yusuke1997/dblp-id-title huggingface dataset:
On the linearity of large non-linear models: when and why the tangent kernel is constant
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Nesterov acceleration despite very noisy gradients
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
Task-agnostic Exploration in Reinforcement Learning
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models
Factored Semi-Tied Covariance Matrices
Instance-Conditional Knowledge Distillation for Object Detection
Natural Counterfactuals With Necessary Backtracking
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
Cooperative Graphical Models
Fast Two-Sample Testing with Analytic Representations of Probability Measures
An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments
LoRA-GA: Low-Rank Adaptation with Gradient Approximation
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
On the Gini-impurity Preservation For Privacy Random Forests
Matérn Gaussian Processes on Riemannian Manifolds
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
Provable Tempered Overfitting of Minimal Nets and Typical Nets
Higher-Order Spectral Clustering of Directed Graphs
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
Depth-discriminative Metric Learning for Monocular 3D Object Detection
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Artefactual Structure from Least-Squares Multidimensional Scaling
Global Analytic Solution for Variational Bayesian Matrix Factorization
Large Scale Hidden Semi-Markov SVMs
Disconnected Manifold Learning for Generative Adversarial Networks
Face Reconstruction from Voice using Generative Adversarial Networks
Limits on Testing Structural Changes in Ising Models
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Redundant representations help generalization in wide neural networks
MRI Banding Removal via Adversarial Training
AirSketch: Generative Motion to Sketch
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
Differential Privacy Over Riemannian Manifolds
Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
Interactive Structure Learning with Structural Query-by-Committee
Faster Rates in Regression via Active Learning
Credit Attribution and Stable Compression
SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark
SOL: Sampling-based Optimal Linear bounding of arbitrary scalar functions
Learning Multiple Tasks using Shared Hypotheses
Hierarchical Question-Image Co-Attention for Visual Question Answering
Adversarial Weight Perturbation Helps Robust Generalization
Optimal Transport for Treatment Effect Estimation
Neural Concept Binder
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Optimistic Verifiable Training by Controlling Hardware Nondeterminism
Adaptive Important Region Selection with Reinforced Hierarchical Search for Dense Object Detection
Latent Structured Active Learning
Scalable Transformer for PDE Surrogate Modeling
On the Reliability of Clustering Stability in the Large Sample Regime
Regret Bounds for Risk-Sensitive Reinforcement Learning
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory
Manifold Denoising
Gaussian and Wishart Hyperkernels
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Optimal Depth Neural Networks for Multiplication and Related Problems
Recursive Sampling for the Nystrom Method
Adapting Neural Architectures Between Domains
Model-Based Domain Generalization
Generalizable Singular Value Decomposition for Ill-posed Datasets
Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition, EMC2@NeurIPS 2019, Vancouver, Canada, December 13, 2019
Linearly-solvable Markov decision problems
ChatCam: Empowering Camera Control through Conversational AI
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
Subset Selection under Noise
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
Improving the Expected Improvement Algorithm
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Pattern Recognition from One Example by Chopping
Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems
Scalable Deep Generative Relational Model with High-Order Node Dependence
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
High-dimensional (Group) Adversarial Training in Linear Regression
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Budgeted stream-based active learning via adaptive submodular maximization
PAC-Bayesian Theory Meets Bayesian Inference
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting
HOI Analysis: Integrating and Decomposing Human-Object Interaction
Dynamic Bayesian Networks for Brain-Computer Interfaces
Scalable Belief Propagation via Relaxed Scheduling
Hyperbolic Graph Neural Networks
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
Ask, Attend, Attack: An Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
Chasing Sparsity in Vision Transformers: An End-to-End Exploration
Learning to Play the Game of Chess
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization
E-Motion: Future Motion Simulation via Event Sequence Diffusion
Continuous Parametric Optical Flow
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
Differentially Private Empirical Risk Minimization under the Fairness Lens
Sharpness-Aware Minimization Leads to Low-Rank Features
Semi-supervised Learning with Deep Generative Models
GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Few-Shot Task Learning through Inverse Generative Modeling
Diverse Randomized Agents Vote to Win
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
MathNAS: If Blocks Have a Role in Mathematical Architecture Design
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Spectral Co-Distillation for Personalized Federated Learning
On Learning Necessary and Sufficient Causal Graphs
Local Bandit Approximation for Optimal Learning Problems
Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting
Using Unity to Help Solve Reinforcement Learning
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
Reinforcement Learning with Function Approximation Converges to a Region
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Gradient Descent for General Reinforcement Learning
Composing graphical models with neural networks for structured representations and fast inference
DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States
Parametric model reduction of mean-field and stochastic systems via higher-order action matching
Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing
Adapting Fairness Interventions to Missing Values
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise
Optimization with Artificial Neural Network Systems: A Mapping Principle and a Comparison to Gradient Based Methods
Parsel🦆: Algorithmic Reasoning with Language Models by Composing Decompositions
A Condition Number for Joint Optimization of Cycle-Consistent Networks
Computing Linear Restrictions of Neural Networks
A Passive Shared Element Analog Electrical Cochlea
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Adversarial Feature Desensitization
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
A Filtering Approach to Stochastic Variational Inference
Breaking the Dilemma of Medical Image-to-image Translation
Conditional Random Fields for Object Recognition
Contrastive losses as generalized models of global epistasis
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Beat Tracking the Graphical Model Way
Classification of Electroencephalogram Using Artificial Neural Networks
REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
Bayesian inference for low rank spatiotemporal neural receptive fields
A Computer Simulation of Cerebral Neocortex: Computational Capabilities of Nonlinear Neural Networks
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
An $\alpha$-regret analysis of Adversarial Bilateral Trade
Learning Chordal Markov Networks via Branch and Bound
Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable
An Information-theoretic Approach to Distribution Shifts
Vertex Identification in High Energy Physics Experiments
Combining Estimators Using Non-Constant Weighting Functions
Self-Supervised GANs with Label Augmentation
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
Full-Atom Peptide Design with Geometric Latent Diffusion
Dangers of Bayesian Model Averaging under Covariate Shift
One-sample Guided Object Representation Disassembling
Recipe for a General, Powerful, Scalable Graph Transformer
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
Multi-Objective Meta Learning
A Gradient-Based Boosting Algorithm for Regression Problems
A Meta-Analysis of Overfitting in Machine Learning
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations
BIOT: Biosignal Transformer for Cross-data Learning in the Wild
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
Dataset Distillation via Factorization
Interference in Learning Internal Models of Inverse Dynamics in Humans
Very loopy belief propagation for unwrapping phase images
A General Method for Robust Learning from Batches
Neural Network Based Model Predictive Control
A/B Testing in Dense Large-Scale Networks: Design and Inference
Fast Computation of Posterior Mode in Multi-Level Hierarchical Models
Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
Forecasting Future World Events With Neural Networks
Rectified Factor Networks
A Convex Duality Framework for GANs
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention
NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
Markovian Interference in Experiments
Identifying Causal Effects Under Functional Dependencies
Compact Generalized Non-local Network
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
Hamiltonian Score Matching and Generative Flows
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
Minimax-optimal Inference from Partial Rankings
Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech
Mining Internet-Scale Software Repositories
Learning Prices for Repeated Auctions with Strategic Buyers
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
Constrained Sampling with Primal-Dual Langevin Monte Carlo
Shape and Material from Sound
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Provably efficient multi-task reinforcement learning with model transfer
A Superadditive-Impairment Theory of Optic Aphasia
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
Adversarial Regularizers in Inverse Problems
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Escaping the Gravitational Pull of Softmax
Spiking Inputs to a Winner-take-all Network
The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World
Optimal Efficiency-Envy Trade-Off via Optimal Transport
On Measuring Excess Capacity in Neural Networks
Maximum Likelihood Estimation of Intrinsic Dimension
Noise Contrastive Alignment of Language Models with Explicit Rewards
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures
Extracting and Learning an Unknown Grammar with Recurrent Neural Networks
Beyond Convexity: Online Submodular Minimization
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks
BAKU: An Efficient Transformer for Multi-Task Policy Learning
Context Selection for Embedding Models
A Recurrent Neural Network Model of Velocity Storage in the Vestibulo-Ocular Reflex
Optimal-er Auctions through Attention
Topmoumoute Online Natural Gradient Algorithm
State Regularized Policy Optimization on Data with Dynamics Shift
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification
Flexible sampling of discrete data correlations without the marginal distributions
Red Teaming Deep Neural Networks with Feature Synthesis Tools
Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
Searching for Higgs Boson Decay Modes with Deep Learning
Image Reconstruction by Linear Programming
EM Algorithms for PCA and SPCA
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Geometry of naturalistic object representations in recurrent neural network models of working memory
With Friends Like These, Who Needs Adversaries?
Monotone k-Submodular Function Maximization with Size Constraints
Dissecting Neural ODEs
Equivariant Single View Pose Prediction Via Induced and Restriction Representations
Learning structured densities via infinite dimensional exponential families
Compositional generalization through abstract representations in human and artificial neural networks
Sparse Bayesian Generative Modeling for Compressive Sensing
DäRF: Boosting Radiance Fields from Sparse Input Views with Monocular Depth Adaptation
Paths to Equilibrium in Games
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks
A 'Neural' Network that Learns to Play Backgammon
ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing
Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization
VLSI Implementation of Motion Centroid Localization for Autonomous Navigation
Predicting spike times from subthreshold dynamics of a neuron
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
Speech Recognition: Statistical and Neural Information Processing Approaches
Multiple Timescales of Adaptation in a Neural Code
Neural Bootstrapper
Unintended Selection: Persistent Qualification Rate Disparities and Interventions
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond 1+α Moments
LRM-Zero: Training Large Reconstruction Models with Synthesized Data
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
Sparse Recovery with Brownian Sensing
Retaining Knowledge for Learning with Dynamic Definition
Algebraic Set Kernels with Application to Inference Over Local Image Representations
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy
HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process
Doubly Mild Generalization for Offline Reinforcement Learning
Space and Time Efficient Kernel Density Estimation in High Dimensions
Biasing Approximate Dynamic Programming with a Lower Discount Factor
Divert More Attention to Vision-Language Tracking
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Co-occurrence is not Factual Association in Language Models
Deep reconstruction of strange attractors from time series
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms
A Recurrent Latent Variable Model for Sequential Data
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
\ell_1-regression with Heavy-tailed Distributions
Online Reinforcement Learning in Stochastic Games
On-the-Fly Adapting Code Summarization on Trainable Cost-Effective Language Models
Data Pruning via Moving-one-Sample-out
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Flexible task abstractions emerge in linear networks with fast and bounded units
The Limits of Differential Privacy in Online Learning
Stability Analysis and Generalization Bounds of Adversarial Training
Bayesian Modelling of fMRI lime Series
Learning Fuzzy Rule-Based Neural Networks for Control
Constrained GPI for Zero-Shot Transfer in Reinforcement Learning
Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices
Infinite Hidden Semi-Markov Modulated Interaction Point Process
An intriguing failing of convolutional neural networks and the CoordConv solution
Convergence of Alternating Gradient Descent for Matrix Factorization
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
Global Coordination of Local Linear Models
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Ranking with Large Margin Principle: Two Approaches
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli
TrueSkill Through Time: Revisiting the History of Chess
A graphon-signal analysis of graph neural networks
Clustering with Same-Cluster Queries
Rational Parametrizations of Neural Networks
Universal Boosting Variational Inference
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding
Nimbus: Secure and Efficient Two-Party Inference for Transformers
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
Sensory Adaptation within a Bayesian Framework for Perception
Image Representations for Facial Expression Coding
Training Generative Adversarial Networks with Limited Data
The power of absolute discounting: all-dimensional distribution estimation
Finite State Automata that Recurrent Cascade-Correlation Cannot Represent
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
Convolutional Gaussian Processes
On the Inductive Bias of Neural Tangent Kernels
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts
Vision Mamba Mender
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
On Sparsity and Overcompleteness in Image Models
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Interventions, Where and How? Experimental Design for Causal Models at Scale
Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors
Tracking the Best Expert in Non-stationary Stochastic Environments
Inserting Anybody in Diffusion Models via Celeb Basis
End-to-End Goal-Driven Web Navigation
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path
Implementation of Neural Hardware with the Neural VLSI of URAN in Applications with Reduced Representations
Multi-Head Adapter Routing for Cross-Task Generalization
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
Optimization of Inter-group criteria for clustering with minimum size constraints
Generalization Dynamics in LMS Trained Linear Networks
Stationary Activations for Uncertainty Calibration in Deep Learning
Parallel Correlation Clustering on Big Graphs
Training Transformers Together
Symmetry-Informed Governing Equation Discovery
SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding
Bad Global Minima Exist and SGD Can Reach Them
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
General Table Completion using a Bayesian Nonparametric Model
Generalized Hindsight for Reinforcement Learning
The Mondrian Process
SGD with shuffling: optimal rates without component convexity and large epoch requirements
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
Molecule Generation with Fragment Retrieval Augmentation
Metric Learning with Multiple Kernels
Learning Modular Simulations for Homogeneous Systems
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care
Nearly-Tight Bounds for Testing Histogram Distributions
"Congruent" and "Opposite" Neurons: Sisters for Multisensory Integration and Segregation
Robust Lipschitz Bandits to Adversarial Corruptions
Emergence of Global Structure from Local Associations
A Universal Growth Rate for Learning with Smooth Surrogate Losses