Search papers, labs, and topics across Lattice.
25 papers published across 1 lab.
Gradient clipping, often seen as a heuristic, unlocks optimal convergence and linear agent speed-up for decentralized SGD under heavy-tailed noise, outperforming normalization in this regime.
Geometric weighting by update norm offers a subtle but measurable improvement in personalized federated learning, outperforming uniform aggregation and FedPer-R1 on CIFAR-10's native client accuracy.
Average-reward RL for general multichain MDPs is now possible without model knowledge or reduction to discounted problems.
LLMs develop inherent fault tolerance as they scale, learning to compute within error-correcting codes that remain efficient even for massive models.
Trillion-parameter agentic RL refits are now 12-40x faster, shrinking an 87.5-minute full checkpoint transfer to 150 seconds by sending only bit-exact weight deltas.
Gradient clipping, often seen as a heuristic, unlocks optimal convergence and linear agent speed-up for decentralized SGD under heavy-tailed noise, outperforming normalization in this regime.
Geometric weighting by update norm offers a subtle but measurable improvement in personalized federated learning, outperforming uniform aggregation and FedPer-R1 on CIFAR-10's native client accuracy.
Average-reward RL for general multichain MDPs is now possible without model knowledge or reduction to discounted problems.
LLMs develop inherent fault tolerance as they scale, learning to compute within error-correcting codes that remain efficient even for massive models.
Trillion-parameter agentic RL refits are now 12-40x faster, shrinking an 87.5-minute full checkpoint transfer to 150 seconds by sending only bit-exact weight deltas.
Parallel LLM agents can finally achieve near-linear speedups by strategically parallelizing only when token-based cost estimates predict a net gain, eliminating redundant work and reconciliation overhead.
Privacy-preserving federated learning achieves segmentation performance on skin lesions comparable to centralized methods, without centralizing sensitive patient data.
Collaborative edge-cloud LLM inference can achieve 275% higher throughput by dynamically synchronizing heterogeneous requests to batch their common cloud-side suffix.
Decentralized learning topologies can be optimized using only local neighborhood and friend-of-a-friend information, achieving competitive performance without global graph awareness.
Distributed MMD with Random Fourier Features achieves near-perfect correlation with expected drift magnitude on massive e-commerce data, while per-dimension KS tests falter.
Private prediction can achieve logarithmic dependence on the time horizon, while private learning requires polynomial dependence, revealing a fundamental gap in their sample complexity for differentially private settings.
Hierarchical MoE routing with a novel arrival-weighted mean estimation prevents expert collapse and enables localized co-activation, outperforming flat routing on large embedding models.
Physics-based secure key distribution can now sustainably support federated learning for medical imaging, reducing key material needs by 35x without sacrificing accuracy.
Asynchronous LLM training can be made robust to stale rollouts by minimizing a novel group-mass capping bias bound, outperforming existing methods on reasoning tasks.
A single controller can now robustly learn stochastic transport across a range of initial distribution shifts, outperforming standard SB and noise augmentation on challenging tasks.
LLM training CI is now 77.5% faster and 63.9% more GPU-efficient by intelligently pruning and prioritizing tests based on runtime evidence.
Semantic communication via VLM orchestrators unlocks robust, long-horizon coordination for multi-robot systems, outperforming prior single-robot approaches on complex manipulation tasks.
Quasi-Newton methods now conquer deep learning's non-convexity and scale, outperforming Adam and others on challenging tasks like 136M-parameter physics-informed models.
An RL agent learns to optimize SpTRSV parallelism by intelligently rewriting sparse matrix dependency graphs, achieving substantial performance gains with minimal structural changes.
A single auxiliary variable per group of failures unlocks near-optimal $T^{-4/5}$ convergence for large-scale Cox regression, outperforming stochastic baselines.
Lossy compression's inherent distortion patterns, not just update geometry, can reliably detect and mitigate model poisoning in federated learning.
Decentralized power allocation in complex MANETs is solved with a learned GNN that matches centralized optimization performance using only local, noisy channel data.
Consumer GPUs can now achieve up to 1.66x faster MoE inference by bypassing NCCL's CPU bottlenecks with a novel DMA-driven communication design.
LLM inference auctions unlock economic efficiency and user utility by enabling dynamic bidding for compute priority, outperforming static pricing tiers.
Parallel search agents can now achieve higher accuracy than generative aggregators by leveraging TRACE to intelligently select the best trajectory from aggregated cross-rollout evidence.
