Post-ADC inference provides valid p-values and confidence intervals for data-dependent targets after active data collection by correcting adaptive sampling and selection biases without assuming the black-box function form.
A time-sensitive testing-by-betting framework favors early rejection via time-weighted rewards, yielding Bellman-optimal e-processes and an exponential-decay-optimal criterion recovering classical growth-rate optimality at large scales.
Hidden-target projection minimizes regret for online inventory optimization on general convex sets, improving dependence on common-demand probability to inverse square root with matching lower bound, plus polylogarithmic and adaptive dynamic guarantees.
A Sinkhorn-based dense associative memory for point-cloud measures uses spherical Hellinger-Kantorovich dynamics to retrieve patterns with exponential capacity and robust convergence.
Regularized Muon induces a Hamiltonian probability gradient flow with mirror-descent structure, yielding exponential convergence under gradient dominance and mean-field propagation of chaos.
Curvature is extended to arbitrary submodular functions, yielding greedy multiplicative approximation guarantees that apply even to negative-valued objectives.
A dual-anchor mechanism accelerates stochastic root-finding to O(ε⁻³) without variance reduction or regularization, reaching near-optimal O(ε⁻²) for strongly monotone cases.
Sobolev-regularized MMD gradient flow penalizes witness function gradients to ensure global convergence without isoperimetric assumptions, applying to both sampling and generative modeling.
Multi-variable conformal prediction extends calibration to vector-valued scores with multiple variables, removing data splitting while preserving coverage and yielding smaller, more stable prediction sets.
A difference-of-convex convex-concave procedure is lifted to Wasserstein space for non-convex measure optimization, yielding almost-stationary iterates and explicit decompositions for MMD and energy distance with faster convergence.
Using input-to-state stability, zeroth-order optimization achieves first-order convergence rates without extra dimension dependence when perturbations are small.
An adaptive algorithm achieves optimal O(1+V_T) local regret for online non-convex bilevel optimization with O(T log T) gradient evaluations, and a window-based method attains optimal Ω(T/W²) window-averaged regret via single-loop updates.
A learning-augmented algorithm for unrelated-machine makespan scheduling uses heavy-job predictions to achieve (1+ε)-approximation that smoothly degrades to 2-approximation as error grows.
Non-asymptotic analysis explains the curse of unrolling, early derivative divergence when differentiating through iterative algorithms, and shows that truncating early iterations mitigates it while reducing memory, with warm-starting providing implicit truncation in bilevel optimization.
FSGD is a streaming SGD method that uses latent factor representations for high-dimensional tasks, achieving scalable optimization with theoretical convergence guarantees including factor estimation error.
Heavy ball and ASGD face compute-efficiency versus serial-runtime tradeoffs in linear regression, with heavy ball extending SGD's efficient batch window by up to √κ and ASGD trading small-batch efficiency for runtime on fast-decaying spectra.