yusuke1997/dblp-id-title huggingface dataset:
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space
Comparison-Based Random Forests
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
Training Your Sparse Neural Network Better with Any Mask
Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning
Fast and Low-Cost Genomic Foundation Models via Outlier Removal
Relational Pooling for Graph Representations
Towards Controlled Data Augmentations for Active Learning
Online Ensemble Learning: An Empirical Study
High Dimensional Bayesian Optimization with Elastic Gaussian Process
Arrow: Accelerator for Time Series Causal Discovery with Time Weaving
Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints
Boosting Protein Graph Representations through Static-Dynamic Fusion
Learning curves theory for hierarchically compositional data with power-law distributed features
Non-stationary Reinforcement Learning under General Function Approximation
Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration
The Ripple Effect: On Unforeseen Complications of Backdoor Attacks
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Latent Bernoulli Autoencoder
On Many-Actions Policy Gradient
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains
HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
Self-Paced Co-training
Learning-augmented private algorithms for multiple quantile release
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
Finding Botnets Using Minimal Graph Clusterings
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
Targeted Data Acquisition for Evolving Negotiation Agents
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
Emoji Attack: Enhancing Jailbreak Attacks Against Judge LLM Detection
High-Dimensional Structured Quantile Regression
Communication-efficient Distributed Learning for Large Batch Optimization
On The Statistical Complexity of Offline Decision-Making
Language Modeling with Gated Convolutional Networks
Deep Neural Room Acoustics Primitive
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods
Online Algorithms with Uncertainty-Quantified Predictions
ROPO: Robust Preference Optimization for Large Language Models
Complementary-Label Learning for Arbitrary Losses and Models
Simulation-Based Inference with Quantile Regression
Ex-VAD: Explainable Fine-grained Video Anomaly Detection Based on Visual-Language Models
Deep Density Destructors
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
A Convolutional Attention Network for Extreme Summarization of Source Code
Path Planning using Neural A* Search
Trajectory Inference with Smooth Schrödinger Bridges
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Grassmann discriminant analysis: a unifying view on subspace-based learning
Is Noise Conditioning Necessary for Denoising Generative Models?
TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging
Learning the Structure of Sum-Product Networks
Self-Infilling Code Generation
Modeling Caption Diversity in Contrastive Vision-Language Pretraining
Learning to Generate Long-term Future via Hierarchical Prediction
Neurocoder: General-Purpose Computation Using Stored Neural Programs
Global Optimization of K-Center Clustering
A new regret analysis for Adam-type algorithms
Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI
A scalable trust-region algorithm with application to mixed-norm regression
Multivariate Conformal Selection
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
WMarkGPT: Watermarked Image Understanding via Multimodal Large Language Models
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
PDUDT: Provable Decentralized Unlearning under Dynamic Topologies
Federated Causal Structure Learning with Non-identical Variable Sets
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method
Smoothed Action Value Functions for Learning Gaussian Policies
Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models
Characteristics of Long-term Learning in Soar and its Application to the Utility Problem
Doubly Robust Policy Evaluation and Learning
Instruct2See: Learning to Remove Any Obstructions Across Distributions
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD
Geometric Median (GM) Matching for Robust k-Subset Selection from Noisy Data
On the Origins of Linear Representations in Large Language Models
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Group Equivariant Convolutional Networks
New í-Support Vector Machines and their Sequential Minimal Optimization
Building Socially-Equitable Public Models
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
Learning with Bad Training Data via Iterative Trimmed Loss Minimization
Training Dynamics of In-Context Learning in Linear Attention
A Physics-Augmented Deep Learning Framework for Classifying Single Molecule Force Spectroscopy Data
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Detached Error Feedback for Distributed SGD with Random Sparsification
On the hardness of finding symmetries in Markov decision processes
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Context Aware Local Differential Privacy
Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves
Random Feature Representation Boosting
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation
Tutorial summary: Learning with dependencies between several response variables
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Band-limited Training and Inference for Convolutional Neural Networks
Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning
More Than Meets the Eye: Enhancing Multi-Object Tracking Even with Prolonged Occlusions
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Not All Poisons are Created Equal: Robust Training against Data Poisoning
RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models
Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Learning with Rare Cases and Small Disjuncts
LinSATNet: The Positive Linear Satisfiability Neural Networks
A Multitask Point Process Predictive Model
Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes
Learning Affinity with Hyperbolic Representation for Spatial Propagation
Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering
Fast solvers and efficient implementations for distance metric learning
Learning Physical Models that Can Respect Conservation Laws
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
Deciding What to Learn: A Rate-Distortion Approach
Towards Understanding Knowledge Distillation
Configurable Markov Decision Processes
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
The support vector decomposition machine
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework
Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks
Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State
Active Learning for Distributionally Robust Level-Set Estimation
MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-Roles
AdaCost: Misclassification Cost-Sensitive Boosting
Nearly Optimal Robust Matrix Completion
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
Optimistic Policy Optimization with Bandit Feedback
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
Imputing Missing Events in Continuous-Time Event Streams
PAC-inspired Option Discovery in Lifelong Reinforcement Learning
Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
Stochastic Discrete Clenshaw-Curtis Quadrature
Attentive Recurrent Comparators
Beyond One-Hot Labels: Semantic Mixing for Model Calibration
Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large p
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
Diffusion bridges vector quantized variational autoencoders
Learning low dimensional predictive representations
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Enhancing Sufficient Dimension Reduction via Hellinger Correlation
Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks
Adaptive Model Design for Markov Decision Process
Switchable Decision: Dynamic Neural Generation Networks
A convex formulation for learning shared structures from multiple tasks
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification
An Efficient Private GPT Never Autoregressively Decodes
Clustering graphs by weighted substructure mining
Towards an optimal stochastic alternating direction method of multipliers
Graph Geometry-Preserving Autoencoders
One-sided Matrix Completion from Two Observations Per Row
Distributed Tree Kernels
Universality of Winning Tickets: A Renormalization Group Perspective
Online Restless Bandits with Unobserved States
A Modern Self-Referential Weight Matrix That Learns to Modify Itself
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Multidimensional Scaling: Approximation and Complexity
Sparse eigen methods by D.C. programming
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Semi-supervised Penalized Output Kernel Regression for Link Prediction
3D-VLA: A 3D Vision-Language-Action Generative World Model
Non Mean Square Error Criteria for the Training of Learning Machines
Diffusion Models for Adversarial Purification
Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control
Agnostic Federated Learning
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
OptNet: Differentiable Optimization as a Layer in Neural Networks
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Shifted Interpolation for Differential Privacy
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Combining parametric and nonparametric models for off-policy evaluation
Polynomial Preconditioning for Gradient Methods
Sparsity-Agnostic Lasso Bandit
Learning Hierarchies from Ambiguous Natural Language Data
Are Large Brainwave Foundation Models Capable Yet ? Insights from Fine-Tuning
Online Alternating Direction Method
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach
Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
An Effective Meaningful Way to Evaluate Survival Models
Cross-Domain Multitask Learning with Latent Probit Models
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning
GRAIL: Graph Edit Distance and Node Alignment using LLM-Generated Code
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
RLSbench: Domain Adaptation Under Relaxed Label Shift
By Tying Embeddings You Are Assuming the Distributional Hypothesis
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
Learning Plans for Competitive Domains
In-Context Learning as Conditioned Associative Memory Retrieval
Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries
Position: A Safe Harbor for AI Evaluation and Red Teaming
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
SparseBERT: Rethinking the Importance Analysis in Self-attention
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension
Recurrent Early Exits for Federated Learning with Heterogeneous Clients
Fast Gaussian process methods for point process intensity estimation
DANCE: Dual Unbiased Expansion with Group-acquired Alignment for Out-of-distribution Graph Fairness Learning
A Simple Model for Complex Networks with Arbitrary Degree Distribution and Clustering
Second-Order Uncertainty Quantification: A Distance-Based Approach
Balancing Similarity and Complementarity for Federated Learning
Training Neural Networks with Local Error Signals
Minimizing Control for Credit Assignment with Strong Feedback
Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures
Inter-domain Deep Gaussian Processes
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
Distributed Nonparametric Regression under Communication Constraints
Overtrained Language Models Are Harder to Fine-Tune
Adversarial Time-to-Event Modeling
Automatic Goal Generation for Reinforcement Learning Agents
CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
Learning probabilistic motion models for mobile robots
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Path-Gradient Estimators for Continuous Normalizing Flows
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
Revisiting Spatial Invariance with Low-Rank Local Connectivity
HYGMA: Hypergraph Coordination Networks with Dynamic Grouping for Multi-Agent Reinforcement Learning
Position: Benchmarking is Limited in Reinforcement Learning Research
SWALP : Stochastic Weight Averaging in Low Precision Training
Does Data Augmentation Lead to Positive Margin?
