yusuke1997/dblp-id-title huggingface dataset:
MIM4DD: Mutual Information Maximization for Dataset Distillation
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
Multi-task Gaussian Process Learning of Robot Inverse Dynamics
Subsidiary Prototype Alignment for Universal Domain Adaptation
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks
Improved Algorithms for Neural Active Learning
Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Fair Sortition Made Transparent
Point-Voxel CNN for Efficient 3D Deep Learning
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Receptive Field Formation in Natural Scene Environments: Comparison of Single Cell Learning Rules
Learning Space Partitions for Path Planning
ICEG Morphology Classification using an Analogue VLSI Neural Network
Flight Control in the Dragonfly: A Neurobiological Simulation
Luna: Linear Unified Nested Attention
Learning-Based Low-Rank Approximations
Kernel Change-point Analysis
FilterBoost: Regression and Classification on Large Datasets
The Impact of Initialization on LoRA Finetuning Dynamics
Improving Subgroup Robustness via Data Selection
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances
Distributed-Order Fractional Graph Operating Network
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
Learning the Learning Rate for Prediction with Expert Advice
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Causal vs. Anticausal merging of predictors
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation
MTGS: A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction
Fast Estimation of Causal Interactions using Wold Processes
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
MeshFormer : High-Quality Mesh Generation with 3D-Guided Reconstruction Model
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Mask Propagation for Efficient Video Semantic Segmentation
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
False Discovery Proportion control for aggregated Knockoffs
Fast Variational Inference in the Conjugate Exponential Family
ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation
Scientific Document Retrieval using Multi-level Aspect-based Queries
Residual Alignment: Uncovering the Mechanisms of Residual Networks
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem
The Implicit Metropolis-Hastings Algorithm
LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark
Associating Objects with Transformers for Video Object Segmentation
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Distributed Exploration in Multi-Armed Bandits
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound
TaskBench: Benchmarking Large Language Models for Task Automation
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
REMAP: Recursive Estimation and Maximization of A Posteriori Probabilities - Application to Transition-Based Connectionist Speech Recognition
Sequence learning with hidden units in spiking neural networks
Probability Matching via Deterministic Neural Networks
Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees
Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization
Structured Determinantal Point Processes
Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs
B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Bilinear Attention Networks
The Implications of Local Correlation on Learning Some Deep Functions
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Linear Regression using Heterogeneous Data Batches
Learning to be Smooth: An End-to-End Differentiable Particle Smoother
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games
The Value of Reward Lookahead in Reinforcement Learning
Convergence Rates of Algorithms for Visual Search: Detecting Visual Contours
Bayesian inference via sparse Hamiltonian flows
Quantum Wasserstein Generative Adversarial Networks
A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts
Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
Sparse Polynomial Learning and Graph Sketching
Elucidating the Design Space of Diffusion-Based Generative Models
Semi-supervised Learning via Gaussian Processes
Wasserstein convergence of Cech persistence diagrams for samplings of submanifolds
Segmenting Scenes by Matching Image Composites
Adaptive Discretization for Model-Based Reinforcement Learning
Idiographic Personality Gaussian Process for Psychological Assessment
Variational Distillation of Diffusion Policies into Mixture of Experts
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers
Risk-Driven Design of Perception Systems
APIGen: Automated PIpeline for Generating Verifiable and Diverse Function-Calling Datasets
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
A Framework for Bilevel Optimization on Riemannian Manifolds
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness
Can the Brain Do Backpropagation? - Exact Implementation of Backpropagation in Predictive Coding Networks
Connectionist Models for Auditory Scene Analysis
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
Posterior vs Parameter Sparsity in Latent Variable Models
Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network
Data-Dependent Bounds for Bayesian Mixture Methods
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
A composable machine-learning approach for steady-state simulations on high-resolution grids
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Some Approximation Properties of Projection Pursuit Learning Networks
Chain of Thoughtlessness? An Analysis of CoT in Planning
Pre-Wiring and Pre-Training: What Does a Neural Network Need to Learn Truly General Identity Rules?
Doubly-Robust Lasso Bandit
Latent Graph Inference with Limited Supervision
How to Turn Your Knowledge Graph Embeddings into Generative Models
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Instance-adaptive Zero-shot Chain-of-Thought Prompting
Calibrated Elastic Regularization in Matrix Completion
Predictive Matrix-Variate t Models
A Kernel Loss for Solving the Bellman Equation
Active Learning from Imperfect Labelers
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation
Exploring Token Pruning in Vision State Space Models
Ideal Observers for Detecting Motion: Correspondence Noise
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
Joint MRI Bias Removal Using Entropy Minimization Across Images
Partition-wise Linear Models
Probabilistic Margins for Instance Reweighting in Adversarial Training
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms
The Time-Marginalized Coalescent Prior for Hierarchical Clustering
Oscillatory Neural Fields for Globally Optimal Path Planning
Pointer Networks
A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
First Order Constrained Optimization in Policy Space
Symmetry Discovery Beyond Affine Transformations
Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication
A-NICE-MC: Adversarial Training for MCMC
Absorb & Escape: Overcoming Single Model Limitations in Generating Heterogeneous Genomic Sequences
Batch Bayesian Optimization For Replicable Experimental Design
Hypothesis Transfer Learning via Transformation Functions
Universality and Individuality in a Neural Code
How transferable are features in deep neural networks?
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
On Scalable Testing of Samplers
Learning Treatment Effects in Panels with General Intervention Patterns
A Reduction to Binary Approach for Debiasing Multiclass Datasets
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Boosting Classifier Cascades
Mechanism Design for Collaborative Normal Mean Estimation
Get More at Once: Alternating Sparse Training with Gradient Correction
Stepping on the Edge: Curvature Aware Learning Rate Tuners
Small coresets via negative dependence: DPPs, linear statistics, and concentration
Neural Functional Transformers
Untangling tradeoffs between recurrence and self-attention in artificial neural networks
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
Online sampling from log-concave distributions
Multiagent Planning with Factored MDPs
When Is Inductive Inference Possible?
A Generic Acceleration Framework for Stochastic Composite Optimization
Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance
Bayesian Hierarchical Reinforcement Learning
Lower bounds on the robustness to adversarial perturbations
Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences
Nonlinear directed acyclic structure learning with weakly additive noise models
Efficient learning by implicit exploration in bandit problems with side observations
Fairness with Overlapping Groups; a Probabilistic Perspective
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting
Tracking Changing Stimuli in Continuous Attractor Neural Networks
Towards Efficient 3D Object Detection with Knowledge Distillation
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
Equivariant spatio-hemispherical networks for diffusion MRI deconvolution
Convergence Analysis of Prediction Markets via Randomized Subspace Descent
3D-Aware Scene Manipulation via Inverse Graphics
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL
Learning Appearance Based Models: Mixtures of Second Moment Experts
Minimizing Quadratic Functions in Constant Time
BagFlip: A Certified Defense Against Data Poisoning
Convergence beyond the over-parameterized regime using Rayleigh quotients
Low-Fidelity Video Encoder Optimization for Temporal Action Localization
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies
DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts
Enhancing Robot Program Synthesis Through Environmental Context
Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
Online Learning with Kernels
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
Learning Horizontal Connections in a Sparse Coding Model of Natural Images
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
Towards Neural Programming Interfaces
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations
Performance Measures for Associative Memories that Learn and Forget
Single Image Reflection Separation via Dual-Stream Interactive Transformers
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Augmented Shortcuts for Vision Transformers
Template-Based Algorithms for Connectionist Rule Extraction
Class-Conditional Conformal Prediction with Many Classes
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Counterfactual Temporal Point Processes
Random Projections with Asymmetric Quantization
Single Pass PCA of Matrix Products
Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Temporally Abstract Partial Models
Assessing Generative Models via Precision and Recall
Fast and Simple Spectral Clustering in Theory and Practice
LoFiT: Localized Fine-tuning on LLM Representations
Subquadratic Overparameterization for Shallow Neural Networks
M 2 Hub: Unlocking the Potential of Machine Learning for Materials Discovery
Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
FELM: Benchmarking Factuality Evaluation of Large Language Models
Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning
Online Nonstochastic Model-Free Reinforcement Learning
Variable margin losses for classifier design
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
High-temperature Expansions for Learning Models of Nonnegative Data
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition
Learning Factored Representations for Partially Observable Markov Decision Processes
Hierarchical Normalization for Robust Monocular Depth Estimation
ScaleKD: Strong Vision Transformers Could Be Excellent Teachers
Making Pairwise Binary Graphical Models Attractive
Finite-Time Analysis of Projected Langevin Monte Carlo
On conditional diffusion models for PDE simulations
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Almost Surely Asymptotically Constant Graph Neural Networks
Monte Carlo Sampling for Regret Minimization in Extensive Games
Inverse Reinforcement Learning with Locally Consistent Reward Functions
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
v-Arc: Ensemble Learning in the Presence of Outliers
Deep Active Learning with a Neural Architecture Search
Corruption Robust Active Learning
Bifurcations and loss jumps in RNN training
Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
Adam Can Converge Without Any Modification On Update Rules
Adjust Pearson's $r$ to Measure Arbitrary Monotone Dependence
Solving Sparse \& High-Dimensional-Output Regression via Compression
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Neural Network Simulation of Somatosensory Representational Plasticity
Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network
Consistency Diffusion Bridge Models
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Privately Learning Subspaces
ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification
Saccader: Improving Accuracy of Hard Attention Models for Vision
On Robustness of Principal Component Regression
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
Im-Promptu: In-Context Composition from Image Prompts
EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
Asymptotics of Gradient-based Neural Network Training Algorithms
Re-ranking for image retrieval and transductive few-shot classification
Variational Learning on Aggregate Outputs with Gaussian Processes
Cross-view Geo-localization with Layer-to-Layer Transformer
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors
Time-Data Tradeoffs by Aggressive Smoothing
Language Models as Hierarchy Encoders
A Survey of Modern Questions and Challenges in Feature Extraction
Efficient Deep Approximation of GMMs
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Model-enhanced Vector Index
Predicting human gaze using low-level saliency combined with face detection
Learning Elastic Costs to Shape Monge Displacements
Domain Agnostic Fourier Neural Operators
AirSim Drone Racing Lab
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
A unified framework for information-theoretic generalization bounds
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Extracting Relationships by Multi-Domain Matching
Towards Disentangling Information Paths with Coded ResNeXt
Learning to See Physics via Visual De-animation
CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Generative Hierarchical Materials Search
Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation
TPR: Topology-Preserving Reservoirs for Generalized Zero-Shot Learning
MCMC for continuous-time discrete-state systems
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
Silicon Auditory Processors as Computer Peripherals
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
Dynamic Sparse Network for Time Series Classification: Learning What to "See"
Recognizing Vector Graphics without Rasterization
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought
Fast and Improved SLEX Analysis of High-Dimensional Time Series
Application of Neural Network Methodology to the Modelling of the Yield Strength in a Steel Rolling Plate Mill
PAC-Bayes-Chernoff bounds for unbounded losses
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
SSDM: Scalable Speech Dysfluency Modeling
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint
Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image
Private Isotonic Regression
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
Training Spiking Neural Networks with Event-driven Backpropagation
Modelling Reciprocating Relationships with Hawkes Processes
Structure-Aware Random Fourier Kernel for Graphs
Generative Neural Articulated Radiance Fields
Implicit Regularization for Optimal Sparse Recovery
Learning Representations from Audio-Visual Spatial Alignment
When Combinatorial Thompson Sampling meets Approximation Regret
How to Use Diffusion Priors under Sparse Views?
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction
Revisiting the Evaluation of Image Synthesis with GANs
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
Meta-Curvature
E(n) Equivariant Normalizing Flows
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Clustering the Sketch: Dynamic Compression for Embedding Tables
Factorial Coding of Color in Primary Visual Cortex
Beyond Aesthetics: Cultural Competence in Text-to-Image Models
DePLM: Denoising Protein Language Models for Property Optimization
A Unified Detection Framework for Inference-Stage Backdoor Defenses
DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos
Clustering appearance and shape by learning jigsaws
Tester-Learners for Halfspaces: Universal Algorithms
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Reliable learning in challenging environments
A Theory of Unsupervised Translation Motivated by Understanding Animal Communication
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Benchmarking Distribution Shift in Tabular Data with TableShift
Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary
Nonstochastic Multiarmed Bandits with Unrestricted Delays
On A Mallows-type Model For (Ranked) Choices
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Deep Learning for Predicting Human Strategic Behavior
The Physical Systems Behind Optimization Algorithms
On the Generalization Ability of Online Strongly Convex Programming Algorithms
Improving on Expectation Propagation
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians
MoMu-Diffusion: On Learning Long-Term Motion-Music Synchronization and Correspondence
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Lifted Inference Rules With Constraints
Topography and Ocular Dominance with Positive Correlations
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis