A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
2019.Introduction to Online Convex Optimization
12 Pith papers cite this work. Polarity classification is still indexing.
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Revenue of any ε-approximate correlated equilibrium in discrete first-price auctions is at least v₂ - Θ(1/k) - Θ(ε k²).
DECO-EF achieves the first expected comparator-adaptive sublinear network-regret bounds for parameter-free decentralized online learning under compressed communication.
SGR-GMM introduces spectral gradient reweighting via an entropy-regularized spectral game to create a robust GMM estimator, with proven convergence and finite-sample error bounds under contamination.
A nested-projection gradient algorithm attains O(log T) regret with O(log T) cumulative constraint violation for strongly convex losses, and O(√T) for both with convex losses; the body's proof is coherent, though the abstract claims lower-bound results the body never contains.
An algorithm for online resource allocation with budget and general constraints achieves O(sqrt(T)) regret in stochastic and alpha-regret in adversarial regimes with bounded constraint violations.
A modular reduction from budget-constrained contextual bandits with adversarial contexts to unconstrained bandits via surrogate rewards, yielding improved guarantees and an efficient algorithm based on SquareCB.
StoSignSGD resolves SignSGD divergence on non-smooth objectives via structural stochasticity, matching optimal convex rates and improving non-convex bounds while delivering 1.44-2.14x speedups in FP8 LLM pretraining.
A latent-cluster quasi-Bayesian method with restarted updates yields sublinear cumulative Wasserstein-1 regret for online distributional prediction under drift and adversarial corruption.
CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.
SSEV reaches 85.5-86.4% execution accuracy on Spider benchmarks and 66.3% on BIRD-Dev through self-refinement and voting; ReCAPAgent-SQL achieves 31% on initial Spider 2.0-Lite queries via agent collaboration.
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Online Resource Allocation With General Constraints
An algorithm for online resource allocation with budget and general constraints achieves O(sqrt(T)) regret in stochastic and alpha-regret in adversarial regimes with bounded constraint violations.