yusuke1997/dblp-id-title huggingface dataset:
CogMath: Assessing LLMs' Authentic Mathematical Ability from a Human Cognitive Perspective
Extreme F-measure Maximization using Sparse Probability Estimates
Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration
Meta-Learning Hypothesis Spaces for Sequential Decision-making
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
On Sparse Nonparametric Conditional Covariance Selection
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Probabilistic Attention-to-Influence Neural Models for Event Sequences
On-Line Portfolio Selection with Moving Average Reversion
Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events
Sampling Binary Data by Denoising through Score Functions
On the Limitations of First-Order Approximation in GAN Dynamics
Globally-Robust Neural Networks
Deep Causal Metric Learning
Super Deep Contrastive Information Bottleneck for Multi-modal Clustering
Why Random Pruning Is All We Need to Start Sparse
Representing Unordered Data Using Complex-Weighted Multiset Automata
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
PixelTransformer: Sample Conditioned Signal Generation
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
Simple and Effective VAE Training with Calibrated Decoders
Variance Reduction in Stochastic Particle-Optimization Sampling
Hierarchical Gaussian process latent variable models
X-Hacking: The Threat of Misguided AutoML
Probabilistic Bilevel Coreset Selection
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
Tractable Control for Autoregressive Language Generation
Rank-One Matrix Pursuit for Matrix Completion
Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
Sparse PCA through Low-rank Approximations
Fast and effective kernels for relational learning from texts
Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
Proceedings of the 4th Workshop on Machine Learning for Interactive Systems (MLIS-2015)
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
Function factorization using warped Gaussian processes
BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent
Pixel-level Certified Explanations via Randomized Smoothing
Causal Conceptions of Fairness and their Consequences
Network Morphism
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm
Removing systematic errors for exoplanet search via latent causes
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Consistency Analysis for Binary Classification Revisited
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Convergence Guarantees for the DeepWalk Embedding on Block Models
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits
Preference Learning for AI Alignment: a Causal Perspective
BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces
New Sample Complexity Bounds for Sample Average Approximation in Heavy-Tailed Stochastic Programming
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
On the Diminishing Returns of Width for Continual Learning
High-Dimensional Geometric Streaming for Nearly Low Rank Data
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Oblivious Sketching-based Central Path Method for Linear Programming
Pruning nearest neighbor cluster trees
SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
CoSER: Coordinating LLM-Based Persona Simulation of Established Roles
Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
A graphical model for chord progressions embedded in a psychoacoustic space
Cradle: Empowering Foundation Agents towards General Computer Control
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Position: Data-driven Discovery with Large Generative Models
Learning Utilities from Demonstrations in Markov Decision Processes
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding
Private Adaptive Optimization with Side information
Spurious Valleys and Clustering Behavior of Neural Networks
Sufficient Dimensionality Reduction - A novel Analysis Method
Exact Optimal Accelerated Complexity for Fixed-Point Iterations
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning
MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition
A Closer Look at Multimodal Representation Collapse
Provable Maximum Entropy Manifold Exploration via Diffusion Models
Discrete and Continuous Difference of Submodular Minimization
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
Constraint-based Learning of Long Relational Concepts
Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
Zero-Shot Knowledge Distillation in Deep Networks
Towards Robust Influence Functions with Flat Validation Minima
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
Learning unknown ODE models with Gaussian processes
Pruning Improves Heuristic Search for Cost-Sensitive Learning
Fairness with Adaptive Weights
Pachinko allocation: DAG-structured mixture models of topic correlations
Multiple-Instance Learning for Natural Scene Classification
Demeaned Sparse: Efficient Anomaly Detection by Residual Estimate
Multi-task learning for HIV therapy screening
StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning
A dependence maximization view of clustering
Real-Time Optimisation for Online Learning in Auctions
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
The knockoff filter for FDR control in group-sparse and multitask regression
pathGCN: Learning General Graph Spatial Operators from Paths
Controllability-Aware Unsupervised Skill Discovery
Prediction Rule Reshaping
Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response
Training Binary Neural Networks through Learning with Noisy Supervision
Nonparametric variable importance using an augmented neural network with multi-task learning
A comparison of tight generalization error bounds
Analysis and extension of spectral methods for nonlinear dimensionality reduction
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Multi-Objective GFlowNets
Optimal Algorithms for Mean Estimation under Local Differential Privacy
UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
PiD: Generalized AI-Generated Images Detection with Pixelwise Decomposition Residuals
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate
Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains
Collapsed Variational Inference for Sum-Product Networks
Federated Continual Learning with Weighted Inter-client Transfer
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Probabilistic Concept Formation in Relational Domains
Adaptive Whitening in Neural Populations with Gain-modulating Interneurons
No-Regret Algorithms for Heavy-Tailed Linear Bandits
Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems
Policy Filtration for RLHF to Mitigate Noise in Reward Models
Action Matching: Learning Stochastic Dynamics from Samples
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Implicit Online Learning
Discovering options from example trajectories
Concept Simplification and Prediction Accuracy
The Power of Adaptivity for Stochastic Submodular Cover
Combined central and subspace clustering for computer vision applications
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Online Episodic Convex Reinforcement Learning
Algorithms for Inverse Reinforcement Learning
On Characterizing GAN Convergence Through Proximal Duality Gap
Estimating α-Rank from A Few Entries with Low Rank Matrix Completion
Weighted Graph Clustering with Non-Uniform Uncertainties
STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering
Stable Differentiable Causal Discovery
Conformal Predictions under Markovian Data
Gridded Transformer Neural Processes for Spatio-Temporal Data
Black Box FDR
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
On the Predictability of Pruning Across Scales
Efficient Classification of Massive, Unsegmented Datastreams
G 2 CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters
GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry
A Fixed-Point Approach for Causal Generative Modeling
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
Adaptive Flow Matching for Resolving Small-Scale Physics
A fast linear separability test by projection of positive points on subspaces
Predicting Rare Events by Shrinking Towards Proportional Odds
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
Rejuvenating image-GPT as Strong Visual Representation Learners
Individual Fairness for k-Clustering
Ordinal Mixed Membership Models
Localizing Task Information for Improved Model Merging and Compression
Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images
Linear Regression under Fixed-Rank Constraints: A Riemannian Approach
Ranking with ordered weighted pairwise classification
GALOIS: An Order-Theoretic Approach to Conceptual Clustering
Randomized Nonlinear Component Analysis
Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty
Characterizing Well-Behaved vs. Pathological Deep Neural Networks
Probabilistic ODE Solutions in Millions of Dimensions
Provable Reinforcement Learning with a Short-Term Memory
Multi-classification by categorical features via clustering
Exact Rule Learning via Boolean Compressed Sensing
Constructive Induction Using Fragmentary Knowledge
Learning Active Classifiers
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
Gradient Inversion of Multimodal Models
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
What is decreased by the max-sum arc consistency algorithm?
The Statistical Scope of Multicalibration
Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics
Estimating local optimums in EM algorithm over Gaussian mixture model
Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Hybrid Reinforcement Learning from Offline Observation Alone
PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Feature Selection in High-Dimensional Classification
Integrated Learning in a real Domain
Semi-Supervised Blind Quality Assessment with Confidence-quantifiable Pseudo-label Learning for Authentic Images
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Blossom: an Anytime Algorithm for Computing Optimal Decision Trees
Learning to Generate with Memory
A statistical perspective on distillation
User-level Private Stochastic Convex Optimization with Optimal Rates
LIMEFLDL: A Local Interpretable Model-Agnostic Explanations Approach for Label Distribution Learning
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Low Latency Privacy Preserving Inference
On the Limitations of Representing Functions on Sets
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks
Equivariance Through Parameter-Sharing
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Grammatical inference as a principal component analysis problem
Rademacher Complexity for Adversarially Robust Generalization
The many faces of optimism: a unifying approach
Synthetic data for model selection
Learning Models from Data with Measurement Error: Tackling Underreporting
Simple Policy Optimization
Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
Frank-Wolfe with Subsampling Oracle
Symmetry-Aware Robot Design with Structured Subgroups
Dimensionality reduction and generalization
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Computing Parametric Ranking Models via Rank-Breaking
VIMA: Robot Manipulation with Multimodal Prompts
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Consistent Long-Term Forecasting of Ergodic Dynamical Systems
Sharpness-Aware Data Generation for Zero-shot Quantization
EPIC: Efficient Position-Independent Caching for Serving Large Language Models
Hyper-Transforming Latent Diffusion Models
Bayesian Nonparametric Matrix Factorization for Recorded Music
DocVXQA: Context-Aware Visual Explanations for Document Question Answering
Proceedings of the Ninth International Workshop on Machine Learning (ML 1992), Aberdeen, Scotland, UK, July 1-3, 1992
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
Fast Minimum Training Error Discretization
On Explaining Equivariant Graph Networks via Improved Relevance Propagation
Feature extraction via generalized uncorrelated linear discriminant analysis
Open-Vocabulary Universal Image Segmentation with MaskCLIP
A Complete Analysis of the l_1, p Group-Lasso
Dynamic Correlation Clustering in Sublinear Update Time
Differentiable Simulations for Enhanced Sampling of Rare Events
Hyperband-based Bayesian Optimization for Black-box Prompt Selection
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
Efficient Representation Learning via Adaptive Context Pooling
Offline Learning for Combinatorial Multi-armed Bandits
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
N2GON: Neural Networks for Graph-of-Net with Position Awareness
Simplified Temporal Consistency Reinforcement Learning
End-to-End Learning for the Deep Multivariate Probit Model
Adversarial Nonnegative Matrix Factorization
Deep k-NN for Noisy Labels
Dependent Relational Gamma Process Models for Longitudinal Networks
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
COPAL: Continual Pruning in Large Language Generative Models
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
From Inverse Optimization to Feasibility to ERM
A Deep Reinforcement Learning Perspective on Internet Congestion Control
The matrix stick-breaking process for flexible multi-task learning
K-means recovers ICA filters when independent components are sparse
Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
Effective Neural Topic Modeling with Embedding Clustering Regularization
What can large language models do for sustainable food?
Learning from Queries and Examples with Tree-structured Bias
Out-of-Distribution Detection with Deep Nearest Neighbors
Mixtures of Factor Analyzers
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Generating 3D Molecules for Target Protein Binding
Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching
MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means
Stability and Generalization Capability of Subgraph Reasoning Models for Inductive Knowledge Graph Completion
Test-Time Model Adaptation with Only Forward Passes
Deep Generative Models for Relational Data with Side Information
Feature Selection for Unbalanced Class Distribution and Naive Bayes
Learning first-order probabilistic models with combining rules
The Effects of Training Set Size on Decision Tree Complexity
Stealix: Model Stealing via Prompt Evolution
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
Revision Cost for Theory Refinement
Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
Computer Modelling of Acquisition Orders in Child Language
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space
Structured Convolutional Kernel Networks for Airline Crew Scheduling
Detecting and Correcting for Label Shift with Black Box Predictors
Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective
Heterogeneity for the Win: One-Shot Federated Clustering
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
Learning to Groove with Inverse Sequence Transformations
Quantum Positional Encodings for Graph Neural Networks
Towards a better understanding of representation dynamics under TD-learning
Learning and Planning in Complex Action Spaces
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Imitation Learning from Purified Demonstrations
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks
Minimax M-estimation under Adversarial Contamination
Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods
Stochastic Variance-Reduced Cubic Regularized Newton Method
Scalable Sobolev IPM for Probability Measures on a Graph
MOTS: Minimax Optimal Thompson Sampling
Learning Robust Neural Processes with Risk-Averse Stochastic Optimization
Optimally Controllable Perceptual Lossy Compression
Toward Understanding the Importance of Noise in Training Neural Networks
Online Speculative Decoding
Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training
Optimizing Tensor Network Contraction Using Reinforcement Learning
Data Mining and Machine Learning (Abstract)
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
Yes, but Did It Work?: Evaluating Variational Inference
On Volume Minimization in Conformal Regression
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
Distributed Inference for Dirichlet Process Mixture Models
Transfer of Samples in Policy Search via Multiple Importance Sampling
Explain Temporal Black-Box Models via Functional Decomposition
Subgoal-Guided Policy Heuristic Search with Learned Subgoals
DIME: Diffusion-Based Maximum Entropy Reinforcement Learning
LESS: Selecting Influential Data for Targeted Instruction Tuning
Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation
Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders
Active Deep Probabilistic Subsampling
Commutative Lie Group VAE for Disentanglement Learning
Unsupervised Models of Images by Spikeand-Slab RBMs
A Generalized Kernel Approach to Structured Output Learning
Regression with the optimised combination technique
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
Meta-Black-Box-Optimization through Offline Q-function Learning
VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty
Learning Discriminative Fisher Kernels
Revision of Production System Rule-Bases
Learning Imbalanced Data with Beneficial Label Noise
Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs
Deep Continuous Networks
MixFlows: principled variational inference via mixed flows
Generalization analysis of listwise learning-to-rank algorithms
Path-Guided Particle-based Sampling
Optimizing Black-box Metrics with Adaptive Surrogates
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
Up or Down? Adaptive Rounding for Post-Training Quantization
Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing
Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Convergence of synchronous reinforcement learning with linear function approximation
Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Sample Complexity of Correlation Detection in the Gaussian Wigner Model
Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
Bandit-based optimization on graphs with application to library performance tuning
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Robust Explanation for Free or At the Cost of Faithfulness
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
On the Tractability of SHAP Explanations under Markovian Distributions
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
Fast Incomplete Multi-view Clustering by Flexible Anchor Learning
Enhancing Simple Models by Exploiting What They Already Know
Comparing Systems and analyzing Functions to Improve Constructive Induction
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer
Improving Diversity in Language Models: When Temperature Fails, Change the Loss
Is it a Pocket or a Purse? Tighly Coupled Theory and Data Driven Learing
A theoretical analysis of Model-Based Interval Estimation
Concentration Inequalities for Conditional Value at Risk
Tutorial summary: The neuroscience of reinforcement learning
Lookahead-Bounded Q-learning
Jump-Start Reinforcement Learning
Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator