yusuke1997/dblp-id-title huggingface dataset:
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
IDRNet: Intervention-Driven Relation Network for Semantic Segmentation
Online Frank-Wolfe with Arbitrary Delays
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization
Submodular-Bregman and the Lovász-Bregman Divergences with Applications
Computation of Smooth Optical Flow in a Feedback Connected Analog Network
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Nonparametric Density Estimation under Adversarial Losses
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing
Provably Bounding Neural Network Preimages
Backdoor Attack with Imperceptible Input and Latent Modification
Interpolating Earth-science Data using RBF Networks and Mixtures of Experts
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox
Long Short-Term Transformer for Online Action Detection
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
On the Universality of Graph Neural Networks on Large Random Graphs
Meta-Learning Adversarial Bandit Algorithms
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Mechanisms of Generalization in Perceptual Learning
Infinite-Horizon Gaussian Processes
Blind Calibration in Compressed Sensing using Message Passing Algorithms
Accelerating Augmentation Invariance Pretraining
Mixture Density Estimation
Semigroup Kernels on Finite Sets
Model-Free Least-Squares Policy Iteration
A Silicon Primitive for Competitive Learning
CSER: Communication-efficient SGD with Error Reset
Hierarchical Optimal Transport for Multimodal Distribution Alignment
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
An Online Algorithm for Maximizing Submodular Functions
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
Modeling Applications with the Focused Gamma Net
Graph Few-shot Learning with Task-specific Structures
Lower Bounds for Passive and Active Learning
RegBN: Batch Normalization of Multimodal Data with Regularization
Products of Edge-perts
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits
Local policy search with Bayesian optimization
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
Presynaptic Neural Information Processing
Diagonal State Spaces are as Effective as Structured State Spaces
Sum-of-Squares Lower Bounds for Sparse PCA
Exponentially many local minima for single neurons
Acceleration with a Ball Optimization Oracle
The Bayesian Stability Zoo
A rational model of causal inference with continuous causes
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
A Stochastic Newton Algorithm for Distributed Convex Optimization
Excess Capacity and Backdoor Poisoning
A Computer Simulation of Olfactory Cortex with Functional Implications for Storage and Retrieval of Olfactory Information
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
Individual Privacy Accounting via a Rényi Filter
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
An urn model for majority voting in classification ensembles
Riemannian Score-Based Generative Modelling
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Unsupervised Context Sensitive Language Acquisition from a Large Corpus
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
Efficient and Thrifty Voting by Any Means Necessary
Adaptive Elastic Models for Hand-Printed Character Recognition
Matrix factorisation and the interpretation of geodesic distance
Information-theoretic lower bounds for convex optimization with erroneous oracles
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework
Interactive Control of Diverse Complex Characters with Neural Networks
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
The Effects of Regularization and Data Augmentation are Class Dependent
An Accelerated Proximal Coordinate Gradient Method
Differentially Private Graph Diffusion with Applications in Personalized PageRanks
Learning with Fitzpatrick Losses
On Agreements in Visual Understanding
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Learning to solve TV regularised problems with unrolled algorithms
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
Learning Efficient Markov Networks
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables
Grounding Aleatoric Uncertainty for Unsupervised Environment Design
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression
Multi-scale Graphical Models for Spatio-Temporal Processes
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
State Aware Imitation Learning
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs
Dimensionality Reduction Using the Sparse Linear Model
Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Algorithmic progress in language models
Medical Dead-ends and Learning to Identify High-Risk States and Treatments
The Inductive Bias of Quantum Kernels
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Unsupervised Classification of 3D Objects from 2D Views
DP-PCA: Statistically Optimal and Differentially Private PCA
Long-range Brain Graph Transformer
Dynamically Adapting Kernels in Support Vector Machines
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
A Practical Algorithm for Distributed Clustering and Outlier Detection
Max-Margin Token Selection in Attention Mechanism
Multi-Agent Learning with Heterogeneous Linear Contextual Bandits
Long Sequence Hopfield Memory
Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
Semi-Supervised Multitask Learning
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
Action Centered Contextual Bandits
Towards Last-layer Retraining for Group Robustness with Fewer Annotations
Adaptive Sampling for Minimax Fair Classification
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
Abstraction and Relational learning
Segmenting Moving Objects via an Object-Centric Layered Representation
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
Coordinated hippocampal-entorhinal replay as structural inference
Tight Continuous Relaxation of the Balanced k-Cut Problem
Learning Conditioned Graph Structures for Interpretable Visual Question Answering
Leveraged volume sampling for linear regression
Persistence Homology Distillation for Semi-supervised Continual Learning
D-Separation for Causal Self-Explanation
From an Image to a Scene: Learning to Imagine the World from a Million 360° Videos
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent
On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
L_0-norm Minimization for Basis Selection
Ontologue: Declarative Benchmark Construction for Ontological Multi-Label Classification
Variational Inference with Gaussian Score Matching
Generalizable One-shot 3D Neural Head Avatar
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose
QBB: Quantization with Binary Bases for LLMs
Deep imitation learning for molecular inverse problems
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
How Control Information Influences Multilingual Text Image Generation and Editing?
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
All Tokens Matter: Token Labeling for Training Better Vision Transformers
Probabilistic Abstraction Hierarchies
Polyhedron Attention Module: Learning Adaptive-order Interactions
Optimal Dynamic Regret in LQR Control
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals
Combating Bilateral Edge Noise for Robust Link Prediction
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Learning Complex Boolean Functions: Algorithms and Applications
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
Learning and Inference in Hilbert Space with Quantum Graphical Models
Exploiting Opponents Under Utility Constraints in Sequential Games
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant
Enhancing Preference-based Linear Bandits via Human Response Time
Approximate Inference Turns Deep Networks into Gaussian Processes
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression
Learning from Trajectories via Subgoal Discovery
Symmetry-induced Disentanglement on Graphs
Robust Model Reasoning and Fitting via Dual Sparsity Pursuit
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
Characterizing response behavior in multisensory perception with conflicting cues
Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
Learning Hawkes Processes from a handful of events
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Learning Sparse Perceptrons
Correlated Neuronal Response: Time Scales and Mechanisms
Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
Mirrored Langevin Dynamics
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
A New Model of Spatial Representation in Multimodal Brain Areas
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation
Constant Regret, Generalized Mixability, and Mirror Descent
N-agent Ad Hoc Teamwork
LLM-based Skill Diffusion for Zero-shot Policy Adaptation
Gradient Methods for Submodular Maximization
Towards Understanding Grokking: An Effective Theory of Representation Learning
From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When
Short-term memory in neuronal networks through dynamical compressed sensing
Single Neuron Model: Response to Weak Modulation in the Presence of Noise
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
Hardness of Learning Neural Networks under the Manifold Hypothesis
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
An Empirical Evaluation of Thompson Sampling
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
Score-based Data Assimilation
Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
End to end learning and optimization on graphs
Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork
A Parameter-free Hedging Algorithm
Self-Supervised Pretraining for Large-Scale Point Clouds
Dual Instrumental Variable Regression
Kuro Siwo: 33 billion m 2 under the water. A global multi-temporal satellite dataset for rapid flood mapping
Testing and Learning on Distributions with Symmetric Noise Invariance
Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells"
Flow-based Image-to-Image Translation with Feature Disentanglement
Generalization in Decision Trees and DNF: Does Size Matter?
Robust PCA via Outlier Pursuit
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Robust Optimization for Non-Convex Objectives
The Everlasting Database: Statistical Validity at a Fair Price
ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings
Addressing "Documentation Debt" in Machine Learning: A Retrospective Datasheet for BookCorpus
Sequential Noise Compensation by Sequential Monte Carlo Method
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
On Imitation in Mean-field Games
FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
STEP: Segmenting and Tracking Every Pixel
Inverse Reinforcement Learning through Structured Classification
CalFAT: Calibrated Federated Adversarial Training with Label Skewness
Spike train entropy-rate estimation using hierarchical Dirichlet process priors
Invariant Feature Extraction and Classification in Kernel Spaces
Pruning Filter in Filter
Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
The Price of Bandit Information for Online Optimization
Neural system identification for large populations separating "what" and "where"
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
DECOrrelated feature space partitioning for distributed sparse regression
How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
Barzilai-Borwein Step Size for Stochastic Gradient Descent
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
An Investigation of Practical Approximate Nearest Neighbor Algorithms
Double Thompson Sampling for Dueling Bandits
Generalization of Hamiltonian algorithms
Deep Representations and Codes for Image Auto-Annotation
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Language Models are Weak Learners
Scattering Vision Transformer: Spectral Mixing Matters
Truncated Matrix Power Iteration for Differentiable DAG Learning
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Edge-exchangeable graphs and sparsity
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Stochastic Optimization with Laggard Data Pipelines
Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction
On the relationship between variational inference and auto-associative memory
Approximate optimization of convex functions with outlier noise
Streaming Algorithms and Lower Bounds for Estimating Correlation Clustering Cost
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
Disentangling and mitigating the impact of task similarity for continual learning
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions
Average-Reward Learning and Planning with Options
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs
Multi-label Multiple Kernel Learning
Learning step sizes for unfolded sparse coding
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Learning via Surrogate PAC-Bayes
A Neural Net Model for Adaptive Control of Saccadic Accuracy by Primate Cerebellum and Brainstem
When is Multicalibration Post-Processing Necessary?
Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
A Game-Theoretic Approach to Hypergraph Clustering
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Learning From Biased Soft Labels
A Probabilistic Approach to Single Channel Blind Signal Separation
Renyi Differential Privacy Mechanisms for Posterior Sampling
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
Positive Curvature and Hamiltonian Monte Carlo
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
Augmented Neural ODEs
Topological Detection of Trojaned Neural Networks
Instance Selection for GANs
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Non-convolutional graph neural networks
MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions
Learning to Multitask
Cross-Domain Policy Adaptation via Value-Guided Data Filtering
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
An Approximate Inference Approach for the PCA Reconstruction Error
Discrete Dictionary-based Decomposition Layer for Structured Representation Learning
One Permutation Hashing
Maximum State Entropy Exploration using Predecessor and Successor Representations
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
Curvature Regularization to Prevent Distortion in Graph Embedding
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data
Global Optimality in Bivariate Gradient-based DAG Learning
Analytical Mean Squared Error Curves in Temporal Difference Learning
Generic Analog Neural Computation - The Epsilon Chip
Focal Attention for Long-Range Interactions in Vision Transformers
Cluster Stability for Finite Samples
Predictive coding in balanced neural networks with noise, chaos and delays
Silicon Retina with Adaptive Filtering Properties
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
Variational Model Perturbation for Source-Free Domain Adaptation
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising
BOSS: Bayesian Optimization over String Spaces
Maximin affinity learning of image segmentation
Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Robust Novelty Detection with Single-Class MPM
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
Can We Learn to Beat the Best Stock
Kernel-Based Reinforcement Learning in Average-Cost Problems: An Application to Optimal Portfolio Choice
The Statistical Complexity of Early-Stopped Mirror Descent
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
Multi-Agent Filtering with Infinitely Nested Beliefs
Weitzman's Rule for Pandora's Box with Correlations
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
Information Bottleneck for Gaussian Variables
Optimizing spatio-temporal filters for improving Brain-Computer Interfacing
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
A Convergence Analysis of Log-Linear Training
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces
Discovery of the Hidden World with Large Language Models
Neural Routing by Memory
Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
Learning Efficient Object Detection Models with Knowledge Distillation
A statistical model for tensor PCA
Understanding Social Reasoning in Language Models with Language Models
Exact sampling of determinantal point processes with sublinear time preprocessing
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
Online Convex Matrix Factorization with Representative Regions
End-to-End Learning on 3D Protein Structure for Interface Prediction
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
Correcting sample selection bias in maximum entropy density estimation
On Adaptive Distance Estimation
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation
Hebb Learning of Features based on their Information Content
Dynamic Modelling of Chaotic Time Series with Neural Networks
How memory biases affect information transmission: A rational analysis of serial reproduction
On the Generalization Ability of On-Line Learning Algorithms
Automatic Local Annealing
Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Learning Regularized Monotone Graphon Mean-Field Games