yusuke1997/dblp-id-title huggingface dataset:
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Learning dynamics in linear recurrent neural networks
Multi-Objective Programming in SVMs
Classification Value Grouping
Stability of Controllers for Gaussian Process Forward Models
A transductive framework of distance metric learning by spectral dimensionality reduction
Safe Element Screening for Submodular Function Minimization
Neurotoxin: Durable Backdoors in Federated Learning
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
On the Design of Estimators for Bandit Off-Policy Evaluation
Out-of-Distribution Generalization via Risk Extrapolation (REx)
ATM Scheduling with Queuing Dely Predictions
The Importance of Non-Markovianity in Maximum State Entropy Exploration
Outlier-Aware Post-Training Quantization for Discrete Graph Diffusion Models
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Wait-Less Offline Tuning and Re-solving for Online Decision Making
Revisiting the Power of Prompt for Visual Tuning
Efficient Active Learning of Halfspaces: an Aggressive Approach
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Background Knowledge in GA-based Concept Learning
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
Variational Inference for Sequential Data with Future Likelihood Estimates
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning
Guided evolutionary strategies: augmenting random search with surrogate gradients
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve
Differentially private exact recovery for stochastic block models
A General Online Algorithm for Optimizing Complex Performance Metrics
Megainduction: A Test Flight
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
Linear Discriminant Trees
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
Position: Compositional Generative Modeling: A Single Model is Not All You Need
Dimensionality Reduction for the Sum-of-Distances Metric
Learning word normalization using word suffix and context from unlabeled data
SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
Beyond the Visual Analysis of Deep Model Saliency
Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
Mahalanobis++: Improving OOD Detection via Feature Normalization
Learning Hierarchies of Abstraction Spaces
Calibration, Entropy Rates, and Memory in Language Models
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction
Cluster Explanation via Polyhedral Descriptions
Ensemble Distribution Distillation via Flow Matching
Fast Projection Onto Convex Smooth Constraints
Mixtures of hierarchical topics with Pachinko allocation
Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees
InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization
Differentially Private Post-Processing for Fair Regression
Neural Markov Jump Processes
Language-Driven Cross-Modal Classifier for Zero-Shot Multi-Label Image Recognition
Fast Probabilistic Optimization from Noisy Gradients
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data
A Binary Classification Framework for Two-Stage Multiple Kernel Learning
Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification
Exploring the Low-Pass Filtering Behavior in Image Super-Resolution
How Deep Do We Need: Accelerating Training and Inference of Neural ODEs via Control Perspective
Wasserstein Propagation for Semi-Supervised Learning
Constraining Learning with Search Control
Fairness risk measures
Teaching Transformers Causal Reasoning through Axiomatic Training
GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
Joint Online Learning and Decision-making via Dual Mirror Descent
Concept-Centric Token Interpretation for Vector-Quantized Generative Models
Bottleneck-Minimal Indexing for Generative Document Retrieval
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
A Goal-Based Approach to Intelligent Information Retrieval
Transformed Distribution Matching for Missing Value Imputation
Non-negative Matrix Factorization under Heavy Noise
Weighted decomposition kernels
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
Adversarial Robustness via Deformable Convolution with Stochasticity
Spherical Topic Models
Preference-CFR: Beyond Nash Equilibrium for Better Game Strategies
Scalable Deep Generative Modeling for Sparse Graphs
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Meta-Learning with Shared Amortized Variational Inference
On Pitfalls of Test-Time Adaptation
Principal Bit Analysis: Autoencoding with Schur-Concave Loss
REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Exchangeable Variable Models
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Kernel Quantile Embeddings and Associated Probability Metrics
Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks
Distilling Reliable Information From Unreliable Theories
Causal Identification under Markov Equivalence: Completeness Results
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994
TVE: Learning Meta-attribution for Transferable Vision Explainer
Scalable Optimization of Neighbor Embedding for Visualization
Sequential update of ADtrees
Nonmyopic Multifidelity Acitve Search
Online learning by ellipsoid method
D 2 : Decentralized Training over Decentralized Data
Test-Time Learning for Large Language Models
Adaptive Sampling for SGD by Exploiting Side Information
FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization
Adversarial Policy Learning in Two-player Competitive Games
P3VI: a partitioned, prioritized, parallel value iterator
An Instability in Variational Inference for Topic Models
Strategic Planning: A Top-Down Approach to Option Generation
Attention-Only Transformers via Unrolled Subspace Denoising
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Full Law Identification in Graphical Models of Missing Data: Completeness Results
Robust Group Synchronization via Quadratic Programming
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
Composite Quantization for Approximate Nearest Neighbor Search
The Label Complexity of Mixed-Initiative Classifier Training
Loss factorization, weakly supervised learning and label noise robustness
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
Hierarchical Classification via Orthogonal Transfer
Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation
Large-Margin Contrastive Learning with Distance Polarization Regularizer
A Language-based Approach to Measuring Scholarly Impact
Label Ranking Methods based on the Plackett-Luce Model
Understanding the Unfairness in Network Quantization
Supervised Tree-Wasserstein Distance
Well-Behaved Borgs, Bolos, and Berserkers
StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization
Latent Feature Lasso
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
Dirichlet enhanced relational learning
Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Near-Optimal Confidence Sequences for Bounded Random Variables
Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models
Incremental Sampling Without Replacement for Sequence Models
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
Pareto Frontier Learning with Expensive Correlated Objectives
Can Large Language Models Reason about Program Invariants?
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Explanation-Based Learning of Reactive Operations
Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination
Towards Scaling Difference Target Propagation by Learning Backprop Targets
Better & Faster Large Language Models via Multi-token Prediction
Reducing Tool Hallucination via Reliability Alignment
Learning What and Where to Transfer
Zero-Inflated Exponential Family Embeddings
Domain Generalization using Causal Matching
Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration
Differentially Private Policy Evaluation
Adaptive Neural Trees
Large Margin Trees for Induction and Transduction
Empirical Substructure Discovery
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
Bi-directional Masks for Efficient N: M Sparse Training
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis
Stochastic Generative Hashing
Partial Label Learning via Label Influence Function
Learning from Labeled and Unlabeled Data using Graph Mincuts
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
Probabilistic Concept Bottleneck Models
Self-Bootstrapping for Versatile Test-Time Adaptation
XAI for Transformers: Better Explanations through Conservative Propagation
Fast Min-ϵ Segmented Regression using Constant-Time Segment Merging
LIMO: Latent Inceptionism for Targeted Molecule Generation
On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents
Whitening for Self-Supervised Representation Learning
Fourier Learning with Cyclical Data
Stochastic Differential Equations with Variational Wishart Diffusions
Simple Randomized Rounding for Max-Min Eigenvalue Augmentation
Towards Inductive Generalization in Higher Order Logic
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
Estimating Accuracy from Unlabeled Data: A Bayesian Approach
SSHR: More Secure Generative Steganography with High-Quality Revealed Secret Images
Learning Classifiers for Target Domain with Limited or No Labels
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Generalized Data Distribution Iteration
Gaussian Process Vine Copulas for Multivariate Dependence
Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition
Modelling transition dynamics in MDPs with RKHS embeddings
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
Deep Fusion: Efficient Network Training via Pre-trained Initializations
Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
Towards Understanding Learning in Neural Networks with Linear Teachers
Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models
GPT-4V(ision) is a Generalist Web Agent, if Grounded
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Doubly robust off-policy evaluation with shrinkage
Multi-Strategy Learning in Nonhomongeneous Domain Theories
Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies
Learning to Route LLMs with Confidence Tokens
Efficient MAP approximation for dense energy functions
A Variational Analysis of Stochastic Gradient Algorithms
Do Vision-Language Models Really Understand Visual Language?
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression
Graph Structure Extrapolation for Out-of-Distribution Generalization
Correcting Forecasts with Multifactor Neural Attention
Policy Certificates: Towards Accountable Reinforcement Learning
Laplace maximum margin Markov networks
On the Universality of Invariant Networks
Large margin hierarchical classification
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Optimizing Data Usage via Differentiable Rewards
Long-Term Rhythmic Video Soundtracker
Workshop summary: Workshop on learning feature hierarchies
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Dueling Convex Optimization
Knowledge Acquisition form Examples Vis Multiple Models
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Position: Stop Making Unscientific AGI Performance Claims
Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs
FreeMesh: Boosting Mesh Generation with Coordinates Merging
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting
SAGA with Arbitrary Sampling
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
Sample-Efficient Multiagent Reinforcement Learning with Reset Replay
Structured Evolution with Compact Architectures for Scalable Policy Optimization
On the consistency of top-k surrogate losses
Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Position: TrustLLM: Trustworthiness in Large Language Models
How Do Transformers Learn Variable Binding in Symbolic Programs?
Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs
On Universally Optimal Algorithms for A/B Testing
Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Active Transfer Learning under Model Shift
Geodesic Convolutional Shape Optimization
Continuous-Time Analysis of Heavy Ball Momentum in Min-Max Games
When, Where and Why to Average Weights?
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Not All Wrong is Bad: Using Adversarial Examples for Unlearning
Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors
Language Generation with Strictly Proper Scoring Rules
Kernel interpolation with continuous volume sampling
Multi-Class Pegasos on a Budget
Reinformer: Max-Return Sequence Modeling for Offline RL
Continuous Inverse Optimal Control with Locally Optimal Examples
Spatial Reasoning with Denoising Models
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
MEWL: Few-shot multimodal word learning with referential uncertainty
ClusterFuG: Clustering Fully connected Graphs by Multicut
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds
Optimistic Bounds for Multi-output Learning
Deep Predictive Coding Network for Object Recognition
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Learning Deep Boltzmann Machines using Adaptive MCMC
Predicting accurate probabilities with a ranking loss
Automatic Feature Decomposition for Single View Co-training
Understanding Heterophily for Graph Neural Networks
Autonomous Sparse Mean-CVaR Portfolio Optimization
On the Hardness of Probabilistic Neurosymbolic Learning
Variational Auto-Regressive Gaussian Processes for Continual Learning
From Language Models over Tokens to Language Models over Characters
Semi-Offline Reinforcement Learning for Optimized Text Generation
Implicit Imitation in Multiagent Reinforcement Learning
Demystifying the Adversarial Robustness of Random Transformation Defenses
The Role of Forgetting in Learning
Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Unimodal Probability Distributions for Deep Ordinal Classification
Improving Neural Language Modeling via Adversarial Training
Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
Towards a Learning Theory of Cause-Effect Inference
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization
Disentangled Multiplex Graph Representation Learning
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Position-aware Graph Neural Networks
Bayesian compressive sensing and projection optimization
Frame-based Data Factorizations
A unified architecture for natural language processing: deep neural networks with multitask learning
UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent
Learning Triggering Kernels for Multi-dimensional Hawkes Processes
Deconstructing the Ladder Network Architecture
Representing and Learning Quality-Improving Search Control Knowledge
Evaluating Neuron Explanations: A Unified Framework with Sanity Checks
Model-Free Trajectory Optimization for Reinforcement Learning
XAI and Strategy Extraction via Reward Redistribution
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
Hierarchical Exploration for Accelerating Contextual Bandits
Graph Inverse Style Transfer for Counterfactual Explainability
Clustering by Low-Rank Doubly Stochastic Matrix Decomposition
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
On Fine-Grained Distinct Element Estimation
Local Correlation Clustering with Asymmetric Classification Errors
Complete Cross-Validation for Nearest Neighbor Classifiers
ODIN: Disentangled Reward Mitigates Hacking in RLHF
GaussMark: A Practical Approach for Structural Watermarking of Language Models
On the Role of Discount Factor in Offline Reinforcement Learning
Dynamics-inspired Neuromorphic Visual Representation Learning
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML 2016, co-located with 33rd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 24, 2016
BLO-SAM: Bi-level Optimization Based Finetuning of the Segment Anything Model for Overfitting-Preventing Semantic Segmentation
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs
Ready Policy One: World Building Through Active Learning
Conformal Prediction as Bayesian Quadrature
Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning
Ensembles of biased classifiers
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
Scaling Laws for Reward Model Overoptimization
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
Ensemble Methods for Structured Prediction
Domain-wise Data Acquisition to Improve Performance under Distribution Shift
Instrumental Variable Regression with Confounder Balancing
Transformers Meet Directed Graphs
Barron and Cover's Theory in Supervised Learning and its Application to Lasso
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
Monte Carlo Tree Diffusion for System 2 Planning
TSPipe: Learn from Teacher Faster with Pipelines
Calibrated Language Models and How to Find Them with Label Smoothing
SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion
Softmax is not Enough (for Sharp Size Generalisation)
A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization
Approximation Algorithms for Cascading Prediction Models
Feature value acquisition in testing: a sequential batch test algorithm
From Debate to Equilibrium: Belief‑Driven Multi‑Agent LLM Reasoning via Bayesian Nash Equilibrium
MetricEmbedding: Accelerate Metric Nearness by Tropical Inner Product
CoT: Cooperative Training for Generative Modeling of Discrete Data
Linear Programming Boosting for Uneven Datasets
Human Body Restoration with One-Step Diffusion Model and A New Benchmark
The MAXQ Method for Hierarchical Reinforcement Learning
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Faster Attend-Infer-Repeat with Tractable Probabilistic Models
Principled Knowledge Extrapolation with GANs
The Lipschitz Constant of Self-Attention
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
Scalable AI Safety via Doubly-Efficient Debate
S2FGL: Spatial Spectral Federated Graph Learning
Participatory Learning: A Constructivist Model
Coresets for Ordered Weighted Clustering
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
Stein Point Markov Chain Monte Carlo
The Infinite Contextual Graph Markov Model
Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
On Graduated Optimization for Stochastic Non-Convex Problems
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance
Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
Measuring Utility and the Design of Provably Good EBL Algorithms
Efficient Ranking from Pairwise Comparisons
Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions
LyaNet: A Lyapunov Framework for Training Neural ODEs