New method improves Pham's algorithm for joint diagonalization.
problem Optimizing joint diagonalization of matrices for statistical learning.
method Quasi-Newton method for Pham's diagonalization criterion.
result Proposed method outperforms Pham's algorithm in experiments.
Constructs families of monotone Lagrangians in Brieskorn-Pham hypersurfaces.
problem Constructing compact monotone Lagrangians in Brieskorn-Pham hypersurfaces.
method Inspired by monodromy considerations, techniques for controlling homology, Maslov class, and monotonicity constant.
result Infinite families of monotone Lagrangian S1imesΣg in C3 for g≥2. Compressed sensing (CS) is an important theory for sub-Nyquist sampling and recovery of compressible data. Recently, it has been extended by Pham and Venkatesh to cope with the case where corruption to the CS data is modeled as impulsive noise. The new formulation, termed as robust CS, combines robust statistics and CS…
Deep signature algorithm for pricing path-dependent options.
problem Pricing path-dependent options with complex payoff functions.
method Extended backward scheme for state-dependent FBSDEs with reflections, incorporating signature layer for path-dependent FBSDEs.
result Convergence analysis of the algorithm with explicit dependence on truncation order and neural network approximation errors.
New examples show Sasaki manifolds without extremal metrics.
problem Finding Sasaki manifolds without extremal metrics.
method Using Berglund-Hübsch transpose rule and relative K-stability.
result Examples of Sasaki manifolds without extremal metrics.
We prove the existence of an abundance of new Einstein metrics on odd dimensional spheres including exotic spheres, many of them depending on continuous parameters. The number of families as well as the number of parameter grows double exponentially with the dimension. Our method of proof uses Brieskorn-Pham singularit…
The results on the mean-variance hedging problem in Gouriéroux, Laurent and Pham (1998), Rheinländer and Schweizer (1997) and Arai (2005) are extended to discontinuous semimartingale models. When the numéraire method is used, we only assume the Radon-Nikodym derivative of the variance-optimal signed martingale measure …
Automorphism group of nonorientable surface curve graph matches surface homeomorphisms.
problem Identifying automorphisms of nonorientable surface curve graphs.
method Using Bowden, Hensel, and Webb's fine curve graph and Long, Margalit, Pham, Verberne, and Yao's proof as a foundation.
result Automorphism group of nonorientable surface curve graph is isomorphic to the surface's homeomorphism group.
Automorphisms of fine graphs for surfaces and tori are studied.
problem Understanding automorphisms of fine graphs for surfaces and tori.
method Extending previous results to tori and discussing smooth versions.
result Automorphism groups of fine graphs for surfaces and tori are naturally isomorphic to homeomorphism groups.
Using S1-equivariant symplectic homology, in particular its mean Euler characteristic, of the natural filling of links of Brieskorn-Pham polynomials, we prove the existence of infinitely many inequivalent contact structures on various manifolds, including in dimension 5 the k-fold connected sums of S2×S3 a…
We consider spaces of plane curves in the setting of algebraic geometry and of singularity theory. On one hand there are the complete linear systems, on the other we consider unfolding spaces of bivariate polynomials of Brieskorn-Pham type. For suitable open subspaces we can define the bifurcation braid monodromy takin…
The paper studies Riemannian metrics on tangent Lie groups using two left-invariant metrics.
problem Exploring Riemannian structures on tangent Lie groups.
method Defining a new left-invariant Riemannian metric on the tangent Lie group using two left-invariant metrics and symplectic forms.
result Explicit formulas for the Levi-Civita connection, tensor curvature, and sectional curvature of the new metric in terms of the original metrics.
New probabilistic scheme combines deep learning with Runge-Kutta methods for solving PDEs.
problem Solving high-dimensional semi-linear parabolic PDEs efficiently.
method Probabilistic scheme using deep learning and Runge-Kutta methods.
result Crank-Nicolson schemes are efficient in terms of precision, computational cost, and numerical implementation.
We prove a long-standing conjecture about complex reflection arrangements.
problem The K(π,1) conjecture for affine Artin groups. method Recent advancements in dual Coxeter and Artin groups theory, new constructions, and poset shellability.
result The complexified complement of an affine reflection arrangement is a classifying space.
Generative model for time series using Schrödinger bridges with jumps.
problem Creating realistic synthetic time series from observed data.
method Entropic optimal transport, Schrödinger bridge framework, jump-diffusion process.
result Jump-diffusion Schrödinger bridge model generates more realistic time series.
We study the connected components of the space of higher spin bundles on hyperbolic Klein surfaces. A Klein surface is a generalisation of a Riemann surface to the case of non-orientable surfaces or surfaces with boundary. The category of Klein surfaces is isomorphic to the category of real algebraic curves. An m-spin …
Solves optimal stopping problem with Poisson constraints using jumps.
problem Optimal stopping with Poisson constraints and jumps.
method Penalized backward stochastic differential equation (PBSDE) with jumps, decomposition method based on Jacod-Pham, comparison theorem of BSDEs with jumps.
result Solves American option pricing in nonlinear markets with Poisson constraints.
Study on homology groups of cDV singularity links, identifying their topology.
problem Identify the topology of links of cDV singularities of types cAn and cDn. method Analyzing the second integral homology group of the links, using results from Smale and Thom-Sebastiani sums.
result The homology groups of the links are determined for cDV singularities of types cAn and cDn. TensorSketch is an oblivious linear sketch introduced in Pagh'13 and later used in Pham, Pagh'13 in the context of SVMs for polynomial kernels. It was shown in Avron, Nguyen, Woodruff'14 that TensorSketch provides a subspace embedding, and therefore can be used for canonical correlation analysis, low rank approximation…
Unified approach learns Ising models from various dynamics and data types.
problem Efficiently learning Ising model parameters from data under diverse conditions.
method Simple logistic regression approach, generalizing existing algorithms.
result Logistic regression succeeds in multiple new settings where assumptions are violated.
Study Cremona transformations in weighted projective planes to find rational cuspidal curves and Zariski pairs.
problem Finding rational cuspidal curves and Zariski pairs in weighted projective planes.
method Construct families of curves using Cremona transformations, compute fundamental groups, and use blow-up-down decompositions.
result Discover new examples of rational cuspidal curves and Zariski pairs in weighted projective planes.
Proposes a robust equilibrium strategy for mean-variance portfolio selection.
problem Time-inconsistency in mean-variance portfolio selection.
method Introduces a novel definition of robust equilibrium strategy and solves the corresponding PDE system.
result A classical solution to the PDE system implies a robust equilibrium strategy.
The article constructs a forward utility for markets with multiple default risks.
problem Characterizing forward performance processes in a market with multiple default risks.
method Using Jacod-Pham decomposition and recursive BSDEs, the article constructs a forward utility and proves its existence and uniqueness.
result The article identifies the risk-sensitive long-run growth rate of the optimal wealth process in a stochastic factor model with ergodic dynamics.
We propose a hedging approach for general contingent claims when liquidity is a concern and trading is subject to transaction cost. Multiple assets with different liquidity levels are available for hedging. Our risk criterion targets a tradeoff between minimizing the risk against fluctuations in the stock price and inc…
We study the curvature of metric spaces and branched covers of Riemannian manifolds, with applications in topology and algebraic geometry. Here curvature bounds are expressed in terms of the CAT(k) inequality. We prove a general CAT(k) extension theorem, giving sufficient conditions on and near the boundary of a locall…
Flat learning curves reveal no progress in ENAS controller.
problem Improving learning speed in neural architecture search.
method Evaluated learning progress of ENAS controller through architecture re-training.
result No observable progress in controller's generated architectures.
Characterizes local tropicalizations of splice type surface singularities.
problem Understanding splice type surface singularities from a tropical geometry perspective.
method Characterization of local tropicalizations as cones over splice diagrams, using tropical methods.
result Characterizes local tropicalizations of splice type surface singularities as cones over associated splice diagrams.
Extends utility maximization theory for infinite horizons without strong no-arbitrage assumptions.
problem Maximizing lifetime utility from wealth over an infinite horizon.
method Develops a duality theory using deflators and supermartingale properties, extending previous work.
result Establishes a strong duality theorem for infinite horizon utility maximization under minimal no-arbitrage assumptions.
This work studies fluctuation in multilayer neural networks using mean field theory.
problem Understanding fluctuation in multilayer neural networks with mean field training.
method Developed a second-order mean field limit to capture fluctuation, demonstrating stability of gradient descent training.
result Gradient descent training in multilayer networks biases towards minimal fluctuation, even after convergence.
Optimally sketches tensors with minimal rows for preserving norms.
problem Efficiently preserving tensor norms in high dimensions.
method Constructs a matrix M to preserve tensor norms with minimal rows.
result Optimal tensor sketch with minimal rows and improved application time.
Examines algorithmic modeling across three cultures.
problem Tackles algorithmic modeling in different cultural contexts.
method Uses parametric regressions, interpretable algorithms, and complex algorithms.
result Extension of Leo Breiman's thesis to include cultural differences.
Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that UCB, M3, S or Exp3 algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
problem Selecting the best algorithm selector for a specific problem instance.
method Apply algorithm selection to the selection of other algorithms (meta-algorithm selection).
result Meta-algorithm selection can be beneficial in some cases but faces challenges in solving the meta-level problem.
Proposes CLRS benchmark to evaluate algorithmic reasoning.
problem Difficulty in transferring results across publications due to targeted algorithmic data.
method Develops a comprehensive benchmark covering various algorithmic tasks.
result Demonstrates performance of algorithmic reasoning baselines on the CLRS benchmark.
Combines multiple bandit algorithms to create a nearly optimal single algorithm.
problem Designing a single bandit algorithm that performs nearly as well as the best individual algorithm in a stochastic environment.
method Develops two general corralling algorithms that achieve favorable regret guarantees.
result The regret of the corralling algorithms is no worse than the best individual algorithm's performance.
We propose accelerated randomized coordinate descent algorithms for stochastic optimization and online learning. Our algorithms have significantly less per-iteration complexity than the known accelerated gradient algorithms. The proposed algorithms for online learning have better regret performance than the known rando…
The exchange algorithm is studied for its convergence and asymptotic variance.
problem Theoretical limitations of the exchange algorithm in sampling from doubly-intractable distributions.
method Theoretical analysis of the exchange algorithm's convergence speed and asymptotic variance.
result The exchange algorithm converges at a geometric rate and satisfies a Central Limit Theorem.
New algorithms optimize algorithm parameters in online settings with reduced computational costs.
problem Optimizing algorithm parameters in online settings with volatile and discontinuous losses.
method Developed semi-bandit optimization algorithms that leverage extra information to reduce computational costs.
result Achieved regret bounds as good as full-information feedback with significantly less computational effort.
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…
No algorithm outperforms uniform sampling in A/B testing.
problem Identifying the best arm in A/B testing with fixed budget.
method Introducing consistent and stable algorithms, deriving lower bounds, and proving optimality of uniform sampling.
result No algorithm performs better than uniform sampling in A/B testing.
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.
Combines online learning algorithms to achieve better performance.
problem Improving online learning algorithms with varying guarantees.
method Adding iterates of two parameter-free algorithms to create a new algorithm with improved regret.
result Generates efficient algorithms that adapt to multiple norms and maintain dimension-free guarantees.
New ELM algorithms reduce computation time and complexity.
problem Efficient computation of extreme learning machine (ELM) algorithms.
method Developed inverse-free ELM algorithms using recursive matrix inverse and inverse LDL' factorization.
result Proposed algorithms significantly reduce computational complexity.
This review article surveys data augmentation MCMC algorithms.
problem Sampling from intractable probability distributions.
method Comprehensive study of DA MCMC algorithms, their convergence properties, and acceleration strategies.
result Synthesizes recent developments and provides insights for researchers.
Bayesian learning rule unifies and generalizes various machine learning algorithms.
problem Machine learning algorithms are diverse and not always understood.
method Bayesian principles and natural gradients are used to derive algorithms.
result Derives a wide range of algorithms including classical and modern ones.
Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach th…
This review summarizes five Lasso optimization algorithms.
problem Optimizing the Lasso objective function.
method Five representative algorithms: ISTA, FISTA, CGDA, SLA, PFA.
result Comparison of convergence rates and strengths/weaknesses.
Neural networks mimic algorithms to solve complex problems.
problem Current machine learning methods struggle with generalisation and efficiency.
method Representing algorithms in a continuous space and adapting them to real-world problems.
result Neural networks can execute classical algorithms more efficiently.