All papers

The paper formulates continuous symmetry discovery across function families as an inverse symmetry operator problem using differential characteristics.

The paper introduces FORGE, an on-chip datapath that regenerates sparse mixture-of-experts weights from compact seeds during inference.

The paper evaluates symbolic regression, refitting techniques, and language model rankings for inferring tumor-growth laws from sparse observation data.

The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging.

The paper presents a temporal reinforcement learning framework that selects physiologically aligned time windows for motor imagery brain-computer interfaces.

The paper provides theoretical convergence and tracking error bounds for Matrix Shampoo optimization in nonstationary stochastic settings.

The paper provides two modifications to make tensor decoupling methods more robust to magnitude mismatches and eliminate manual spline tuning.

The paper presents AutoConvexRelax, which learns a reinforcement learning policy to automatically construct valid convex relaxations for nonconvex optimization problems.

The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data.

The paper introduces HarnessSR to jointly optimize agent workflows and prompts for discovering symbolic regression equations from data.

The paper presents GenTuRBO, using pretrained generative models as adaptive proposal trust regions for discrete Bayesian optimization tasks.

The paper proves constant-depth rational-weight graph neural networks match the 1-WL expressiveness bound under Schanuel's conjecture from number theory.

The paper proposes a method to learn the intrinsic geometry and diffusion operators of underlying processes from distorted, indirect observations.

The paper introduces a physics-coupled graph neural operator to predict mid-plane stress tensor fields in concrete during projectile penetration.

This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception.

This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage.

This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers.

This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit.

This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects.

This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data.

This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts.

This paper presents an integer-only local learning system on edge FPGAs for energy-efficient convolutional neural network training.

This paper presents a masked diffusion model that synthesizes complete household travel survey diaries from tokenized sequences.

This paper provides a formal Lean proof establishing that guarded masked attention pooling is invariant to padding and row permutation.

This paper introduces a benchmark evaluating multi-agent organizational structures across diverse domains and collaborative interaction patterns.

This paper proposes a method that uses conformal score gradients to guide Markov chain Monte Carlo exploration for fast, calibrated Bayesian inference.

This paper develops and evaluates implicit and explicit reparameterized gradient estimators for Folded Normal and Rice distributions in variational inference.

This paper presents an end-to-end spiking neural network that decodes and reconstructs dynamic visual stimuli directly from biological retinal spike trains.

This paper develops a generalized McDiarmid concentration inequality capturing sample reuse and unbounded differences for stochastic optimization and generative modeling.

This paper introduces an uncertainty-aware property prediction workflow to guide out-of-distribution candidate selection for ionic liquid solvent discovery.

This paper presents a label-free decision head for frozen language models that preserves multiple-choice accuracy with reduced layers and memory.

This paper presents a cleaned and enriched residential floor plan dataset with graph, geometry, and image representations for machine learning.

This paper introduces a coordinate encoder trained on open map data to produce multi-scale location embeddings without requiring imagery.

This paper proposes density-matrix reinforcement learning, representing policies as density matrices to capture relationships and coherences between actions.

This paper introduces a reinforcement learning method that calibrates test pass-rate rewards to create adaptive training curricula for software engineering agents.

This paper introduces an implicit solvation potential providing solvent-dependent corrections to gas-phase machine learning interatomic potentials without retraining.

This paper presents a discrete flow-matching generator that evaluates molecular identity probabilities on demand to accelerate high-fidelity molecular generation.

This paper presents a distillation method using frozen language models to guide molecular flow generators toward natural language discovery goals.

The authors propose an action quantization method that preserves value functions by minimizing reachability error for search-based robot policy learning.

The authors compare federated learning algorithms for hospital mortality prediction and report that evaluation protocol choices outweigh aggregation algorithm differences.

The authors propose a score-based diffusion framework that controls generation speed across different graph frequency bands via colored noise priors.

The authors demonstrate that a single autoregressive neuron using chain-of-thought generation can simulate any feedforward circuit of the same activation type.

The authors provide a theoretical criterion to decide whether neural network layers should receive sign-based or spectral optimization updates during training.

The authors develop a geometric construction method to build two-hidden-layer neural networks that achieve exact classification on given training sets.

The authors propose a deep unrolling algorithm combining alternating projections with a learned denoiser for phase retrieval under changing measurement conditions.

The authors derive an online-to-offline reduction achieving near-optimal regret bounds for strongly monotone stochastic optimization under semi-bandit feedback.

The authors present an autotuning framework for WebGPU kernels to improve cross-platform inference speeds for language models on edge devices.

The authors present an ensemble variational data assimilation algorithm that incorporates learned generative forecasting models to account for complex model errors.

The authors propose a test-time learning method that trains a proposer to generate prompts, tools, and controls guiding scientific discovery agents.

The authors introduce a masked autoencoder that pretrains wearable sensor representations across varied recording durations and sampling rates using cross-resolution views.

The authors define measure neural mappings and establish quantitative error bounds for self-attention transformers acting on probability measures in unbounded spaces.

The authors present a mathematical method to enforce Dirichlet, Neumann, and Robin boundary conditions exactly on curved triangular domains for scientific machine learning.

The authors introduce an on-policy distillation method to transfer knowledge from autoregressive language models into diffusion language models using autoregressive rollouts.

The authors argue that cooperative multi-agent reinforcement learning systems require test-time coordination mechanisms rather than relying solely on training-time centralized information.

The authors develop a reachability-guided dynamic sparse training method that preserves functional pathways during extreme network pruning and regrowth.

The authors propose an optimizer for Monarch-factored weights that replaces dense matrix orthogonalization with smaller block updates to reduce memory and pretraining time.

The authors present a verification workflow combining reachability analysis with Bayesian optimization to close residual test bins in chip design verification.

The authors compare self-supervised and sleep-stage-supervised representations for identifying sleep-wake disorders across thousands of polysomnography recordings.

The authors analyze continual learning on a spherical parameter space to determine how many tasks a model can learn without catastrophic forgetting.

The authors introduce a selective hypothesis test for comparing cluster means under an unknown covariance matrix without inflating false-positive rates.