yusuke1997/dblp-id-title huggingface dataset:
Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions
Kernel Stein Discrepancy Descent
Data-free Neural Representation Compression with Riemannian Neural Dynamics
Achieving Linear Speedup in Non-IID Federated Bilevel Learning
New d-separation identification results for learning continuous latent variable models
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
FrameQuant: Flexible Low-Bit Quantization for Transformers
EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
Graph Filtration Learning
Skip Context Tree Switching
C4.5 competence map: a phase transition-inspired approach
Incremental Development of Complex Behaviors
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
DeepWalking Backwards: From Embeddings Back to Graphs
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines
Principal Component Hierarchy for Sparse Quadratic Programs
Accelerated Federated Learning with Decoupled Adaptive Optimization
Born-Again Neural Networks
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Boosting products of base classifiers
LLM-Assisted Semantically Diverse Teammate Generation for Efficient Multi-agent Coordination
Learning from interpretations: a rooted kernel for ordered hypergraphs
Fast dropout training
Auxiliary Learning as an Asymmetric Bargaining Game
Learning Curves for Analysis of Deep Networks
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
More Results on the Complexity of Knowledge Base Refinement: Belief Networks
The Challenge of Revising an Impure Theory
What can online reinforcement learning with function approximation benefit from general coverage conditions?
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Stable and Efficient Representation Learning with Nonnegativity Constraints
The Algebraic Path Problem for Graph Metrics
Linear optimal partial transport embedding
World Model Implanting for Test-time Adaptation of Embodied Agents
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Fairness Without Demographics in Repeated Loss Minimization
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?
Comparisons of sequence labeling algorithms and extensions
Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
Unveiling the Dynamics of Information Interplay in Supervised Learning
Sequence Modeling via Segmentations
Teaching iCub to recognize objects using deep Convolutional Neural Networks
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Variational Boosting: Iteratively Refining Posterior Approximations
Bridging Layout and RTL: Knowledge Distillation based Timing Prediction
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
High Order Regularization for Semi-Supervised Learning of Structured Output Problems
Identifiable Object Representations under Spatial Ambiguities
Neural Relational Inference for Interacting Systems
On Least Square Estimation in Softmax Gating Mixture of Experts
Random Gegenbauer Features for Scalable Kernel Methods
Retrieval Augmented Time Series Forecasting
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits
Self-supervised Label Augmentation via Input Transformations
Asymptotics of Learning with Deep Structured (Random) Features
Joint Metric Space Embedding by Unbalanced Optimal Transport with Gromov-Wasserstein Marginal Penalization
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms
The Teaching Dimension of Regularized Kernel Learners
Explicit Gradient Learning for Black-Box Optimization
Premise Order Matters in Reasoning with Large Language Models
Robust feature induction for support vector machines
Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
(Non-)Convergence Results for Predictive Coding Networks
KoNODE: Koopman-Driven Neural Ordinary Differential Equations with Evolving Parameters for Time Series Analysis
Non-parametric policy gradients: a unified treatment of propositional and relational domains
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling
Efficient Sparse Group Feature Selection via Nonconvex Optimization
Discovering Options for Exploration by Minimizing Cover Time
Incorporating Prior Knowledge into Boosting
Rethinking DP-SGD in Discrete Domain: Exploring Logistic Distribution in the Realm of signSGD
Bilevel Optimization: Convergence Analysis and Enhanced Design
Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Origin Identification for Text-Guided Image-to-Image Diffusion Models
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Differentiable Weightless Neural Networks
General Covariance Data Augmentation for Neural PDE Solvers
Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs
Music Plus One and Machine Learning
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
On the Generalization Ability of Next-Token-Prediction Pretraining
NExT-Chat: An LMM for Chat, Detection and Segmentation
Hierarchical Generation of Molecular Graphs using Structural Motifs
Marginal Structured SVM with Hidden Variables
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning
On Context-Dependent Clustering of Bandits
On Positivity Condition for Causal Inference
A Unified Robust Regression Model for Lasso-like Algorithms
Exponential Integration for Hamiltonian Monte Carlo
KoopSTD: Reliable Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design
Larimar: Large Language Models with Episodic Memory Control
Federated Heavy Hitter Recovery under Linear Sketching
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Do Differentiable Simulators Give Better Policy Gradients?
