yusuke1997/dblp-id-title huggingface dataset:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
Making Fisher Discriminant Analysis Scalable
Last Switch Dependent Bandits with Monotone Payoff Functions
Organize the Web: Constructing Domains Enhances Pre-Training Data Curation
On the Weight Dynamics of Deep Normalized Networks
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Automatic Posterior Transformation for Likelihood-Free Inference
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models
SpeCache: Speculative Key-Value Caching for Efficient Generation of LLMs
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
On the Equivalence Between Temporal and Static Equivariant Graph Representations
Policy Gradient with Tree Expansion
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
Adversarial Masking for Self-Supervised Learning
Towards a Self-contained Data-driven Global Weather Forecasting Framework
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
Data-efficient Large Vision Models through Sequential Autoregression
Policy Optimization with Demonstrations
Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
Regression with Multi-Expert Deferral
Efficient Regret Minimization in Non-Convex Games
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems
The Constrained Weight Space SVM: Learning with Ranked Features
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
State-based Classification of Finger Gestures from Electromyographic Signals
Multiobjective distribution matching
UnICORNN: A recurrent model for learning very long time dependencies
Multi-Objective Causal Bayesian Optimization
Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness
Constructive Induction by Analogy
Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, July 8-12, 1997
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
A Two-Step Computation of the Exact GAN Wasserstein Distance
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
How Transformers Learn Structured Data: Insights From Hierarchical Filtering
Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization
Democratic approximation of lexicographic preference models
OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks
Visual Learning by Evolutionary Feature Synthesis
Adversarial Inputs for Linear Algebra Backends
Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
Comparison of Methods Based on Inverse Resolution
Position: Building Guardrails for Large Language Models Requires Systematic Design
Measure Estimation in the Barycentric Coding Model
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
Data Engineering for Scaling Language Models to 128K Context
Multiplier Bootstrap-based Exploration
Multi-View Masked World Models for Visual Robotic Manipulation
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Inductive Learning with BCT
Beyond Inversion of Resolution
Efficient Nonmyopic Active Search
Bayesian Optimization with Tree-structured Dependencies
Bayesian Adaptation of Network Depth and Width for Continual Learning
Refined Complexity of PCA with Outliers
Stereographic Spherical Sliced Wasserstein Distances
Learning from Noisy Labels with No Change to the Training Process
Framework for Evaluating Faithfulness of Local Explanations
FESSNC: Fast Exponentially Stable and Safe Neural Controller
The Entropy Enigma: Success and Failure of Entropy Minimization
Learning Recurrent Neural Networks with Hessian-Free Optimization
How recurrent networks implement contextual processing in sentiment analysis
EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
Certifiably Byzantine-Robust Federated Conformal Prediction
Learning Cascade Ranking as One Network
Learning to Select a Model in a Changing World
AutoGFM: Automated Graph Foundation Model with Adaptive Architecture Customization
Identifying useful subgoals in reinforcement learning by local graph partitioning
Adaptive Smoothing Gradient Learning for Spiking Neural Networks
A PAC-Bayes Sample-compression Approach to Kernel Methods
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
Encodings for Prediction-based Neural Architecture Search
Mechanistic Neural Networks for Scientific Machine Learning
MemFreezing: A Novel Adversarial Attack on Temporal Graph Neural Networks under Limited Future Knowledge
On Disentangled Representations Learned from Correlated Data
UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning
Online Algorithms for Rent-Or-Buy with Expert Advice
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
Online Prototype Alignment for Few-shot Policy Transfer
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
Moderately Distributional Exploration for Domain Generalization
Privately Learning Smooth Distributions on the Hypercube by Projections
Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization
Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
Robust Autonomy Emerges from Self-Play
Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
Machine Learning Meets Medical Imaging - First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015. Revised Selected Papers
GSM-∞: How Do your LLMs Behave over Infinitely Increasing Reasoning Complexity and Context Length?
Automating the Selection of Proxy Variables of Unmeasured Confounders
Learning to combine distances for complex representations
Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention
Making Linear MDPs Practical via Contrastive Representation Learning
Fraud-Proof Revenue Division on Subscription Platforms
Partial disentanglement for domain adaptation
Unsupervised Representation Learning of Brain Activity via Bridging Voxel Activity and Functional Connectivity
Visual Development and the Acquisition of Binocular Disparity Sensitivities
Relational Clichés: Constraining Induction During Relational Learning
A Theory for Conditional Generative Modeling on Multiple Data Sources
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph
Explanation-Based Learning with Week Domain Theories
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling
Measuring Diversity: Axioms and Challenges
Counterfactual Image Editing
Exploration Control in Reinforcement Learning using Optimistic Model Selection
Self-conditioning Pre-Trained Language Models
Can a Few Decide for Many? The Metric Distortion of Sortition
Robust matching and recognition using context-dependent kernels
ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference
Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
Bayesian online change point detection with Hilbert space approximate Student-t process
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models
The Principal Axes Method for Constructive Induction
Online Streaming Feature Selection
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Minimum Norm Interpolation Meets The Local Theory of Banach Spaces
Projection-free Distributed Online Learning in Networks
The Coherent Loss Function for Classification
Learning Hash Functions Using Column Generation
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
DLP: Dynamic Layerwise Pruning in Large Language Models
Kernel Normalized Cut: a Theoretical Revisit
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams
ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation
Learning from Multiway Data: Simple and Efficient Tensor Regression
Multilinear Multitask Learning
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
Exponentiated Gradient Methods for Reinforcement Learning
Reinforcement learning for optimized trade execution
Understanding the Role of Feedback in Online Learning with Switching Costs
Estimation of Causal Peer Influence Effects
Out-of-Domain Robustness via Targeted Augmentations
Delta Decompression for MoE-based LLMs Compression
MARINA: Faster Non-Convex Distributed Learning with Compression
Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies
A Peer-review Look on Multi-modal Clustering: An Information Bottleneck Realization Method
Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data
Robust multi-task learning with t -processes
Hyperparameter Selection for Imitation Learning
Improving Regressors using Boosting Techniques
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Robust Estimation of Tree Structured Gaussian Graphical Models
General Functional Matrix Factorization Using Gradient Boosting
Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
Mixing Predictions for Online Metric Algorithms
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent
Bandit Online Linear Optimization with Hints and Queries
Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Confident Multiple Choice Learning
Evolutionary MCMC Sampling and Optimization in Discrete Spaces
Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization
Local-to-Global Bayesian Network Structure Learning
Context Consistency Regularization for Label Sparsity in Time Series
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
The Hidden Vulnerability of Distributed Learning in Byzantium
Some Theoretical Aspects of Boosting in the Presence of Noisy Data
LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
Coordinated Reinforcement Learning
Option Decision Trees with Majority Votes
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
Towards Learning to Complete Anything in Lidar
GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
Data Reduction Techniques for Instance-Based Learning from Human/Computer Interface Data
Learning complex motions by sequencing simpler motion templates
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
To Understand Deep Learning We Need to Understand Kernel Learning
Asynchronous Stochastic Gradient Descent with Delay Compensation
On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
Behavior Generation with Latent Actions
Joint Composite Latent Space Bayesian Optimization
Hyperplane margin classifiers on the multinomial manifold
Learning Important Features Through Propagating Activation Differences
On Linear Identifiability of Learned Representations
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Approximation Bounds for Inference using Cooperative Cuts
Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
Metric-Optimized Example Weights
Mean-field Chaos Diffusion Models
Multiaccuracy and Multicalibration via Proxy Groups
Per-Decision Option Discounting
Principled Simplicial Neural Networks for Trajectory Prediction
A Field Guide for Pacing Budget and ROS Constraints
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching
Contextual Reliability: When Different Features Matter in Different Contexts
Cycle Representation Learning for Inductive Relation Prediction
Robustness of Nonlinear Representation Learning
Maximal Initial Learning Rates in Deep ReLU Networks
Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
Mobile Attention: Mobile-Friendly Linear-Attention for Vision Transformers
Understanding Generalization in Quantum Machine Learning with Margins
Field Matching: an Electrostatic Paradigm to Generate and Transfer Data
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models
Bounds on the Classification Error of the Nearest Neighbor Rule
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
Profiling Users of the Velo'v Bike Sharing System
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Reconstructive Neuron Pruning for Backdoor Defense
Extreme Learning to Rank via Low Rank Assumption
Randomized Algorithms for Submodular Function Maximization with a k-System Constraint
Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
Localized multiple kernel learning
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
Batch Value-function Approximation with Only Realizability
On Layer Normalization in the Transformer Architecture
Local Loss Optimization in Operator Models: A New Insight into Spectral Learning
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance
High-Dimensional Prediction for Sequential Decision Making
Neural Tangent Generalization Attacks
Topic Modeling with Nonparametric Markov Tree
The continuous categorical: a novel simplex-valued exponential family
Nonparametric Decomposition of Sparse Tensors
Three new graphical models for statistical language modelling
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization
O-MAPL: Offline Multi-agent Preference Learning
Directly Denoising Diffusion Models
The Role of Learning Algorithms in Collective Action
Optimal and Practical Batched Linear Bandit Algorithm
A Composite Randomized Incremental Gradient Method
Cross-City Latent Space Alignment for Consistency Region Embedding
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform
FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings
Efficient Distributed Learning with Sparsity
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
Smooth Imitation Learning for Online Sequence Prediction
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
Manifold Relevance Determination
A Flexible Diffusion Model
Relativized Options: Choosing the Right Transformation
Streaming Submodular Maximization with Differential Privacy
Breaking through the learning plateaus of in-context learning in Transformer
Causal Structure Learning for Latent Intervened Non-stationary Data
Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
ContextNet: Deep learning for Star Galaxy Classification
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
Using fast weights to improve persistent contrastive divergence
Online Active Regression
Tempered Adversarial Networks
Elucidating the design space of language models for image generation
Outside the Echo Chamber: Optimizing the Performative Risk
PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
Coded-InvNet for Resilient Prediction Serving Systems
Improving the Continuity of Goal-Achievement Ability via Policy Self-Regularization for Goal-Conditioned Reinforcement Learning
Multi-Agent Routing Value Iteration Network
Forward and Reverse Gradient-Based Hyperparameter Optimization
Learning to Aggregate Ordinal Labels by Maximizing Separating Width
Position: Technical Research and Talent is Needed for Effective AI Governance
Provable Algorithms for Inference in Topic Models
Co-EM support vector learning
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures
FlowMM: Generating Materials with Riemannian Flow Matching
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
Implicit Neural Spatial Representations for Time-dependent PDEs
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems
No Oops, You Won't Do It Again: Mechanisms for Self-correction in Crowdsourcing
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration
Predicting Dose-Response Curves with Deep Neural Networks
Probabilistic Classifiers and the Concepts They Recognize
Implicit meta-learning may lead language models to trust more reliable sources
Learning prediction suffix trees with Winnow
xxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
Efficient projections onto the l 1 -ball for learning in high dimensions
Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning
Faster Rates for Private Adversarial Bandits
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
An Information-Theoretic Analysis of In-Context Learning
KIND: Knowledge Integration and Diversion for Training Decomposable Models
Quality-Diversity with Limited Resources
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
Fair yet Asymptotically Equal Collaborative Learning
Isotonic Hawkes Processes
MoH: Multi-Head Attention as Mixture-of-Head Attention
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades
Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling
Explainable AI Methods - A Brief Overview
Fundamental Tradeoffs in Distributionally Adversarial Training
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
Predicate Exchange: Inference with Declarative Knowledge
Locality Preserving Markovian Transition for Instance Retrieval
Improved Regret for Differentially Private Exploration in Linear MDP
Online Graph Dictionary Learning
Label differential privacy and private training data release
Disentangled 3D Scene Generation with Layout Learning
Controlling Underestimation Bias in Constrained Reinforcement Learning for Safe Exploration
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Learning Chomsky-like Grammars for Biological Sequence Families
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
Been There, Done That: Meta-Learning with Episodic Recall
Adaptation-Based Explanation: Explanations as Cases
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
GA-based Learning of Context-Free Grammars using Tabular Representations
One-Shot Generalization in Deep Generative Models
Chip Placement with Diffusion Models
SAND: One-Shot Feature Selection with Additive Noise Distortion
Unimodal Bandits
Adaptive Proximal Gradient Methods Are Universal Without Approximation
A Robust Approach to Numeric Discovery
Continuous Spatiotemporal Transformer
GRAM: A Generative Foundation Reward Model for Reward Generalization
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
An Approach to Anytime Learning
Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
Locally Private Hypothesis Testing
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
Faster Global Minimum Cut with Predictions
Covered Forest: Fine-grained generalization analysis of graph neural networks
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
Discrete Tree Flows via Tree-Structured Permutations
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
Efficiently Access Diffusion Fisher: Within the Outer Product Span Space
Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan
Dynamic Game Theoretic Neural Optimizer
A Minimaximalist Approach to Reinforcement Learning from Human Feedback