2316 papers
This paper rigorously proves that incorporating local updates into the distributed DIGing algorithm can provably accelerate convergence for a broad class of objective functions, demonstrating that two local updates are sufficient to achieve maximal improvement without requiring reduced step sizes.
This paper introduces Bayesian Generative Modeling (BGM), a unified framework that leverages a stochastic iterative Bayesian updating algorithm to learn a single generative model capable of performing arbitrary conditional inference with principled uncertainty quantification, without requiring retraining for different conditioning structures.
This paper presents an automated system using LLM-powered web research agents to generate and resolve diverse, real-world forecasting questions at scale, demonstrating high-quality question creation and resolution rates that surpass human-curated platforms while effectively evaluating and improving AI forecasting performance.
The paper introduces "Infusion," a framework that leverages scalable influence-function approximations to compute subtle perturbations in training data, demonstrating that modifying as little as 0.2% of a dataset can effectively and transferably shape model behavior across vision and language domains.
This paper proposes a robust, data-driven framework for assortment optimization that maximizes worst-case expected revenue under potential customer preference shifts, establishing computational tractability and deriving statistically optimal algorithms with tight sample complexity bounds to ensure reliable generalization.
This paper proposes a novel bottleneck transformer architecture that integrates convolutional blocks for frame-level feature extraction and multi-head self-attention for information aggregation to achieve improved non-intrusive prediction of the Short-Time Objective Intelligibility (STOI) metric, outperforming state-of-the-art self-supervised learning models in both seen and unseen scenarios.
The paper proposes B-DENSE, a novel distillation framework that leverages multi-branch trajectory alignment to enforce dense intermediate supervision, thereby overcoming the structural information loss and discretization errors of existing methods to achieve superior image generation quality with reduced inference latency.
The paper introduces Missing-by-Design (MBD), a unified framework for revocable multimodal sentiment analysis that combines structured representation learning with a certifiable parameter-modification pipeline to enable the machine-verifiable deletion of specific data modalities while maintaining predictive performance and privacy compliance.
This paper proposes a curriculum-based continual learning framework that decomposes complex robust control problems with multiple uncertainties into sequential tasks, combining a model-based controller with deep reinforcement learning to achieve efficient, non-forgetting policy updates and successful sim-to-real transfer for automotive powertrain vibration control.
The paper introduces Coupled Discrete Diffusion (CoDD), a hybrid framework that overcomes the "factorization barrier" in diffusion language models by replacing fully factorized outputs with a lightweight probabilistic inference layer, thereby enabling efficient parallel generation of coherent, high-quality text without the prohibitive costs of full joint modeling or reinforcement learning.
This paper introduces SpeedTransformer, a novel Transformer-based model that utilizes only speed inputs from dense smartphone GPS trajectories to achieve superior accuracy and transferability in transportation mode detection compared to traditional deep learning approaches.
This paper establishes that history-dependent policies with sublinear expected regret are robust-optimal for non-rectangular average-reward robust MDPs without requiring rectangularity, and introduces a transient-value framework with an epoch-based policy that achieves constant-order finite-time performance by combining worst-case optimality with online learning.
The paper introduces DUEL, a framework that enables exact likelihood computation for masked diffusion models under the test-time distribution, revealing that their true performance significantly surpasses previous estimates and establishing a new standard for comparing and optimizing parallel text generation.
This paper introduces \textsc{Gome}, a gradient-based MLE agent that outperforms traditional tree search methods on MLE-Bench by mapping diagnostic reasoning to gradient computation, demonstrating that as LLM reasoning capabilities improve, gradient-based optimization becomes increasingly superior to exhaustive enumeration.
The paper introduces FinTexTS, a large-scale financial text-paired time-series dataset constructed via a novel semantic-based and multi-level pairing framework that overcomes the limitations of simple keyword matching by leveraging LLMs to align news articles with stock prices across macro, sector, related company, and target-company levels, thereby significantly improving stock price forecasting performance.
This paper introduces two software-only techniques, Overflow-Aware Scaling (OAS) and Macro Block Scaling (MBS), that significantly reduce the accuracy gap between the hardware-efficient MXFP4 format and NVIDIA's NVFP4 standard in Large Language Models, achieving near-parity performance with minimal computational overhead.
This paper proposes the Online-Within-Online Fair Multi-Task Learning (OWO-FMTL) framework, which leverages a dual-loop mechanism with primal-dual updates to ensure long-term equitable inference performance for heterogeneous users in AI-RANs while maintaining low computational overhead.
KernelCraft introduces the first benchmark evaluating agentic LLM systems that use feedback-driven workflows to automatically generate and optimize low-level kernels for emerging hardware with novel ISAs, demonstrating their ability to produce valid, high-performance code that rivals or exceeds traditional compiler baselines.
This paper presents ALADIN, an accuracy-latency-aware framework that enables the pre-deployment evaluation of mixed-precision quantized neural networks on scratchpad-based embedded AI accelerators by transforming models into platform-aware representations to analyze trade-offs and bottlenecks without requiring physical hardware.
This paper reviews the landscape of ultra-low-power edge and in-sensor AI processors and empirically benchmarks a segmentation model on GAP9, STM32N6, and Sony IMX500 platforms to demonstrate that while in-sensor processing offers superior energy-delay performance, different architectures provide distinct trade-offs between latency, energy efficiency, and power budgets.