yusuke1997/dblp-id-title huggingface dataset:
Simulation-guided Beam Search for Neural Combinatorial Optimization
Beyond Mahalanobis Distance for Textual OOD Detection
From Tempered to Benign Overfitting in ReLU Neural Networks
Semi-Supervised Learning in Gigantic Image Collections
Differentially Private Image Classification by Learning Priors from Random Processes
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms
On-line Learning from Finite Training Sets in Nonlinear Networks
A Statistical Decision-Theoretic Framework for Social Choice
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights
Hypergraph Propagation and Community Selection for Objects Retrieval
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models
Full-Capacity Unitary Recurrent Neural Networks
Structured Recognition for Generative Models with Explaining Away
The Electrotonic Transformation: a Tool for Relating Neuronal Form to Function
Analysis of one-hidden-layer neural networks via the resolvent method
Risk-averse Heteroscedastic Bayesian Optimization
The Storage Capacity of a Fully-Connected Committee Machine
Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30 - December 3, 1992]
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
Policy Mirror Descent with Lookahead
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
Probabilistic Computation in Spiking Populations
On the Power of Decision Trees in Auto-Regressive Language Modeling
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
Nonparametric Bayesian inference on multivariate exponential families
Co-evolution Transformer for Protein Contact Prediction
Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds
Decompose a Task into Generalizable Subtasks in Multi-Agent Reinforcement Learning
Knowledge Distillation by On-the-Fly Native Ensemble
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Joint-task Self-supervised Learning for Temporal Correspondence
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
Similarity and Matching of Neural Network Representations
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Metric-Free Individual Fairness in Online Learning
Relax and Randomize : From Value to Algorithms
SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning
Distinguishing Learning Rules with Brain Machine Interfaces
Accelerating Value Iteration with Anchoring
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning
Contextual Gaussian Process Bandits with Neural Networks
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
Active Invariant Causal Prediction: Experiment Selection through Stability
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Exploring through Random Curiosity with General Value Functions
Compositional Generalization by Learning Analytical Expressions
Convergence of Laplacian Eigenmaps
On Generalization Bounds for Projective Clustering
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
IQ-Learn: Inverse soft-Q Learning for Imitation
On the Role of Randomization in Adversarially Robust Classification
Poisson-Randomized Gamma Dynamical Systems
Information-theoretic analysis of generalization capability of learning algorithms
Reinforcement Learning with Long Short-Term Memory
Abductive Reasoning in Logical Credal Networks
Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering
Vector Quantization Prompting for Continual Learning
Patching open-vocabulary models by interpolating weights
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
What makes unlearning hard and what to do about it
Introspective Distillation for Robust Question Answering
Periodic Graph Transformers for Crystal Material Property Prediction
Metric Transforms and Low Rank Representations of Kernels for Fast Attention
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Data augmentation for efficient learning from parametric experts
Iteratively Learn Diverse Strategies with State Distance Information
Latent Functional Maps: a spectral framework for representation alignment
Learning Optimal Flows for Non-Equilibrium Importance Sampling
A Classification-based Cocktail-party Processor
GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Distributed Delayed Stochastic Optimization
Cardinality-Regularized Hawkes-Granger Model
The Fast Convergence of Boosting
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback
Singleshot : a scalable Tucker tensor decomposition
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
Active Vision Reinforcement Learning under Limited Visual Observability
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
Posterior Meta-Replay for Continual Learning
Early Stopping for Nonparametric Testing
Equal Opportunity in Online Classification with Partial Feedback
Combining Visual and Acoustic Speech Signals with a Neural Network Improves Intelligibility
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Worst-Case Bounds for Gaussian Process Models
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
Realistic evaluation of transductive few-shot learning
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
PHOTOSWAP: Personalized Subject Swapping in Images
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
Correlation Robust Influence Maximization
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
The Recurrent Cascade-Correlation Architecture
Analyzing Vision Transformers for Image Classification in Class Embedding Space
Projecting Ising Model Parameters for Fast Mixing
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns
Online Learning of Dynamic Parameters in Social Networks
LaSCal: Label-Shift Calibration without target labels
Model-based Policy Optimization with Unsupervised Model Adaptation
High-Quality Self-Supervised Deep Image Denoising
Approximate Solutions to Optimal Stopping Problems
Scalable Rule-Based Representation Learning for Interpretable Classification
Fast Rates for Regularized Objectives
Random Walk Approach to Regret Minimization
Learning Active Learning from Data
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Accelerating Stochastic Composition Optimization
Accelerated Regularized Learning in Finite N-Person Games
Convex Methods for Transduction
MedCalc-Bench: Evaluating Large Language Models for Medical Calculations
Fearless Stochasticity in Expectation Propagation
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Does mitigating ML's impact disparity require treatment disparity?