Deep Factors for Forecasting
Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Removing the Genetics from the Standard Genetic Algorithm
Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
Let's be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Multi-armed bandit problems with dependent arms
AdaGrad stepsizes: sharp convergence over nonconvex landscapes
Temporal Query Network for Efficient Multivariate Time Series Forecasting
Matrix-Free Preconditioning in Online Learning
On Provable Copyright Protection for Generative Models
An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions
Three-Dimensional Trajectory Prediction with 3DMoTraj Dataset
Coupled Clustering: a Method for Detecting Structural Correspondence
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets
Adversarial Robust Generalization of Graph Neural Networks
Q-Supervised Contrastive Representation: A State Decoupling Framework for Safe Offline Reinforcement Learning
Better Rates for Any Adversarial Deterministic MDP
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model
Causal Logistic Bandits with Counterfactual Fairness Constraints
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes
Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations
A Fast Variational Approach for Learning Markov Random Field Language Models
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Computational Rationalization: The Inverse Equilibrium Problem
Learning to Generate Noise for Multi-Attack Robustness
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift
MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization
Quantifying and Learning Linear Symmetry-Based Disentanglement
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Interacting Particle Markov Chain Monte Carlo
Improved Contrastive Divergence Training of Energy-Based Models
Maximum Margin Output Coding
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Online feature elicitation in interactive optimization
Learning all optimal policies with multiple criteria
Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems
Improving Expert Predictions with Conformal Prediction
Practical solutions to the problem of diagonal dominance in kernel document clustering
Elementary superexpressive activations
nu -support vector machine as conditional value-at-risk minimization
Absolute Policy Optimization: Enhancing Lower Probability Bound of Performance with High Confidence
Federated Representation Learning in the Under-Parameterized Regime
Unsupervised Concept Discovery Mitigates Spurious Correlations
A Contextual Combinatorial Bandit Approach to Negotiation
Trainable Decoding of Sets of Sequences for Neural Sequence Models
Efficient Network Automatic Relevance Determination
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition
Conditional Neural Processes
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
Connectivity-Optimized Representation Learning via Persistent Homology
Finite-Sample Analysis of LSTD
Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models
Proto Successor Measure: Representing the Behavior Space of an RL Agent
Link-based Classification
Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning
Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
Manifold Preserving Hierarchical Topic Models for Quantization and Approximation
Using the Triangle Inequality to Accelerate k-Means
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models
Improving Out-of-Distribution Detection via Dynamic Covariance Calibration
Drug-TTA: Test-Time Adaptation for Drug Virtual Screening via Multi-task Meta-Auxiliary Learning
QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles for model-monitoring
Fast OSCAR and OWL Regression via Safe Screening Rules
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
Hard-Margin Active Linear Regression
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Momentum Particle Maximum Likelihood
Text-to-LoRA: Instant Transformer Adaption
Free Process Rewards without Process Labels
Learning to rank: from pairwise approach to listwise approach
Why Discretization Works for Naive Bayesian Classifiers
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
Bregman Neural Networks
Tackling the Poor Assumptions of Naive Bayes Text Classifiers
Causal Bounds in Quasi-Markovian Graphs
Robust Multi-Objective Bayesian Optimization Under Input Noise
T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model
Delayed Feedback in Kernel Bandits
Variance Control for Distributional Reinforcement Learning
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Distinguishing Cause from Effect with Causal Velocity Models
Mixtures of Conditional Maximum Entropy Models
Dual Mirror Descent for Online Allocation Problems
On the Convergence and Robustness of Adversarial Training
Lower-Bounded Proper Losses for Weakly Supervised Classification
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo
La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation
Graph-Based Relational Concept Learning
On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network
TSP: A Two-Sided Smoothed Primal-Dual Method for Nonconvex Bilevel Optimization
Probably Approximately Global Robustness Certification
Feedback Loops With Language Models Drive In-Context Reward Hacking
Active Ranking of Experts Based on their Performances in Many Tasks
Neural Kernels Without Tangents
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation
Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization
Scaling Spherical CNNs
Run-off Election: Improved Provable Defense against Data Poisoning Attacks
Learnware Specification via Dual Alignment
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
Online Learning with Abstention
Sparse Shrunk Additive Models
NP-Match: When Neural Processes meet Semi-Supervised Learning
Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models
A Framework for Multi-Paradigmatic Learning
Learning an Internal Dynamics Model from Control Demonstration
Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning
The Impact of Record Linkage on Learning from Feature Partitioned Data
Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models
Relational Active Learning for Joint Collective Classification Models
Spectral clustering based on the graph p -Laplacian
Geometric Mean Metric Learning
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
Fast approximation of matrix coherence and statistical leverage
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks
Bayesian haplo-type inference via the dirichlet process
A DC Programming Approach for Sparse Eigenvalue Problem
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
An Asymptotic Analysis of Speedup Learning
Practical Performance Guarantees for Pipelined DNN Inference
Improving Decision-Making on the Basis of Experience
Bilinear Classes: A Structural Framework for Provable Generalization in RL
Multi-Objective Population Based Training
Optimal Transport for Structure Learning Under Missing Data
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Margin-based Neural Network Watermarking
Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning
Oblivious Sketching for Logistic Regression
Supervised Off-Policy Ranking
FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion
Fast Training of Nonlinear Embedding Algorithms
Algorithms for bounding contribution for histogram estimation under user-level privacy
Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations
Abstracting Concepts with Inverse Resolution
Aligning Protein Conformation Ensemble Generation with Physical Feedback
Training restricted Boltzmann machines using approximations to the likelihood gradient
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Benchmarking Deep Reinforcement Learning for Continuous Control
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions
Innovation Pursuit: A New Approach to the Subspace Clustering Problem
Greedy when Sure and Conservative when Uncertain about the Opponents
Do Bayesian Neural Networks Actually Behave Like Bayesian Models?
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Learning Efficient Structured Sparse Models
Constant Curvature Graph Convolutional Networks
Reinforcement learning by reward-weighted regression for operational space control
Statistically Efficient Off-Policy Policy Gradients
Topological Signatures of Adversaries in Multimodal Alignments
Coordinate-descent for learning orthogonal matrices through Givens rotations
Neural Network Attributions: A Causal Perspective
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
Fast Exact Unlearning for In-Context Learning Data for LLMs
Topic Modeling via Full Dependence Mixtures
Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer
Quantum clustering algorithms
K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
Optimal Densification for Fast and Accurate Minwise Hashing
Efficient Algorithms for Adversarial Contextual Learning
GenCO: Generating Diverse Designs with Combinatorial Constraints
Improving Graph Neural Networks with Learnable Propagation Operators
SlimLLM: Accurate Structured Pruning for Large Language Models
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling
KBG : A Knowledge Based Generalizer
Remember and Forget for Experience Replay
A Bayesian approach to protein model quality assessment
Latent Action Learning Requires Supervision in the Presence of Distractors
Structure-preserving GANs
COFFIN: A Computational Framework for Linear SVMs
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
Boosted Fitted Q-Iteration
How Powerful are Spectral Graph Neural Networks
Stability and Hypothesis Transfer Learning
In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval
LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions
Learning Tactical Plans for Pilot Aiding
Online Learning with Bounded Recall
Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World
SVM Classifier Estimation from Group Probabilities
A needle in a haystack: local one-class optimization
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
Kernel PLS-SVC for Linear and Nonlinear Classification
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization
Tensor Belief Propagation
Asynchronous Byzantine Machine Learning (the case of SGD)
ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions
Finding Variational Structure in Data by Cross-Entropy Optimization
Fast transpose methods for kernel learning on sparse data
Relational Invariant Learning for Robust Solvation Free Energy Prediction
Boosting on a Budget: Sampling for Feature-Efficient Prediction
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
Strategic Classification Made Practical
DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent)
The Teaching Dimension of Linear Learners
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
Experiments with a New Boosting Algorithm
Explicit Discovery of Nonlinear Symmetries from Dynamic Data
Light RUMs
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers
Trainability, Expressivity and Interpretability in Gated Neural ODEs
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Multivariate Submodular Optimization
The First Phase of Real-World Discovery: Determining Repeatability and Error of Experiments
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
Causal Effect Identification in LiNGAM Models with Latent Confounders
Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents
Traversing Between Modes in Function Space for Fast Ensembling
Escaping Saddles with Stochastic Gradients
An Infinite Hidden Markov Model With Similarity-Biased Transitions
On the Importance of Gaussianizing Representations
Multicalibration as Boosting for Regression
A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon
From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble
Robust Learning for Data Poisoning Attacks
Scalable Computation of Causal Bounds
MERIT: Maximum-normalized Element-wise Ratio for Language Model Large-batch Training
Learning Procedures from Interactive Natural Language Instructions
Direct convex relaxations of sparse SVM
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain
Kernel Mean Matching for Content Addressability of GANs
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
Model-Based Exploration in Monitored Markov Decision Processes
On Kernel Methods for Relational Learning
Links between perceptrons, MLPs and SVMs
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient