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.
The aims of this study are twofold. First, we consider an optimal risk allocation problem with non-convex preferences. By establishing an infimal representation for distortion risk measures, we give some necessary and sufficient conditions for the existence of optimal and asymptotic optimal allocations. We will show th…
Defines certainty equivalent and utility indifference pricing for incomplete preferences.
problem Incomplete preferences represented by multiple priors and utility functions.
method Defines certainty equivalent and utility buy/sell prices as set-valued functions of claims, proves monotonicity and convexity properties, approximates bounds via convex vector optimization.
result Certainty equivalent and indifference price bounds can be computed or approximated by convex vector optimization.
The study provides foundations for naive diversification, a preference for equal treatment of alternatives.
problem Understanding and mathematically grounding naive diversification preferences.
method Axiomatization of naive diversification as a preference for equality over inequality, and derivation of its relationship to classical diversification.
result Naive diversification is a preference for equality over inequality, and it is characterized by convex and permutation invariant preferences.
The paper analyzes financial market equilibrium with heterogeneous risk preferences and convex constraints.
problem Characterizing equilibrium in a market with heterogeneous risk preferences and convex constraints.
method Continuous-time financial market model with heterogeneous agents and convex portfolio constraints.
result Margin constraints increase market price of risk and decrease interest rates, leading to higher equity risk premium and pro-cyclical leverage cycles.
In an incomplete semimartingale model of a financial market, we consider several risk-averse financial agents who negotiate the price of a bundle of contingent claims. Assuming that the agents' risk preferences are modelled by convex capital requirements, we define and analyze their demand functions and propose a notio…
Study on identifying most preferred policy in bandits with vector-valued rewards.
problem Identifying the most preferred policy in bandits with vector-valued rewards.
method Derive a novel lower bound on sample complexity, design the Preference-based Track and Stop (PreTS) algorithm, and derive a new concentration inequality.
result The sample complexity of PreTS is asymptotically tight.
Paper addresses online alignment of large language models under uncertain preference feedback.
problem Online alignment of large language models with misspecified preference feedback.
method Formulates an oracle-robust objective as a worst-case optimization problem for log-linear policies, and develops projected stochastic composite updates.
result Shows that the robust objective admits an exact closed-form decomposition and achieves O(ε−2) oracle complexity.
Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the worker-abilities by optimizing an objective function, for instance, by maximizing the data likelihood based on an assumed underlying model. A variety of methods have been proposed in the literature to address this inference …
In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by at…
Consider an agent taking two successive decisions to maximize his expected utility under uncertainty. After his first decision, a signal is revealed that provides information about the state of nature. The observation of the signal allows the decision-maker to revise his prior and the second decision is taken according…
This work proposes a method to learn nonlinear feature relations using non-convex regularized binned regression.
problem Learning feature nonlinearities in large scale complex problems.
method Binning feature values, finding the best fit in each quantile using non-convex regularized linear regression, enforcing smoothness via piecewise-constant/linear approximation, and selecting a sparse subset of features.
result The proposed algorithm achieves linear rate of convergence while requiring near-minimal number of samples, accurately learning feature nonlinearities.