Showing Optimization Show all papers
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026
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Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026
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Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Spectral optimizer Muon exceeds SGD associative memory capacity, matching Newton's method with first-order updates and larger critical batch sizes.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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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.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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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.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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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.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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GFlowNet forward-policy training uses trajectory Fisher-Rao geometry to derive structure-aware natural gradients and decomposable second-moment updates.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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A Sinkhorn-based dense associative memory for point-cloud measures uses spherical Hellinger-Kantorovich dynamics to retrieve patterns with exponential capacity and robust convergence.
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Regularized Muon induces a Hamiltonian probability gradient flow with mirror-descent structure, yielding exponential convergence under gradient dominance and mean-field propagation of chaos.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Curvature is extended to arbitrary submodular functions, yielding greedy multiplicative approximation guarantees that apply even to negative-valued objectives.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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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.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sobolev-regularized MMD gradient flow penalizes witness function gradients to ensure global convergence without isoperimetric assumptions, applying to both sampling and generative modeling.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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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.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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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.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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Using input-to-state stability, zeroth-order optimization achieves first-order convergence rates without extra dimension dependence when perturbations are small.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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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.
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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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.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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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.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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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.
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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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.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Proximal preconditioned stochastic gradient methods extend Muon/Scion to nonconvex constrained optimization with heavy-tailed noise convergence and faster variance-reduced variants.
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Mirror descent-ascent achieves O(N^{-1/2}) and O(N^{-2/3}) convergence to mean-field Nash equilibria via infinite-dimensional dual Bregman analysis.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Random search for stochastic optimization works under weaker smoothness assumptions and achieves faster convergence via variance-reduced variants using translation invariance to balance noise.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Refined analysis and a new LIPPAX algorithm improve federated stochastic variational inequality convergence rates and reduce client drift.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Threshold-based algorithms achieve constant class envy-freeness and exceed 1/2 utilitarian welfare in online class matching, with near-matching upper bounds characterizing fairness costs.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Intrinsic Muon extends norm-constrained matrix optimization to Riemannian manifolds via canonical intrinsic norms, yielding closed-form updates and convergence rates independent of factor conditioning.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Functional gradient descent with adaptive representations converges to stationary points or global minimizers despite approximation errors and outperforms fixed approximations and neural network baselines.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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A variational-inequality framework estimates generalized linear models via equilibrium conditions, yielding finite-sample bounds, asymptotic normality, and improved stability over maximum likelihood for non-canonical links.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Jacobian descent resolves multi-objective gradient conflicts via projection to improve convergence and enable instance-wise risk minimization.
Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026 · ▲ 1 on Hugging Face
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The paper studies an interested seller algorithmically selling information to budget-constrained buyers to maximize revenue and induce desirable actions, proving optimal menu protocols are polynomial-time computable and analyzing single-policy restrictions.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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SDBPG adaptively perturbs dual formulations to stabilize multipliers near lower-level stationary points, yielding first explicit sample-complexity guarantees for stochastic nonconvex simple bilevel optimization.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Under local PŁ conditions, unique optimistic lower-level selection ensures hyper-gradient differentiability via pseudoinverses, yielding HG-MS with manifold-dependent convergence and strong LLM reweighting results.
Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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CV-ZOD adaptively integrates directional hints into zeroth-order optimization, achieving rates that interpolate between first- and zeroth-order convergence based on hint quality without prior knowledge.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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For any k>2, (k+1)/(k+2)-EFkX allocations always exist and are computable in polynomial time, yielding 3/4-EF2X for any number of agents and 2/3-EF X for eight agents.
Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Penalty-based first-order methods solve bilevel minimax optimization without lower-level strong convexity, achieving O(ε^-4) deterministic and O(ε^-9) stochastic complexity.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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A dynamic k-center model with known lifetimes achieves deterministic (2+ε)-approximation with amortized updates and linear memory, plus a (6+ε)-approximation with worst-case updates and sublinear memory.
Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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A polynomial-time algorithm computes optimal Thiele committees for voter-interval preferences via a concavity theorem and Lagrangian relaxation.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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ADUCA is a parameter-free cyclic algorithm for Minty variational inequalities that uses delayed operator updates to avoid line searches and achieves near-optimal global oracle complexity.
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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No-regret online algorithms unconditionally converge to mixed performatively stable equilibria via randomized martingale arguments, avoiding distribution-response assumptions and PPAD hardness.
Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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A meta-learning framework with randomized lazy FTRL and movement-aware mixing achieves near-optimal dynamic regret with indicator switching costs and adapts to both switch counts and path length without prior knowledge.
Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026
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Globalized semismooth Newton with lazy Hessian updates achieves global and superlinear convergence for nonsmooth optimization without per-step second-order evaluations, yielding substantial speedups.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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Spectral optimizer analysis reveals three phases where Muon's SignSVD preconditions covariance differently than SignSGD.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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A GDRO algorithm via flexible sample queries and a prediction-with-limited-advice game achieves high-probability error O(√(∑ m/r_j)/t) with consistent sample complexity O(m log m/ε²).
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Dynamic population sizes provably accelerate NSGA-II and GSEMO on the CLIMB problem, yielding O(n log n) versus Ω(n^1.5) evaluations and a super-constant speedup.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Augmenting base methods with one random direction step achieves almost-sure d-stationarity in nonsmooth nonconvex optimization without affecting convergence rates.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Simple KNN-based outlier removal achieves constant-factor robust k-means reductions with matching or better real-world clustering performance and speed.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Edge of stability selectively redistributes learning across data groups via Hessian-aligned gradients and non-vanishing magnitudes, favoring output outliers over saturated ones.
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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A projected-gradient algorithm for Gromov-Wasserstein transport uses verifiable inexact projections to guarantee convergence to stationary points with scalable reliability.
Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Frank-Wolfe achieves Ω(T^{-p/(p-1)}) lower bounds on p-uniformly convex sets for p ≥ 3 under exact line search or short steps, matching upper bounds via low-dimensional dynamics.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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Overparameterized Gaussian mixtures have a loss manifold of slow growth where Polyak steps achieve geometric loss reduction, and alternating short gradient steps with long Polyak steps yields local linear convergence to near-optimal solutions.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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For discrete-time finite-horizon mean-field games with state-independent transitions and weakly monotone rewards, anchored proximal gradient descent computes mean-field equilibria via monotone inclusions over occupation measures at an O(1/√T) rate without regularization or uniqueness.
Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Epistemic pairwise maximin share (EPMMS) relaxes PMMS fairness; 4/5-EPMMS allocations exist for additive valuations, exact EPMMS for bivalued valuations, and existence holds for three additive or two-type agents despite MMS nonexistence.
Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026
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Quantum estimators for heavy-tailed noise enable QNSGD and QPSGD to find ε-stationary or optimal solutions with poly(√d, ε) oracle queries, beating classical lower bounds in low dimensions.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Super-level-set regression directly optimizes minimum-volume prediction regions via geometric optimization, bypassing full conditional density estimation to capture complex multimodal conditional structures.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Compressed distributed online convex optimization achieves optimal regret via error feedback and online compression, with applications to distributed non-smooth optimization.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Machine-learned predictions augment exact exponential subset-selection algorithms, reducing search space and runtime smoothly with prediction quality under weak independence or unknown-accuracy settings.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Riemannian optimization on the Segre manifold recovers noisy low-CP-rank tensors via RGD and RGN, which achieve linear and quadratic-to-linear convergence under mild noise.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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PZOS combines exact leader gradients with zeroth-order follower estimates for MPECs, yielding lower variance and faster convergence than black-box methods on routing and security games.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Adam converges with high probability on generalized-smooth objectives under only second-moment stochastic gradients, matching a sharp δ^{-1/2} confidence dependence and yielding expectation rates for p<1.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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A primal mirror descent algorithm computes exact Wasserstein barycenters for discrete and continuous measures in Fisher-Rao geometry with convergence guarantees.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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A curvature-adaptive FTPL algorithm tunes its perturbation online to achieve O(sqrt(T)) regret for non-convex Lipschitz losses and O(log T) under linear curvature growth, with matching lower bounds.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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An incremental multiple-oracle framework computes approximate continuous-action game equilibria with constant memory via fixed-cardinality strategy sets without exact global best responses.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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This paper proposes a linear programming-based minimum s-t cut algorithm with an optimal Lipschitz constant, yielding the first dynamic algorithm with non-trivial recourse and improved b-matching stability.
Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Online resource allocation with budget and general constraints achieves near-optimal dynamic regret and bounded violations via weakly adaptive Lagrangian analysis.
Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Thiele rules are polynomial-time computable on voter-interval and linearly consistent domains via integral linear programming, and linearly consistent domains strictly contain voter-candidate interval domains, though tree-based extensions are NP-hard.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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A forward-only gradient approximation for bilinear forms of Hermitian matrix functions reuses the Lanczos pass with minimal overhead, offering provable error bounds and unconditional stability without reorthogonalization.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Natural heuristics for the envy-cycle elimination algorithm improve worst-case welfare loss by jointly selecting goods and agents to maximize utility.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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This paper bounds the query complexity of multi-round local search on general graphs, proving deterministic upper and randomized lower bounds that extend grid results to arbitrary connected graphs.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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A local dual sharpness condition enables Frank-Wolfe to converge faster than 1/t on uniformly convex sets, bypassing standard lower bounds.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Muon achieves optimal heavy-tailed sample complexity with dimension-independent convergence for nuclear-norm stationarity, unlike Euclidean methods.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Adaptive Polyak step sizes for Schedule-Free SGD and Adam compute iteration-wise learning rates from losses and gradients, achieving anytime convergence without tuning base rates or horizons.
Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Learning-augmented online scheduling achieves O(1)-competitive latency with O(1) preemptions per job on parallel machines, with overhead scaling logarithmically in prediction error.
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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<|message_model|><|content_text|>A Sample-Sketch-Solve paradigm estimates squared Wasserstein distance between smooth distributions within ε error in near-optimal time. For α-Hölder smooth distributions on (0,1)^d it achieves ε^{-max(2,(d+1+o(1))/(1+α))} runtime, with optimal Θ(ε^{-2}) in 2D for α>1
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Geometric characterization and fast-rate learning algorithm exactly compress linear programs into lower-dimensional equivalents preserving optimality with 1/n generalization.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Multi-block DC programming defines a broader structured nonconvex class with polynomial decompositions and constructive formulations for deep networks, plus convergent batch and stochastic algorithms.
Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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A reduction framework converts online convex optimization with delayed feedback into immediate feedback, improving delay-dependent regret bounds for both first-order and bandit settings via continuous-time decomposition.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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A re-solving algorithm achieves O(log² n) regret in nonstationary online linear programming using just one sample per distribution via dynamic programming and dual methods.
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Schedule-Free gradient and stochastic gradient descent achieve optimal nonconvex convergence rates and escape strict saddles with small perturbations via Lyapunov analysis.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Multiplicative tâtonnement uses local per-chore excess-demand updates to converge to competitive equilibrium in CCH disutility chores markets. It achieves O(1/ε²) approximate equilibrium rates with better constants and runs substantially faster in practice.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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A normalized variance-reduced decentralized bilevel algorithm handles heavy-tailed noise without clipping and achieves convergence guarantees for nonconvex problems.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Gradient descent for logistic regression with Gaussian design achieves O(√(‖θ*‖₂⁵d/n)) ℓ₂ error with linear convergence, and a sharper Θ(√(‖θ*‖₂d/n)) rate is tight in high dimensions.
Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Classical momentum acceleration improves proportionally with mini-batch size for quadratic interpolation optimization, enabling perfect parallelization of stochastic gradient computations.
Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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