New method calculates super-hedging prices with transaction costs.
problem Super-hedging European contingent claims under proportional transaction costs.
method Explicit recursive scheme based on convex duality and Legendre-Fenchel transform.
result Computes super-hedging price and optimal strategy without martingale arguments.
New dual formulation reduces generalization error for ERM-fDR.
problem Generalization error in constrained optimization problems.
method Introduces a dual formulation of ERM-fDR using Legendre-Fenchel transform and implicit function theorem.
result Explicit characterizations of generalization error for algorithms under mild conditions.
Dual optimization connects ERM-fDR to normalization function.
problem Empirical risk minimization with f-divergence regularization.
method Dual formulation, Legendre-Fenchel transform, implicit function theorem, nonlinear ODE.
result Computational method to calculate normalization function efficiently.
We make non-convex optimization problems convex.
problem Learning from constrained labels is non-convex.
method Characterize and compute tightest convex extensions of non-convex objectives.
result We can compute efficient convex approximations of non-convex objectives.
Theory unifies various reinforcement learning methods with a generalized regularized approach.
problem Improving reinforcement learning algorithms with regularization.
method Develops a theory of regularized Markov Decision Processes, extending previous approaches.
result Unified analysis of various reinforcement learning algorithms.
New analysis reveals multi-branched multifractality in time series.
problem Analyzing non-monotonic behavior in mean inter-event times.
method Modified Multifractal Detrended Fluctuation Analysis with Legendre-Fenchel transform.
result Discovery of multi-branched multifractality leading to phase transitions.
Researchers develop a new framework to control neural network sensitivity.
problem Understanding and controlling the behavior of neural networks.
method Direct parameterization of bi-Lipschitzness in convex neural networks.
result A clear and tight control of neural network sensitivity achieved.
Dual-based algorithms optimize distributed convex problems over networks.
problem Optimizing distributed convex problems over network constraints.
method Dual formulation of primal problem, distributed algorithms achieving optimal rates.
result Achieves optimal rates similar to centralized algorithms with additional cost related to network spectral properties.
The Heston model is a popular stock price model with stochastic volatility that has found numerous applications in practice. In the present paper, we study the Riemannian distance function associated with the Heston model and obtain explicit formulas for this function using geometrical and analytical methods. Geometric…
New knots share same Upsilon invariant despite different Alexander polynomials.
problem Identifying concordant knots via Upsilon invariant.
method Examined hyperbolic L-space knots and their Upsilon invariants.
result Infinitely many pairs of hyperbolic L-space knots with distinct Alexander polynomials share the same Upsilon invariant.
The paper tackles efficient computation of optimal transport by approximating conjugates with amortized optimization.
problem Efficient computation of convex conjugates in optimal transport is challenging and limits the quality of transport maps.
method The approach combines amortized approximations of conjugates with a fine-tuning solver to improve transport map quality.
result The method significantly improves the quality of transport maps for the Wasserstein-2 benchmark and models many 2D couplings and flows.
A certain spectrum, indexed by a\in[0,\infty], of upper bounds P_a(X;x) on the tail probability P(X\geq x), with P_0(X;x)=P(X\geq x) and P_\infty(X;x) being the best possible exponential upper bound on P(X\geq x), is shown to be stable and monotonic in a, x, and X, where x is a real number and X is a random variable. T…