A Bayesian Approach to Concept Drift
Tighter Expected Generalization Error Bounds via Wasserstein Distance
Cryptographic Hardness of Learning Halfspaces with Massart Noise
Sublinear Algorithms for Hierarchical Clustering
Exploring Fixed Point in Image Editing: Theoretical Support and Convergence Optimization
Image2Struct: Benchmarking Structure Extraction for Vision-Language Models
A Multiscale Adaptive Network Model of Motion Computation in Primates
Stochastic Deep Gaussian Processes over Graphs
Anti-efficient encoding in emergent communication
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
BayesTune: Bayesian Sparse Deep Model Fine-tuning
A Regularized Framework for Sparse and Structured Neural Attention
Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
RecursiveMix: Mixed Learning with History
Comparative Gene Prediction using Conditional Random Fields
Unsupervised Attention-guided Image-to-Image Translation
Attractor Dynamics with Synaptic Depression
Boolean Decision Rules via Column Generation
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model
Computing with Action Potentials
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
Computing the Solution Path for the Regularized Support Vector Regression
OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
Learn what matters: cross-domain imitation learning with task-relevant embeddings
Summary Statistics for Partitionings and Feature Allocations
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification
On Component Interactions in Two-Stage Recommender Systems
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Learning Symmetric Rules with SATNet
Improving robustness against common corruptions by covariate shift adaptation
Doubly Stochastic Normalization for Spectral Clustering
The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations
A High-Resolution Dataset for Instance Detection with Multi-View Object Capture
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments
Language Model Tokenizers Introduce Unfairness Between Languages
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis
Selecting Diverse Features via Spectral Regularization
Learning Generalized Gumbel-max Causal Mechanisms
Personalizing Many Decisions with High-Dimensional Covariates
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
Neural Networks with Small Weights and Depth-Separation Barriers
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
Sidestepping Intractable Inference with Structured Ensemble Cascades
Efficiently Identifying Task Groupings for Multi-Task Learning
How do Large Language Models Handle Multilingualism?
LoQT: Low-Rank Adapters for Quantized Pretraining
On Scrambling Phenomena for Randomly Initialized Recurrent Networks
Approximate Linear Programming for Average-Cost Dynamic Programming
A Reduced-Dimension fMRI Shared Response Model
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
Towards Robust and Reliable Algorithmic Recourse
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
Defending Neural Backdoors via Generative Distribution Modeling
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy
Neural Basis of Object-Centered Representations
Learning Multi-Class Dynamics
Evolving Connectivity for Recurrent Spiking Neural Networks
Software for ANN Training on a Ring Array Processor
A Class of Algorithms for General Instrumental Variable Models
SpAM: Sparse Additive Models
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Optimal Signalling in Attractor Neural Networks
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
Deep Contextual Video Compression
On the Power of Edge Independent Graph Models
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
An Algorithm to Learn Polytree Networks with Hidden Nodes
Approximate Cross-Validation for Structured Models
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
Conditional Neural Fields
Active learning of neural population dynamics using two-photon holographic optogenetics
Resolution Limits of Sparse Coding in High Dimensions
Controllable and Compositional Generation with Latent-Space Energy-Based Models
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Are Hopfield Networks Faster than Conventional Computers?
TIES-Merging: Resolving Interference When Merging Models
HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data
When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models
Controling the Hyperparameter Search in MacKay's Bayesian Neural Network Framework
Efficient estimation of hidden state dynamics from spike trains
Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples
Structured Federated Learning through Clustered Additive Modeling
Geometric Neural Diffusion Processes
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
Wiring Optimization in the Brain
DualNet: Continual Learning, Fast and Slow
Responding to Modalities with Different Latencies
Perceptual Fairness in Image Restoration
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning
Image-to-image translation for cross-domain disentanglement
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
Robust Knowledge Transfer in Tiered Reinforcement Learning
Causal Dependence Plots
A Model for Learned Bloom Filters and Optimizing by Sandwiching
Boosted CVaR Classification
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization
Locally Hierarchical Auto-Regressive Modeling for Image Generation
Distributionally Robust Optimization and Generalization in Kernel Methods
Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
Dueling Bandits with Adversarial Sleeping
Correcting Sample Selection Bias by Unlabeled Data
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Practical Characteristics of Neural Network and Conventional Pattern Classifiers on Artificial and Speech Problems
Deriving Receptive Fields Using an Optimal Encoding Criterion
Online Control for Meta-optimization
ChatQA: Surpassing GPT-4 on Conversational QA and RAG
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
Fine-grained Expressivity of Graph Neural Networks
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Language Modeling with Recurrent Highway Hypernetworks
Generative Fractional Diffusion Models
Designing smoothing functions for improved worst-case competitive ratio in online optimization
Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
Adversarial Intrinsic Motivation for Reinforcement Learning
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds
Perceptual Organization Based on Temporal Dynamics
Streaming Bayesian Inference for Crowdsourced Classification
Transform-invariant Image Decomposition with Similarity Templates
Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
Benchmarking Large Language Models on CMExam - A comprehensive Chinese Medical Exam Dataset
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Hold me tight! Influence of discriminative features on deep network boundaries
Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing
Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis
Off-Policy Evaluation with Policy-Dependent Optimization Response
Sequence to Sequence Learning with Neural Networks
Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model
Robust Online Correlation Clustering
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
Diffeomorphic Temporal Alignment Nets
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
Generative Local Metric Learning for Nearest Neighbor Classification
Learning with ensembles: How overfitting can be useful
Multi-modal Transfer Learning between Biological Foundation Models
Relational Knowledge Extraction from Neural Networks
Generalisation in humans and deep neural networks
Massively scalable Sinkhorn distances via the Nyström method
Heavy-tailed Distances for Gradient Based Image Descriptors
A Unified Approach to Fair Online Learning via Blackwell Approachability
Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression
A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics
Feature Adaptation for Sparse Linear Regression
Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets
Multiparameter Persistence Image for Topological Machine Learning
FedSplit: an algorithmic framework for fast federated optimization
Decomposition of Reinforcement Learning for Admission Control of Self-Similar Call Arrival Processes
Making Non-Stochastic Control (Almost) as Easy as Stochastic
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
The Many Faces of Optimal Weak-to-Strong Learning
MotionCraft: Physics-Based Zero-Shot Video Generation
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Motion Transformer with Global Intention Localization and Local Movement Refinement
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
Tuning Multi-mode Token-level Prompt Alignment across Modalities
Professor Forcing: A New Algorithm for Training Recurrent Networks
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
MLFMF: Data Sets for Machine Learning for Mathematical Formalization
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
Closed-form Estimators for High-dimensional Generalized Linear Models
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning
Variational bounds for mixed-data factor analysis
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning
Classification with Valid and Adaptive Coverage
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search
The Bethe Partition Function of Log-supermodular Graphical Models
OnACID: Online Analysis of Calcium Imaging Data in Real Time
Learning Formal Mathematics From Intrinsic Motivation
Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection
SIREN: Shaping Representations for Detecting Out-of-Distribution Objects
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets
A Data-Driven Approach to Modeling Choice
Nonparametric Reduced Rank Regression
On Slicing Optimality for Mutual Information
Dual Variational Generation for Low Shot Heterogeneous Face Recognition
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective
Norm-based Generalization Bounds for Sparse Neural Networks
DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
Clustered Multi-Task Learning Via Alternating Structure Optimization
Bayesian Learning via Q-Exponential Process
A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems
Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning
Discrete Rényi Classifiers
Streaming PCA for Markovian Data
The Learnability of In-Context Learning
Deep Equilibrium Models
A Massively Parallel Digital Learning Processor
Elucidating the Design Space of Dataset Condensation
Sparse Variational Inference: Bayesian Coresets from Scratch
Out-of-Distribution Detection using Multiple Semantic Label Representations
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Marginal Causal Flows for Validation and Inference
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
Learning Social Welfare Functions