yusuke1997/dblp-id-title huggingface dataset:
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dense Connector for MLLMs
One-Class LP Classifiers for Dissimilarity Representations
Hierarchical Attentive Recurrent Tracking
Structural Causal Bandits: Where to Intervene?
ALPINE: Unveiling The Planning Capability of Autoregressive Learning in Language Models
Recurrent Networks: Second Order Properties and Pruning
Sample Complexity of Posted Pricing for a Single Item
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Guided Similarity Separation for Image Retrieval
Optimizing Generalized Rate Metrics with Three Players
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Identification of Recurrent Patterns in the Activation of Brain Networks
A Systematic Review of NeurIPS Dataset Management Practices
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
Differentially private Bayesian learning on distributed data
RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
Neural Experts: Mixture of Experts for Implicit Neural Representations
Color Constancy by Learning to Predict Chromaticity from Luminance
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
BiT: Robustly Binarized Multi-distilled Transformer
Theoretical guarantees for EM under misspecified Gaussian mixture models
SOPE: Spectrum of Off-Policy Estimators
Robust, Accurate Stochastic Optimization for Variational Inference
AlphaMath Almost Zero: Process Supervision without Process
Tight Rates for Bandit Control Beyond Quadratics
More data means less inference: A pseudo-max approach to structured learning
Drawing out of Distribution with Neuro-Symbolic Generative Models
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
Learning Transferable Features for Implicit Neural Representations
Is Score Matching Suitable for Estimating Point Processes?
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood
MIND dataset for diet planning and dietary healthcare with machine learning: Dataset creation using combinatorial optimization and controllable generation with domain experts
Some New Bounds on the Generalization Error of Combined Classifiers
Tensor Completion Made Practical
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
Conservative Contextual Linear Bandits
A new convergent variant of Q-learning with linear function approximation
Fully Dynamic k-Clustering in Õ(k) Update Time
Improved Sample Complexity for Multiclass PAC Learning
Search for Efficient Large Language Models
On Sparse Canonical Correlation Analysis
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
Powerset Convolutional Neural Networks
Self-Supervised Generation of Spatial Audio for 360° Video
Novel iteration schemes for the Cluster Variation Method
Process-constrained batch Bayesian optimisation
Domain Adaptation under Open Set Label Shift
Minimax Statistical Learning with Wasserstein distances
NAS-Bench-x11 and the Power of Learning Curves
Adaptive Selective Sampling for Online Prediction with Experts
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Towards a fuller understanding of neurons with Clustered Compositional Explanations
Fused Orthogonal Alternating Least Squares for Tensor Clustering
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Higher Order Statistical Decorrelation without Information Loss
Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs
Towards Better Evaluation for Dynamic Link Prediction
Kirchoff Law Markov Fields for Analog Circuit Design
NewTerm: Benchmarking Real-Time New Terms for Large Language Models with Annual Updates
Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization
Percentile Criterion Optimization in Offline Reinforcement Learning
Theoretical Analysis of Weak-to-Strong Generalization
Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
On-line Learning of Dichotomies
Attribution Preservation in Network Compression for Reliable Network Interpretation
Postal Address Block Location Using a Convolutional Locator Network
Neural Spline Flows
On a Connection between Kernel PCA and Metric Multidimensional Scaling
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
How to Describe Neuronal Activity: Spikes, Rates, or Assemblies?
Automatic Annotation of Everyday Movements
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure
Efficient anomaly detection using bipartite k-NN graphs
Learning Statistically Neutral Tasks without Expert Guidance
Networks for the Separation of Sources that Are Superimposed and Delayed
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
An Integer Polynomial Programming Based Framework for Lifted MAP Inference
A perspective on multi-agent communication for information fusion
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Faster Algorithms for User-Level Private Stochastic Convex Optimization
Experiment Planning with Function Approximation
Visual Prompt Tuning in Null Space for Continual Learning
Approximation with CNNs in Sobolev Space: with Applications to Classification
Adaptive Anonymity via b -Matching
Know Thy Neighbour: A Normative Theory of Synaptic Depression
Ensemble Methods for Phoneme Classification
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
Infinite-Dimensional Feature Interaction
Learning to Infer Implicit Surfaces without 3D Supervision
A Systematic Study of the Input/Output Properties of a 2 Compartment Model Neuron With Active Membranes
Using Combinatorial Optimization within Max-Product Belief Propagation
Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
An exploration-exploitation model based on norepinepherine and dopamine activity
Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination
The Power of Hard Attention Transformers on Data Sequences: A formal language theoretic perspective
Speech Recognition with Missing Data using Recurrent Neural Nets
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions
Explore to Generalize in Zero-Shot RL
Wide Two-Layer Networks can Learn from Adversarial Perturbations
A Mixture Model System for Medical and Machine Diagnosis
WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
Activating Self-Attention for Multi-Scene Absolute Pose Regression
Analytically deriving Partial Information Decomposition for affine systems of stable and convolution-closed distributions
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Learning person-object interactions for action recognition in still images
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
Reliable Decisions with Threshold Calibration
Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Linear Time Algorithms for k-means with Multi-Swap Local Search
H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition
Kohonen Feature Maps and Growing Cell Structures - a Performance Comparison
Exploring Conceptual Soundness with TruLens
Learning and using language via recursive pragmatic reasoning about other agents
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
A Neural Network Classifier for the I100 OCR Chip
Deep State Space Models for Unconditional Word Generation
Solving Inverse Physics Problems with Score Matching
A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
Rethinking Neural Operations for Diverse Tasks
KD-Zero: Evolving Knowledge Distiller for Any Teacher-Student Pairs
Long-range Meta-path Search on Large-scale Heterogeneous Graphs
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity
Generalized Fast Exact Conformalization
How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick
Factoring Variations in Natural Images with Deep Gaussian Mixture Models
Probable Domain Generalization via Quantile Risk Minimization
Cross-Modal Learning with Adversarial Samples
AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix
GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes
Multi-Task Learning via Conic Programming
Latent Attention For If-Then Program Synthesis
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling
Online Learning: Random Averages, Combinatorial Parameters, and Learnability
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization
Joint quantile regression in vector-valued RKHSs
Microscopic Equations in Rough Energy Landscape for Neural Networks
Decoupled Self-supervised Learning for Graphs
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
Multi-step learning and underlying structure in statistical models
Beyond IID: data-driven decision-making in heterogeneous environments
Distributionally Robust Graphical Models
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models
A/B/n Testing with Control in the Presence of Subpopulations
Identifying Equivalent Training Dynamics
TopRank: A practical algorithm for online stochastic ranking
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
Certified Adversarial Robustness via Randomized α-Smoothing for Regression Models
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Learning to Explore and Exploit in POMDPs
An Analog VLSI Model of Central Pattern Generation in the Leech
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
Adaptive Choice of Grid and Time in Reinforcement Learning
Continuous Relaxations for Discrete Hamiltonian Monte Carlo
Modelling motion primitives and their timing in biologically executed movements
EyeGraph: Modularity-aware Spatio Temporal Graph Clustering for Continuous Event-based Eye Tracking
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
Escaping the Convex Hull with Extrapolated Vector Machines
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
DUE: End-to-End Document Understanding Benchmark
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
Model-based Reinforcement Learning and the Eluder Dimension
A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction
EcoFormer: Energy-Saving Attention with Linear Complexity
VCT: A Video Compression Transformer
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
Modeling Small Oscillating Biological Networks in Analog VLSI
A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch
Distributionally Robust Bayesian Optimization with φ-divergences
Encoding Spatial Distribution of Convolutional Features for Texture Representation
Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Adaptive Sampling Towards Fast Graph Representation Learning
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
Efficient and Robust Automated Machine Learning
Latency-aware Spatial-wise Dynamic Networks
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Learning Robust Options by Conditional Value at Risk Optimization
Modeling Social Annotation Data with Content Relevance using a Topic Model
Worst-Case Regret Bounds for Exploration via Randomized Value Functions
Order-Invariant Cardinality Estimators Are Differentially Private
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
Metamorphosis Networks: An Alternative to Constructive Models
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Sparse Space-Time Deconvolution for Calcium Image Analysis
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
Sample-Efficient Agnostic Boosting
Boosting Weakly Supervised Referring Image Segmentation via Progressive Comprehension
UniTox: Leveraging LLMs to Curate a Unified Dataset of Drug-Induced Toxicity from FDA Labels
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
Aligning Diffusion Models by Optimizing Human Utility
Posterior Collapse and Latent Variable Non-identifiability
Conditional Synthesis of 3D Molecules with Time Correction Sampler
Towards Playing Full MOBA Games with Deep Reinforcement Learning
DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
Multiple Descent: Design Your Own Generalization Curve
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation
Training Chain-of-Thought via Latent-Variable Inference
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders
LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
Community Detection on Evolving Graphs
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
From Parity to Preference-based Notions of Fairness in Classification
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
A Bayes Rule for Density Matrices
Efficient Active Learning with Abstention
Multiple Threshold Neural Logic
Discrete profile alignment via constrained information bottleneck
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Mental Sampling in Multimodal Representations
Analysing the Generalisation and Reliability of Steering Vectors
Streaming Radiance Fields for 3D Video Synthesis
Progressive Stochastic Binarization of Deep Networks
Precision and Recall for Time Series
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again
Adaptive Hedge
Max-Margin Deep Generative Models
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Barycentric Interpolators for Continuous Space and Time Reinforcement Learning
Improving Inference for Neural Image Compression
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
On Private and Robust Bandits
Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context
Alignment for Honesty
Discretely beyond 1/e: Guided Combinatorial Algortihms for Submodular Maximization
Flexible and accurate inference and learning for deep generative models
Query K-means Clustering and the Double Dixie Cup Problem
Calibrating CNNs for Lifelong Learning
EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals
ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Knowledge Diffusion for Distillation
Approximate Inference A lgorithms for Two-Layer Bayesian Networks
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
KDGAN: Knowledge Distillation with Generative Adversarial Networks
Stochastic Multiple Target Sampling Gradient Descent
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update
Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization
Estimation of the covariance structure of heavy-tailed distributions
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
Learning Mackey-Glass from 25 Examples, Plus or Minus 2
A Note on Sparse Generalized Eigenvalue Problem
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks
Learning Continuous Control Policies by Stochastic Value Gradients
Automatic Integration for Spatiotemporal Neural Point Processes
The Complexity of Bayesian Network Learning: Revisiting the Superstructure
Certifying Confidence via Randomized Smoothing
Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics
Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error
Note on Development of Modularity in Simple Cortical Models
Pulsestream Synapses with Non-Volatile Analogue Amorphous-Silicon Memories
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
Collaboratively Learning Preferences from Ordinal Data
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
Sorting out typicality with the inverse moment matrix SOS polynomial
SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
Dilated Recurrent Neural Networks
Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations
PhyRecon: Physically Plausible Neural Scene Reconstruction
Soft Clustering on Graphs
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body
SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting
Video Annotation and Tracking with Active Learning
Are Graph Neural Networks Optimal Approximation Algorithms?
Semi-Parametric Efficient Policy Learning with Continuous Actions
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
Latent Matters: Learning Deep State-Space Models
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
Learning to Model the Tail
Estimators for Multivariate Information Measures in General Probability Spaces
Learning Debiased Representation via Disentangled Feature Augmentation
Transformers over Directed Acyclic Graphs
Gaussian Process Models for Link Analysis and Transfer Learning
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Linear Stochastic Bandits Under Safety Constraints
Efficient Methods for Non-stationary Online Learning
Unrolled denoising networks provably learn to perform optimal Bayesian inference
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
Amortized Inference for Causal Structure Learning
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
On Transductive Regression
A theory of weight distribution-constrained learning
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems
Hard ImageNet: Segmentations for Objects with Strong Spurious Cues
Analysis of Robust PCA via Local Incoherence
ETNet: Error Transition Network for Arbitrary Style Transfer
The Recurrent Temporal Restricted Boltzmann Machine
Representation Learning for Event-based Visuomotor Policies
Temporal Variability in Implicit Online Learning
Signal Detection in Noisy Weakly-Active Dendrites
Feature Set Embedding for Incomplete Data
Relationship Prompt Learning is Enough for Open-Vocabulary Semantic Segmentation
Mixture Regression for Covariate Shift
Statistical and Computational Trade-Offs in Kernel K-Means
On Optimal Learning Under Targeted Data Poisoning
A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
Learning What and Where to Draw
Nonparametric Identifiability of Causal Representations from Unknown Interventions
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
Structured Estimation with Atomic Norms: General Bounds and Applications
Learning Bayesian Networks with Low Rank Conditional Probability Tables
GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
Augmentation-free Dense Contrastive Distillation for Efficient Semantic Segmentation
AND: Adversarial Neural Degradation for Learning Blind Image Super-Resolution
Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs
Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization
Estimating Conditional Probability Densities for Periodic Variables