochastic Differential Equations
A Theory of Multimodal Learning
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
Robust Learning of Fixed-Structure Bayesian Networks
Low-rank matrix reconstruction and clustering via approximate message passing
Efficient state-space modularization for planning: theory, behavioral and neural signatures
Evaluating Graph Generative Models with Contrastively Learned Features
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
HT-Step: Aligning Instructional Articles with How-To Videos
Error Bounds of Imitating Policies and Environments
Disentangling Linear Quadratic Control with Untrusted ML Predictions
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
Learning the structure of manifolds using random projections
Best Response Regression
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
UniMTS: Unified Pre-training for Motion Time Series
Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space
Invariant Pattern Recognition by Semi-Definite Programming Machines
HyTrel: Hypergraph-enhanced Tabular Data Representation Learning
Induced Model Matching: Restricted Models Help Train Full-Featured Models
Time Warping Invariant Neural Networks
Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records
Recovering Private Text in Federated Learning of Language Models
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA