yusuke1997/dblp-id-title huggingface dataset:
Bayesian Joint Spike-and-Slab Graphical Lasso
The Variational Predictive Natural Gradient
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition
Synthesizer: Rethinking Self-Attention for Transformer Models
QuEST: Stable Training of LLMs with 1-Bit Weights and Activations
Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources
Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
End-to-End Differentiable Adversarial Imitation Learning
Learnability of the Superset Label Learning Problem
Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration
Invertible Residual Networks
The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy
KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning
Concept Bottleneck Models
Disentangled Generative Models for Robust Prediction of System Dynamics
DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models
Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations
Graph Positional and Structural Encoder
LEMoN: Label Error Detection using Multimodal Neighbors
An Investigation of Transformation-Based Learning in Discourse
Fast Context Adaptation via Meta-Learning
Early Time Classification with Accumulated Accuracy Gap Control
Differentiable Spatial Planning using Transformers
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Hermite Polynomial Features for Private Data Generation
That was fast! Speeding up NN search of high dimensional distributions
Optimizing Mode Connectivity for Class Incremental Learning
ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning
Time Is MattEr: Temporal Self-supervision for Video Transformers
One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework
A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Logistic regression with an auxiliary data source
Stabilizing Sample Similarity in Representation via Mitigating Random Consistency
Avoiding Bias when Aggregating Relational Data with Degree Disparity
Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Auto Speculation
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
A Boosted Maximum Entropy Model for Learning Text Chunking
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
Physics Aware Neural Networks for Unsupervised Binding Energy Prediction
Robust Universal Adversarial Perturbations
Gated Autoencoders with Tied Input Weights
ASA-CoroNet: Adaptive Self-attention Network for COVID-19 Automated Diagnosis using Chest X-ray Images
Optimal Counterfactual Explanations in Tree Ensembles
Ordinal Non-negative Matrix Factorization for Recommendation
Optimal Ridge Regularization for Out-of-Distribution Prediction
Random Latent Exploration for Deep Reinforcement Learning
Uniform Convergence of Rank-weighted Learning
Federated Composite Optimization
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution
Bayesian and Empirical Bayesian Forests
Convergence of Online Learning Algorithm for a Mixture of Multiple Linear Regressions
Learning hierarchical multi-category text classification models
A Universal Unbiased Method for Classification from Aggregate Observations
Cumulants of Hawkes Processes are Robust to Observation Noise
Bandits with Adversarial Scaling
How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation
Fast Allocation of Gaussian Process Experts
Delay-Adaptive Step-sizes for Asynchronous Learning
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
Loss Shaping Constraints for Long-Term Time Series Forecasting
Causal Strategic Linear Regression
Latent Programmer: Discrete Latent Codes for Program Synthesis
Making Large-Scale Nyström Approximation Possible
Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends
Revisiting Fundamentals of Experience Replay
What is Dataset Distillation Learning?
On Variational Inference in Biclustering Models
OTL: A Framework of Online Transfer Learning
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Bundle Selling by Online Estimation of Valuation Functions
A Unified Loss Function in Bayesian Framework for Support Vector Regression
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Conceptual Clustering and Exploratory Data Analysis
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Adaptive Belief Propagation
Word-Level Speech Recognition With a Letter to Word Encoder
VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models
Nonnegative matrix factorization via rank-one downdate
Data-Driven Subgroup Identification for Linear Regression
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
ELTA: An Enhancer against Long-Tail for Aesthetics-oriented Models
Learning Linear Block Error Correction Codes
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity
Stein Points
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Optimizing Large Language Model Training Using FP4 Quantization
Predicting diverse subsets using structural SVMs
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks
FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary
Stochastic Rising Bandits
Naive Bayes Classifiers over Missing Data: Decision and Poisoning
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
MTL-UE: Learning to Learn Nothing for Multi-Task Learning
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Estimating Joint Treatment Effects by Combining Multiple Experiments
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis
A Compilation Target for Probabilistic Programming Languages
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Allocation Requires Prediction Only if Inequality Is Low
BabelTower: Learning to Auto-parallelized Program Translation
Robust Situational Reinforcement Learning in Face of Context Disturbances
Learning Monotonic Probabilities with a Generative Cost Model
Differentiable Model Scaling using Differentiable Topk
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
Communication-Efficient Distributed PCA by Riemannian Optimization
Sample Efficient Demonstration Selection for In-Context Learning
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
Variational Wasserstein gradient flow
TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization
On Estimation in Latent Variable Models
Dimensionality-Driven Learning with Noisy Labels
Max-Margin Multiple-Instance Dictionary Learning
Decomposed Mutual Information Estimation for Contrastive Representation Learning
Robust Automatic Modulation Classification with Fuzzy Regularization
Hi-Patch: Hierarchical Patch GNN for Irregular Multivariate Time Series
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors
Deep Generative Learning via Variational Gradient Flow
Learning to Locate an Object in 3D Space from a Sequence of Camera Images
Geometry of Neural Network Loss Surfaces via Random Matrix Theory
Context-Aware Drift Detection
Rates of Convergence for Variable Resolution Schemes in Optimal Control
Universal Length Generalization with Turing Programs
Switching Linear Dynamics for Variational Bayes Filtering
On the Stepwise Nature of Self-Supervised Learning
SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning
Global curvature for second-order optimization of neural networks
State-Free Inference of State-Space Models: The *Transfer Function* Approach
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Selective Regression under Fairness Criteria
ResearchTown: Simulator of Human Research Community
Refining a Relational Theory with Multiple Faults in the Concept and Subconcepts
Generalization and Robustness Implications in Object-Centric Learning
Optimizing Noise Distributions for Differential Privacy
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Towards Fast Computation of Certified Robustness for ReLU Networks
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Deep Transfer Learning with Joint Adaptation Networks
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Estimating Barycenters of Distributions with Neural Optimal Transport
Kernel Semi-Implicit Variational Inference
On Bayesian bounds
Spectral Subsampling MCMC for Stationary Time Series
Simplifying DINO via Coding Rate Regularization
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, 22 July 2022, Baltimore, Maryland, USA
Iterative Machine Teaching
Describing Differences between Text Distributions with Natural Language
A fast and simple algorithm for training neural probabilistic language models
Revisiting Structured Variational Autoencoders
Fast Algorithm for Generalized Multinomial Models with Ranking Data
Hierarchically Decoupled Imitation For Morphological Transfer
Lognormal and Gamma Mixed Negative Binomial Regression
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models
Adaptive Median Smoothing: Adversarial Defense for Unlearned Text-to-Image Diffusion Models at Inference Time
Towards a Formal Theory of Representational Compositionality
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning
Inducing Process Models from Continuous Data
Observation Interference in Partially Observable Assistance Games
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
Learning Local Invariant Mahalanobis Distances
Analyzing the tree-layer structure of Deep Forests
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
Causal Bandits with Propagating Inference
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms
SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Evaluation of Test-Time Adaptation Under Computational Time Constraints
Personalized Federated Learning via Variational Bayesian Inference
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Iterative Search Attribution for Deep Neural Networks
Best Arm Identification for Stochastic Rising Bandits
Algorithms and Applications for Multitask Learning
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
Bayesian Multi-Task Reinforcement Learning
Temporal Predictive Coding For Model-Based Planning In Latent Space
Test-Time Training Provably Improves Transformers as In-context Learners
The Limits of Tractable Marginalization
Fundamental Limits of Visual Autoregressive Transformers: Universal Approximation Abilities
Learning String Patterns and Tree Patterns from Examples
Crafting Papers on Machine Learning
Discovering Temporal Causal Relations from Subsampled Data
Breaking the Limits of Message Passing Graph Neural Networks
E(n) Equivariant Graph Neural Networks
Distance Metric Learning with Joint Representation Diversification
Structured Stochastic Gradient MCMC
Relative Deviation Margin Bounds
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
A Sampling-Based Method for Tensor Ring Decomposition
Robot trajectory optimization using approximate inference
Mind the Boundary: Coreset Selection via Reconstructing the Decision Boundary
Learning Curves of Stochastic Gradient Descent in Kernel Regression
Position: The Platonic Representation Hypothesis
Deep Boosting
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction
DeepCrossAttention: Supercharging Transformer Residual Connections
Partial Optimality in Cubic Correlation Clustering
Robust Training under Label Noise by Over-parameterization
Neural SDEs as Infinite-Dimensional GANs
Amortized Variational Deep Kernel Learning
Independent factor topic models
Synonymous Variational Inference for Perceptual Image Compression
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
Diffy-S: Learning Robot Operator Schemata from Examples
Incentivizing Exploration with Linear Contexts and Combinatorial Actions
Optimistic Planning by Regularized Dynamic Programming
Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters
A Semismooth Newton Method for Fast, Generic Convex Programming
A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition
DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Reducing Real-world Failures of Approximate Explanation-based Rules
Applied Online Algorithms with Heterogeneous Predictors
TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness
Activation-Descent Regularization for Input Optimization of ReLU Networks
The Ladder: A Reliable Leaderboard for Machine Learning Competitions
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Logistic Variational Bayes Revisited
DPO Meets PPO: Reinforced Token Optimization for RLHF
First-Order Manifold Data Augmentation for Regression Learning
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
In-Context Language Learning: Architectures and Algorithms
Predictive Dynamic Fusion
On the Estimation of Gaussian Mixture Copula Models
Additive Error Guarantees for Weighted Low Rank Approximation
A Mathematical Framework for Studying Representation
Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries
MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency
Large scale genomic sequence SVM classifiers
any4: Learned 4-bit Numeric Representation for LLMs
Classification with Rejection Based on Cost-sensitive Classification
Detecting Any instruction-to-answer interaction relationship: Universal Instruction-to-Answer Navigator for Med-VQA
Graph Neural Networks with a Distribution of Parametrized Graphs
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Mallows ranking models: maximum likelihood estimate and regeneration
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn
LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models
Thompson Sampling for (Combinatorial) Pure Exploration
Clipped Action Policy Gradient
Artificial Universes - Towards a Systematic Approach to Evaluation Algorithms which Learn form Examples
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis
Improving Short-Text Classification using Unlabeled Data for Classification Problems
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
Discriminant analysis in correlation similarity measure space
Learning from labeled and unlabeled data on a directed graph
Efficient displacement convex optimization with particle gradient descent
On Breaking Deep Generative Model-based Defenses and Beyond
Position: Towards Implicit Prompt For Text-To-Image Models
A support vector method for multivariate performance measures
Sharf: Shape-conditioned Radiance Fields from a Single View
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Defense Through Diverse Directions
AutoStep: Locally adaptive involutive MCMC
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Margins, Kernels and Non-linear Smoothed Perceptrons
Feature Subset Selection and Order Identification for Unsupervised Learning
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
A Generalization Theory for Zero-Shot Prediction
Improved Nystrom Low-rank Decomposition with Priors
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
Differentially Private Submodular Maximization: Data Summarization in Disguise
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
Shaping in Reinforcement Learning by Changing the Physics of the Problem
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
The impact of uncertainty on regularized learning in games
Data Amplification: Instance-Optimal Property Estimation
PCA-based Multi-Task Learning: a Random Matrix Approach
Deep Fuzzy Multi-view Learning for Reliable Classification
Towards Domain-Agnostic Contrastive Learning
Controlling the distance to a Kemeny consensus without computing it
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Feature Selection using Stochastic Gates
Learned Optimizers that Scale and Generalize
Expressiveness of Rectifier Networks
Task-Specific Skill Localization in Fine-tuned Language Models
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints
Modelling Time and Location in Topic Models
Classification Under Strategic Self-Selection
Incremental Induction of Topologically Minimal Trees
Matching Normalizing Flows and Probability Paths on Manifolds
MISSL: multiple-instance semi-supervised learning
The Merit of River Network Topology for Neural Flood Forecasting
Large-scale deep unsupervised learning using graphics processors
Identification of Linear Non-Gaussian Latent Hierarchical Structure
Exploration in Metric State Spaces
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
Optimization for Neural Operators can Benefit from Width
The Implicit Fairness Criterion of Unconstrained Learning
Which Tasks Should Be Learned Together in Multi-task Learning?
On Kernelized Multi-armed Bandits
Potential Based Diffusion Motion Planning
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize
On the Relationship Between Explanation and Prediction: A Causal View
Graph Distillation with Eigenbasis Matching
Metadata-conscious anonymous messaging
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret
Layerwise Change of Knowledge in Neural Networks
Structured Cooperative Learning with Graphical Model Priors
Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization
Understanding the Logic of Direct Preference Alignment through Logic
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers
Easy Variational Inference for Categorical Models via an Independent Binary Approximation
GACT: Activation Compressed Training for Generic Network Architectures
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches
Distributed Linear Bandits under Communication Constraints
An Augmentation-Aware Theory for Self-Supervised Contrastive Learning
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Learning Fair Representations
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Contextual Combinatorial Cascading Bandits
Elucidating Flow Matching ODE Dynamics via Data Geometry and Denoisers
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
The Kernelized Stochastic Batch Perceptron
Operationalizing Complex Causes: A Pragmatic View of Mediation
Dynamic Programming Boosting for Discriminative Macro-Action Discovery
Constrained Online Convex Optimization with Polyak Feasibility Steps
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Frequency Bias in Neural Networks for Input of Non-Uniform Density
H-Tuning: Toward Low-Cost and Efficient ECG-based Cardiovascular Disease Detection with Pre-Trained Models
Learning Mixtures of Experts with EM: A Mirror Descent Perspective
Gamma Distribution PCA-Enhanced Feature Learning for Angle-Robust SAR Target Recognition
EncryptedLLM: Privacy-Preserving Large Language Model Inference via GPU-Accelerated Fully Homomorphic Encryption
Approximate Group Fairness for Clustering
Online Time Series Prediction with Missing Data
Trajectory-Based Off-Policy Deep Reinforcement Learning
On the Generalization Benefit of Noise in Stochastic Gradient Descent
Toward Off-Policy Learning Control with Function Approximation
GNOT: A General Neural Operator Transformer for Operator Learning
Large-scale Multi-label Learning with Missing Labels