Markov Latent Feature Models
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
Weak Detection of Signal in the Spiked Wigner Model
Adversarial Inception Backdoor Attacks against Reinforcement Learning
Better to Teach than to Give: Domain Generalized Semantic Segmentation via Agent Queries with Diffusion Model Guidance
Optimal Estimator for Unlabeled Linear Regression
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
Vector Quantized Wasserstein Auto-Encoder
Fast Federated Machine Unlearning with Nonlinear Functional Theory
Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Tower of Hanoi with Connectionist Networks: Learning New Features
Efficient algorithms for decision tree cross-validation
Evaluating Lossy Compression Rates of Deep Generative Models
Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic
LongCoder: A Long-Range Pre-trained Language Model for Code Completion
Learning Variable Descriptors for Applying Heuristics Across CSP Problems
Editing Partially Observable Networks via Graph Diffusion Models
A Large-Scale Study on Regularization and Normalization in GANs
Streaming Inference for Infinite Feature Models
Graph-based Nearest Neighbor Search: From Practice to Theory
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
SPRI: Aligning Large Language Models with Context-Situated Principles
Hierarchical Learning in Stochastic Domains: Preliminary Results
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
Discriminative Reranking for Natural Language Parsing
Bridging Fairness and Efficiency in Conformal Inference: A Surrogate-Assisted Group-Clustered Approach
XTab: Cross-table Pretraining for Tabular Transformers
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning
Principled Offline RL in the Presence of Rich Exogenous Information
Robust Reward Alignment via Hypothesis Space Batch Cutting
Reparameterized Policy Learning for Multimodal Trajectory Optimization
Streaming Submodular Maximization under a k-Set System Constraint
Learning structurally consistent undirected probabilistic graphical models
Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space
Large Batch Experience Replay
Correlation Clustering Beyond the Pivot Algorithm
Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer
Optimization with First-Order Surrogate Functions
Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests
Learning Semantic Representations for Unsupervised Domain Adaptation
Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis
Energy-based Backdoor Defense without Task-Specific Samples and Model Retraining
Generative Pretraining for Black-Box Optimization
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Learning diverse rankings with multi-armed bandits
Generative Video Transformer: Can Objects be the Words?
Subgoal-based Demonstration Learning for Formal Theorem Proving
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
Provably Efficient Exploration in Policy Optimization
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
Debiased Distribution Compression
Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition
Distributed Event-Based Learning via ADMM
Data Feedback Loops: Model-driven Amplification of Dataset Biases
Complexity Matters: Feature Learning in the Presence of Spurious Correlations
Learning to segment from a few well-selected training images
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
Rotting Infinitely Many-Armed Bandits
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
Total Variation Graph Neural Networks
Representational Similarity Learning with Application to Brain Networks
Importance Weighted Expectation-Maximization for Protein Sequence Design
Fixed-Parameter and Approximation Algorithms for PCA with Outliers
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Boosting for Online Convex Optimization
Feature Programming for Multivariate Time Series Prediction
Efficient Learning for AlphaZero via Path Consistency
BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
Cross-Modal Fine-Tuning: Align then Refine
Learning the Irreducible Representations of Commutative Lie Groups
Coded Sparse Matrix Multiplication
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Query Learning Using an ANN with Adaptive Architecture
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning
Bilinear Bandits with Low-rank Structure
Scalable Pre-training of Large Autoregressive Image Models
Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Efficient Approximate Inference for Stationary Kernel on Frequency Domain
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement
MDDM: Practical Message-Driven Generative Image Steganography Based on Diffusion Models
Unifying Orthogonal Monte Carlo Methods
RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning
Offline Learning in Markov Games with General Function Approximation
CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
Flowformer: Linearizing Transformers with Conservation Flows
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
Nonparametric Estimation of Multi-View Latent Variable Models
An exact solver for the Weston-Watkins SVM subproblem
QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline
Detecting the direction of causal time series
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Hyperbolic Image-text Representations
Mirror Learning: A Unifying Framework of Policy Optimisation
Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
Cross-domain transfer for reinforcement learning
Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation