yusuke1997/dblp-id-title huggingface dataset:
High-Dimensional Optimization in Adaptive Random Subspaces
Natural Language Instruction-following with Task-related Language Development and Translation
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Scatterbrain: Unifying Sparse and Low-rank Attention
Coding of Naturalistic Stimuli by Auditory Midbrain Neurons
Unsupervised Foreground Extraction via Deep Region Competition
Subgame solving without common knowledge
Continuous Surface Embeddings
SIXO: Smoothing Inference with Twisted Objectives
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms
A Closer Look at the Training Strategy for Modern Meta-Learning
Unsupervised Cross-Task Generalization via Retrieval Augmentation
A unified framework for bandit multiple testing
An Analog VLSI Model of Adaptation in the Vestibulo-Ocular Reflex
Large-Scale Prediction of Disulphide Bond Connectivity
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond
Deep Scale-spaces: Equivariance Over Scale
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
HOUDINI: Lifelong Learning as Program Synthesis
ProG: A Graph Prompt Learning Benchmark
Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries
Symmetric Linear Bandits with Hidden Symmetry
Learning to Discover and Detect Objects
Causal Context Adjustment Loss for Learned Image Compression
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
On the Surprising Effectiveness of Attention Transfer for Vision Transformers
Max-Margin Markov Networks
Black-Box Generalization: Stability of Zeroth-Order Learning
Learning and Forecasting Opinion Dynamics in Social Networks
Analysis of Variance of Multiple Causal Networks
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
Modelling and unsupervised learning of symmetric deformable object categories
An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
Generative and Discriminative Learning with Unknown Labeling Bias
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Efficient Neural Network Robustness Certification with General Activation Functions
Solving Decision Problems with Limited Information
Task-Robust Model-Agnostic Meta-Learning
One-bit Supervision for Image Classification
Comparing distributions: 퓁 1 geometry improves kernel two-sample testing
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
Training stochastic stabilized supralinear networks by dynamics-neutral growth
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Graph Clustering: Block-models and model free results
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
End-to-End Ontology Learning with Large Language Models
Pose-Sensitive Embedding by Nonlinear NCA Regression
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
Practical Equivariances via Relational Conditional Neural Processes
Toddler-Inspired Visual Object Learning
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory
Classifying Single Trial EEG: Towards Brain Computer Interfacing
Online Learning with Gaussian Payoffs and Side Observations
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness
How biased are maximum entropy models?
Hand-Object Interaction Image Generation
The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments
Bayesian nonparametric models for bipartite graphs
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
Statistical Knowledge Assessment for Large Language Models
Toward Approaches to Scalability in 3D Human Pose Estimation
Assortment Optimization Under the Mallows model
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
A Privacy-Friendly Approach to Data Valuation
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Contrastive Representation Learning for Electroencephalogram Classification
Rethinking the Backward Propagation for Adversarial Transferability
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra
Federated Linear Contextual Bandits
Learning Deep Bilinear Transformation for Fine-grained Image Representation
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning
Independent Factor Analysis with Temporally Structured Sources
Matrix Completion for Multi-label Image Classification
Untangling in Invariant Speech Recognition
On the Number of Linear Regions of Deep Neural Networks
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
PRODuctive bandits: Importance Weighting No More
HiBug: On Human-Interpretable Model Debug
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation
Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
Hierarchical nucleation in deep neural networks
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
Deep Homomorphism Networks
The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators
The Curse of Unrolling: Rate of Differentiating Through Optimization
E FWI : Multiparameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
Shape Recipes: Scene Representations that Refer to the Image
Efficient Recovery of Jointly Sparse Vectors
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
Byzantine-Tolerant Methods for Distributed Variational Inequalities
Learning to Inpaint for Image Compression
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
Active Matting
QT-ViT: Improving Linear Attention in ViT with Quadratic Taylor Expansion
Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks
Subspace Identification for Multi-Source Domain Adaptation
The Infinite Markov Model
Towards Stable and Robust AdderNets
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
Analysis of Unstandardized Contributions in Cross Connected Networks
Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression
Differentially Private Learning with Margin Guarantees
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
Sheaf Hypergraph Networks
Online Learning Of Neural Computations From Sparse Temporal Feedback
Robust Graph Structure Learning via Multiple Statistical Tests
Interventionally Consistent Surrogates for Complex Simulation Models
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
Infinite Recommendation Networks: A Data-Centric Approach
PAC-Bayes-Empirical-Bernstein Inequality
Algorithm selection by rational metareasoning as a model of human strategy selection
Primitive Manipulation Learning with Connectionism
What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
Adversarially robust learning for security-constrained optimal power flow
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
SketchGen: Generating Constrained CAD Sketches
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions
Optimal integration of visual speed across different spatiotemporal frequency channels
Just One View: Invariances in Inferotemporal Cell Tuning
PEP: Parameter Ensembling by Perturbation
Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
Trajectory balance: Improved credit assignment in GFlowNets
Data Amplification: A Unified and Competitive Approach to Property Estimation
Regularized Softmax Deep Multi-Agent Q-Learning
Agnostic Classification of Markovian Sequences
Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching
Novel positional encodings to enable tree-based transformers
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares
Structured Denoising Diffusion Models in Discrete State-Spaces
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Score-Based Diffusion meets Annealed Importance Sampling
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Phase Transitions in the Pooled Data Problem
Semidefinite Relaxations of the Gromov-Wasserstein Distance
A Hippocampal Model of Recognition Memory
Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
Searching for Low-Bit Weights in Quantized Neural Networks
The Hessian Screening Rule
Recurrent Relational Networks
Simplifying and Empowering Transformers for Large-Graph Representations
Algorithms that Approximate Data Removal: New Results and Limitations
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
The MAGICAL Benchmark for Robust Imitation
The Convergence of Contrastive Divergences
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data
Smoothing Regularizers for Projective Basis Function Networks
Towards Accurate Binary Convolutional Neural Network
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits
Model-free Posterior Sampling via Learning Rate Randomization
Kernel Bayes' Rule
The Met Dataset: Instance-level Recognition for Artworks
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
Efficient Truncated Linear Regression with Unknown Noise Variance
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
On Communication Cost of Distributed Statistical Estimation and Dimensionality
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Learning Bound for Parameter Transfer Learning
Non-Intrusive Gaze Tracking Using Artificial Neural Networks
The Behavior and Convergence of Local Bayesian Optimization
Type-to-Track: Retrieve Any Object via Prompt-based Tracking
From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
Regularization properties of adversarially-trained linear regression
A Neural Network for Motion Detection of Drift-Balanced Stimuli
Code Generation as a Dual Task of Code Summarization
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
Fisher GAN
Privacy Aware Learning
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks
Efficient Principled Learning of Thin Junction Trees
coVariance Neural Networks
Structure Learning for Optimization
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty
Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework
Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis
Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
Universal low-rank matrix recovery from Pauli measurements
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
Batch normalization provably avoids ranks collapse for randomly initialised deep networks
Deep Archimedean Copulas
The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning
Adaptive Online Gradient Descent
Learning to Compose Visual Relations
ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
The Sample Complexity of One-Hidden-Layer Neural Networks
Neural Representation of Multi-Dimensional Stimuli
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Accelerated Mini-batch Randomized Block Coordinate Descent Method
An analysis on negative curvature induced by singularity in multi-layer neural-network learning
Differential Privacy for Growing Databases
Transfer Q-star : Principled Decoding for LLM Alignment
Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
Hebbian Learning of Bayes Optimal Decisions
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
DINTR: Tracking via Diffusion-based Interpolation
Large Language Models Must Be Taught to Know What They Don't Know
Generalization Bounds for Uniformly Stable Algorithms
Online Learning for Multivariate Hawkes Processes
Blossom Tree Graphical Models
Generalisation of A Class of Continuous Neural Networks
HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count
Agglomerative Information Bottleneck
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity
Forecasting Human Trajectory from Scene History
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
A Back-Propagation Algorithm with Optimal Use of Hidden Units
A General Framework for Auditing Differentially Private Machine Learning
Scalable Non-linear Learning with Adaptive Polynomial Expansions
How to Choose an Activation Function
Fast Classification Rates for High-dimensional Gaussian Generative Models
Alias-Free Generative Adversarial Networks
Data subsampling for Poisson regression with pth-root-link
Data Integration for Classification Problems Employing Gaussian Process Priors
Distribution of Mutual Information
CCVS: Context-aware Controllable Video Synthesis
Social Motion Prediction with Cognitive Hierarchies
MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
Margin-Based Algorithms for Information Filtering
Neighbourhood Components Analysis
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
What Can Crowd Computing Do for the Next Generation of AI Systems?
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers
Counterbalancing Learning and Strategic Incentives in Allocation Markets
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
Numerical influence of ReLU'(0) on backpropagation
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Remarks on Interpolation and Recognition Using Neural Nets
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine
TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers
Refined Mechanism Design for Approximately Structured Priors via Active Regression
Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
Oracle Complexity in Nonsmooth Nonconvex Optimization
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification
Generalization in the Face of Adaptivity: A Bayesian Perspective
Statistical Model Criticism using Kernel Two Sample Tests
Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection
Training Data Attribution via Approximate Unrolling
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
Binary Search with Distributional Predictions
Iron: Private Inference on Transformers
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
Optimal Linear Estimation under Unknown Nonlinear Transform
Dynamic Inference with Neural Interpreters
Exponential Family Estimation via Adversarial Dynamics Embedding
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers
Collapsing Bandits and Their Application to Public Health Intervention
Tempo Adaptation in Non-stationary Reinforcement Learning
Gradient Weights help Nonparametric Regressors
Thompson Sampling Efficiently Learns to Control Diffusion Processes
Neuro-symbolic Learning Yielding Logical Constraints
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
Unifying the Sensory and Motor Components of Sensorimotor Adaptation
Understanding the Intrinsic Memorability of Images
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
On the Computational Power of Noisy Spiking Neurons
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Achieving Õ(1/ε) Sample Complexity for Constrained Markov Decision Process
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network
Continual Learning with Deep Generative Replay
Geometric Exploration for Online Control
Are aligned neural networks adversarially aligned?
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval
Axioms for Learning from Pairwise Comparisons
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
MICo: Improved representations via sampling-based state similarity for Markov decision processes
Scalable Global Optimization via Local Bayesian Optimization
High Dimensional Structured Superposition Models
A Domain Decomposition Method for Fast Manifold Learning
Phoneme Recognition with Large Hierarchical Reservoirs
PageRank Bandits for Link Prediction
LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning
Supported Policy Optimization for Offline Reinforcement Learning
A Hitchhiker's Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning
S-STE: Continuous Pruning Function for Efficient 2: 4 Sparse Pre-training
The Large Margin Mechanism for Differentially Private Maximization
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
RD-Suite: A Benchmark for Ranking Distillation
Experiments with Neural Networks for Real Time Implementation of Control
Zero-Shot Anomaly Detection via Batch Normalization
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
SAND: Smooth imputation of sparse and noisy functional data with Transformer networks
Minimal Exploration in Structured Stochastic Bandits
Large Scale Bayes Point Machines
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
Mechanism of Neural Interference by Transcranial Magnetic Stimulation: Network or Single Neuron?
Implicit Variational Inference for High-Dimensional Posteriors
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
PAC-Bayesian Model Selection for Reinforcement Learning
Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games
"Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter"
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
TARSS-Net: Temporal-Aware Radar Semantic Segmentation Network
Margin Semi-Supervised Learning for Structured Variables
Hindsight Credit Assignment
Towards Learning Convolutions from Scratch
Recurrent Cortical Amplification Produces Complex Cell Responses
Variational Gaussian Process State-Space Models
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
A Bayesian Theory of Conformity in Collective Decision Making
The interplay between randomness and structure during learning in RNNs
Primal-Dual Block Generalized Frank-Wolfe
On The Classification-Distortion-Perception Tradeoff
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
Batched Coarse Ranking in Multi-Armed Bandits
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation
Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
A Classification of $G$-invariant Shallow Neural Networks
Woodbury Transformations for Deep Generative Flows