yusuke1997/dblp-id-title huggingface dataset:
Fair Bayes-Optimal Classifiers Under Predictive Parity
Attacks on Online Learners: a Teacher-Student Analysis
Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control
Collaborative Alignment of NLP Models
CrossCT: CNN and Transformer cross–teaching for multimodal image cell segmentation
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
Dendritic Integration Inspired Artificial Neural Networks Capture Data Correlation
An Online Algorithm for Large Scale Image Similarity Learning
Adversarial training for high-stakes reliability
Convexity Certificates from Hessians
Manifold Parzen Windows
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model
Learning to Adapt to Evolving Domains
Repairing Neural Networks by Leaving the Right Past Behind
Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments
Generating steganographic images via adversarial training
Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices
Improving Existing Fault Recovery Policies
Similarity and Discrimination in Classical Conditioning: A Latent Variable Account
Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case
A Novel Two-Step Method for Cross Language Representation Learning
Robust Reinforcement Learning with General Utility
Online Learning for Latent Dirichlet Allocation
NS3: Neuro-symbolic Semantic Code Search
Deep Recursive Neural Networks for Compositionality in Language
Are ResNets Provably Better than Linear Predictors?
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
An Analog Self-Organizing Neural Network Chip
Maximizing Revenue under Market Shrinkage and Market Uncertainty
Who's Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation
HandMeThat: Human-Robot Communication in Physical and Social Environments
Probabilistic amplitude and frequency demodulation
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
Multi-view Contrastive Graph Clustering
Measuring Coding Challenge Competence With APPS
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Hierarchical Methods of Moments
The Quantization Model of Neural Scaling
Towards Harmless Rawlsian Fairness Regardless of Demographic Prior
Beyond Bandit Feedback in Online Multiclass Classification
Implicit Regularization Paths of Weighted Neural Representations
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions
Local Algorithms for Approximate Inference in Minor-Excluded Graphs
SOAR: Improved Indexing for Approximate Nearest Neighbor Search
Directed Graph Contrastive Learning
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
Monocular Dynamic View Synthesis: A Reality Check
Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning
CriticEval: Evaluating Large-scale Language Model as Critic
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
Common-Frame Model for Object Recognition
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Hierarchically Supervised Latent Dirichlet Allocation
Monotonicity Hints
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers
Efficient Structure Learning of Markov Networks using L 1 -Regularization
MotionGPT: Human Motion as a Foreign Language
PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics
Improving self-training under distribution shifts via anchored confidence with theoretical guarantees
LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Control and Rendering
Boosting Density Estimation
Non-Asymptotic Pure Exploration by Solving Games
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints
Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function
Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
PRNet: Self-Supervised Learning for Partial-to-Partial Registration
Active representation learning for general task space with applications in robotics
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
Real-Time Autonomous Robot Navigation Using VLSI Neural Networks
Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA
DiffuserLite: Towards Real-time Diffusion Planning
Optimal Private Median Estimation under Minimal Distributional Assumptions
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Forecasting Spot Oil Price Using Google Probabilities
Respecting Transfer Gap in Knowledge Distillation
Variational Bayesian Stochastic Complexity of Mixture Models
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Coupled Gradient Estimators for Discrete Latent Variables
Feature selection in functional data classification with recursive maxima hunting
Variational Amodal Object Completion
Estimating the Bayes Risk from Sample Data
Whitening Convergence Rate of Coupling-based Normalizing Flows
Attention over Learned Object Embeddings Enables Complex Visual Reasoning
Federated Compositional Deep AUC Maximization
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
Adaptive Nonlinear System Identification with Echo State Networks
Neuron-level Structured Pruning using Polarization Regularizer
Learning abstract structure for drawing by efficient motor program induction
Adversarially Robust Learning with Uncertain Perturbation Sets
Support Vector Method for Novelty Detection
Speedy Q-Learning
Automatic online tuning for fast Gaussian summation
Neural encoding with visual attention
Learning from User Feedback in Image Retrieval Systems
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
SafeWorld: Geo-Diverse Safety Alignment
Statistical and Computational Trade-off in Multi-Agent Multi-Armed Bandits
Foundation Posteriors for Approximate Probabilistic Inference
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Evaluating Approximate Inference in Bayesian Deep Learning
Perceiving the arrow of time in autoregressive motion
Loss function based second-order Jensen inequality and its application to particle variational inference
P 2 C 2 Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics
Analyzing and Visualizing Single-Trial Event-Related Potentials
Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift
Blockwise Parallel Transformers for Large Context Models
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
Optimal Exploration for Model-Based RL in Nonlinear Systems
Performance analysis for L_2 kernel classification
Computing with Finite and Infinite Networks
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier
Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention
Asymptotic Slowing Down of the Nearest-Neighbor Classifier
RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes
Multi-Label Prediction via Sparse Infinite CCA
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
Continual Counting with Gradual Privacy Expiration
Fusion with Diffusion for Robust Visual Tracking
Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
Entropy Rate Estimation for Markov Chains with Large State Space
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
Robust Mean Estimation Without Moments for Symmetric Distributions
A Generalization of Principal Components Analysis to the Exponential Family
Iterative Non-linear Dimensionality Reduction with Manifold Sculpting
2D Observers for Human 3D Object Recognition?
Regularizing AdaBoost
On Controllable Sparse Alternatives to Softmax
DeepPINK: reproducible feature selection in deep neural networks
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
Discriminative Densities from Maximum Contrast Estimation
Communication-Efficient Federated Group Distributionally Robust Optimization
Time-Reversal Symmetric ODE Network
Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
Towards Video Text Visual Question Answering: Benchmark and Baseline
Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
The Pareto Regret Frontier for Bandits
Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie Stimuli
Auditing Local Explanations is Hard
Splatter a Video: Video Gaussian Representation for Versatile Processing
WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
Matching Networks for One Shot Learning
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
On Differentially Private Sampling from Gaussian and Product Distributions
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Continual Learning with Global Alignment
Efficient Lifelong Model Evaluation in an Era of Rapid Progress
Nonlinear Discriminant Analysis Using Kernel Functions
Flow Factorized Representation Learning
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
Causal meets Submodular: Subset Selection with Directed Information
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows
Efficient Relational Learning with Hidden Variable Detection
Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
Reinforcement Learning Based on On-Line EM Algorithm
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
Generalized Nonnegative Matrix Approximations with Bregman Divergences
Stein Variational Gradient Descent as Gradient Flow
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Domain Separation Networks
Accelerated consensus via Min-Sum Splitting
Nonstationary Dual Averaging and Online Fair Allocation
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience
Formulating Discrete Probability Flow Through Optimal Transport
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Multi-View Decision Processes: The Helper-AI Problem
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Constrained Diffusion with Trust Sampling
A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion
Assessing Approximations for Gaussian Process Classification
Distributed Neural Information Processing in the Vestibulo-Ocular System
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models
Concentration Inequalities for the Missing Mass and for Histogram Rule Error
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Learning with Transformation Invariant Kernels
Learning Multi-Agent Communication through Structured Attentive Reasoning
Trading robust representations for sample complexity through self-supervised visual experience
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
Second-order Learning Algorithm with Squared Penalty Term
Online Convex Optimization with Unconstrained Domains and Losses
Beyond Accuracy: Tracking more like Human via Visual Search
Discovering Preference Optimization Algorithms with and for Large Language Models
SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors
Training Compute-Optimal Protein Language Models
Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training
Layer-Neighbor Sampling - Defusing Neighborhood Explosion in GNNs
Linear Relaxations for Finding Diverse Elements in Metric Spaces
Adjoint-Functions and Temporal Learning Algorithms in Neural Networks
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
Unfolding recurrence by Green's functions for optimized reservoir computing
Active Learning of Multi-Index Function Models
Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking
MeCo: Zero-Shot NAS with One Data and Single Forward Pass via Minimum Eigenvalue of Correlation
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Are Transformers more robust than CNNs?
