A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New bounds on minimax regret for sequential probability assignment using logarithmic loss.
Near-logarithmic regret per switch achieved for mixable/exp-concave losses.
New loss function helps learn unstable dynamical systems.
Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.
We develop a new theoretical framework, the \emph{envelope complexity}, to analyze the minimax regret with logarithmic loss functions and derive a Bayesian predictor that adaptively achieves the minimax regret over high-dimensional -balls within a factor of two. The prior is newly derived for achieving the mini…
The study examines correlations of logarithms of integers at different scalings.
Paper generalizes VB-FTRL for online learning of quantum states with logarithmic loss.
This work broadens calibeating to various proper losses using Bregman divergence.
This work generalizes calibeating for a broader range of proper losses using Bregman divergence.
We introduce a temperature into the exponential function and replace the softmax output layer of neural nets by a high temperature generalization. Similarly, the logarithm in the log loss we use for training is replaced by a low temperature logarithm. By tuning the two temperatures we create loss functions that are non…
We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the opti…
New bounds for online portfolio selection without smoothness assumptions.
We analyze the problem of sequential probability assignment for binary outcomes with side information and logarithmic loss, where regret---or, redundancy---is measured with respect to a (possibly infinite) class of experts. We provide upper and lower bounds for minimax regret in terms of sequential complexities of the …
Algorithm learns expert weights to minimize regret in adversarial setting.
Research examines correlations of complex logarithms of lattice points, showing level repulsion and Poissonian behavior.
Efficient algorithm for contextual bandits with first-order guarantees.
New algorithm reduces online logistic regression regret without exponential constant.
Study minimax regret in sequential probability assignment with and without side information.
New algorithm exploits curvature of feasible sets for fast online convex optimization.
New algorithm for online portfolio selection with reduced runtime.
Paper approximates Kelly betting for wealth growth.
The overarching goal of this paper is to derive excess risk bounds for learning from exp-concave loss functions in passive and sequential learning settings. Exp-concave loss functions encompass several fundamental problems in machine learning such as squared loss in linear regression, logistic loss in classification, a…
We derive PAC-Bayesian learning guarantees for heavy-tailed losses, and obtain a novel optimal Gibbs posterior which enjoys finite-sample excess risk bounds at logarithmic confidence. Our core technique itself makes use of PAC-Bayesian inequalities in order to derive a robust risk estimator, which by design is easy to …
The question addressed in this paper is the performance of the optimal strategy, and the impact of partial information. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. We focus on the optimal strategy with a logarithmic utility functi…
Unified analysis of online optimization with self-concordant barriers, improving regret bounds.
The paper tackles attributing forecast gaps in complex model suites.
We examine gradient descent on unregularized logistic regression problems, with homogeneous linear predictors on linearly separable datasets. We show the predictor converges to the direction of the max-margin (hard margin SVM) solution. The result also generalizes to other monotone decreasing loss functions with an inf…
The paper improves sparse Gaussian processes by optimizing predictive loss.
A new subdivision scheme for Heisenberg group values with central smoothness loss.
The paper proves LOO CV is reliable under estimator stability.
Algorithm achieves optimal regret for unknown Lipschitz convex losses.
We study differentially private (DP) algorithms for stochastic convex optimization (SCO). In this problem the goal is to approximately minimize the population loss given i.i.d. samples from a distribution over convex and Lipschitz loss functions. A long line of existing work on private convex optimization focuses on th…
Optimizes privacy-preserving optimization for heavy-tailed data.
Optimizes private learning with differential privacy for LASSO problems.
Gaptron algorithm reduces mistakes in online multiclass classification.
We demonstrate that, in the classical non-stochastic regret minimization problem with decisions, gains and losses to be respectively maximized or minimized are fundamentally different. Indeed, by considering the additional sparsity assumption (at each stage, at most decisions incur a nonzero outcome), we derive…
Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance. The empirical success has led to great interests in theoretical understanding of this heuristic. In this paper, we approach this problem in a way very different from exis…
A spring-block chain placed on a running conveyor belt is considered for modeling stylized facts observed in the dynamics of stock indexes. Individual stocks are modeled by the blocks, while the stock-stock correlations are introduced via simple elastic forces acting in the springs. The dragging effect of the moving be…
It is now known that an extended Gaussian process model equipped with rescaling can adapt to different smoothness levels of a function valued parameter in many nonparametric Bayesian analyses, offering a posterior convergence rate that is optimal (up to logarithmic factors) for the smoothness class the true function be…
The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.
In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification framework with logarithmic time and space, and on a general approach for error correcting output coding (ECOC) with loss-based decoding, and intr…
New algorithm reduces prediction errors across various loss functions.
Algorithm achieves logarithmic regret with sublinear hints.
We introduce a class of utility-based market makers that always accept orders at their risk-neutral prices. We derive necessary and sufficient conditions for such market makers to have bounded loss. We prove that hyperbolic absolute risk aversion utility market makers are equivalent to weighted pseudospherical scoring …
Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss functions such as hinge loss, squared hinge loss, logistic loss, and exponential loss are introduced. T…
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming …
Polyak step size GD reaches final radius of convergence after log iterations.
Optimal algorithms for mixable losses in dynamic environments with reduced redundancy.