Population Matching Discrepancy and Applications in Deep Learning
Unsupervised Domain Adaptation with Residual Transfer Networks
Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
FLAIR : a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
Sparse Additive Text Models with Low Rank Background
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP
Community Detection via Measure Space Embedding
Integration of Visual and Somatosensory Information for Preshaping Hand in Grasping Movements
Modulated Neural ODEs
Multiple Operator-valued Kernel Learning
On the Variance, Admissibility, and Stability of Empirical Risk Minimization
Planning in entropy-regularized Markov decision processes and games
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
Unconditional stability of a recurrent neural circuit implementing divisive normalization
Control What You Can: Intrinsically Motivated Task-Planning Agent
How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective
Programmable Synaptic Chip for Electronic Neural Networks
Training Language GANs from Scratch
Statistical Guarantees of Distributed Nearest Neighbor Classification
ActAnywhere: Subject-Aware Video Background Generation
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
Modeling Consistency in a Speaker Independent Continuous Speech Recognition System
Inverse Density as an Inverse Problem: the Fredholm Equation Approach
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
High-Dimensional Graphical Model Selection Using ℓ 1 -Regularized Logistic Regression
OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations
A Unified Approach for Learning the Parameters of Sum-Product Networks
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
Active Learning from Weak and Strong Labelers
Triangle Fixing Algorithms for the Metric Nearness Problem
Boosting with Multi-Way Branching in Decision Trees
Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
The CityLearn Challenge 2022: Overview, Results, and Lessons Learned
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Constructive Learning Using Internal Representation Conflicts
DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Adversarial Reweighting for Partial Domain Adaptation
Language Model Alignment with Elastic Reset
Ensemble Clustering using Semidefinite Programming
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
RoMA: Robust Model Adaptation for Offline Model-based Optimization
You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
On the Equivalence between Online and Private Learnability beyond Binary Classification
Geometric Algebra Transformer
Random Projections for $k$-means Clustering
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
M2N: Mesh Movement Networks for PDE Solvers
CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
Discovering Hidden Variables: A Structure-Based Approach
GloptiNets: Scalable Non-Convex Optimization with Certificates
Sparse Features for PCA-Like Linear Regression
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Copula-like Variational Inference
3D Indoor Instance Segmentation in an Open-World
Clustering in Causal Attention Masking
High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction
Induction of Finite-State Automata Using Second-Order Recurrent Networks
Pointwise Tracking the Optimal Regression Function
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent
Why did TD-Gammon Work?
Offline Meta Reinforcement Learning - Identifiability Challenges and Effective Data Collection Strategies
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
Better Private Linear Regression Through Better Private Feature Selection
Model-Powered Conditional Independence Test
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
Rates of Convergence for Large-scale Nearest Neighbor Classification
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Invariant Learning via Probability of Sufficient and Necessary Causes
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
Towards a Scalable Reference-Free Evaluation of Generative Models
Safety Verification of Decision-Tree Policies in Continuous Time
Envy-free Policy Teaching to Multiple Agents
Going beyond persistent homology using persistent homology
OT4P: Unlocking Effective Orthogonal Group Path for Permutation Relaxation
Softmax Deep Double Deterministic Policy Gradients
Multi Dimensional ICA to Separate Correlated Sources
Learning to Synthesize Programs as Interpretable and Generalizable Policies
DiffSF: Diffusion Models for Scene Flow Estimation
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
The Efficiency and the Robustness of Natural Gradient Descent Learning Rule
Differentially Private Statistical Inference through β-Divergence One Posterior Sampling
Recursive Attribute Factoring
Learning Compressed Transforms with Low Displacement Rank
Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization
Towards Multi-Grained Explainability for Graph Neural Networks
Using Expectation to Guide Processing: A Study of Three Real-World Applications
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Learning to Scaffold: Optimizing Model Explanations for Teaching
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
TopicNet: Semantic Graph-Guided Topic Discovery
Deconfounded Representation Similarity for Comparison of Neural Networks
Dual Learning for Machine Translation
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Differentiable Simulation of Soft Multi-body Systems
Middle-Out Decoding
BAN: Detecting Backdoors Activated by Adversarial Neuron Noise
Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments
Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
GNeSF: Generalizable Neural Semantic Fields
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent
Multimodal Learning and Reasoning for Visual Question Answering
EventNarrative: A Large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation