arXiv research

The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.

Cumulant expansion is used to derive accurate closed-form approximation for Monthly Sum Options in case of constant volatility model. Payoff of Monthly Sum Option is based on sum of $N$ caped (and probably floored) returns. It is noticed, that $1/\sqrt{N}$ can be used as a small parameter in Edgeworth expansion. First …

The paper calculates bounds for risk metrics and entropies under partial information constraints.

The paper analyzes worst-case distortion risk metrics and weighted entropy under partial information.

Paper tackles constrained bandit problems with a new learning framework.

We introduce a new type of graphical model called a "cumulative distribution network" (CDN), which expresses a joint cumulative distribution as a product of local functions. Each local function can be viewed as providing evidence about possible orderings, or rankings, of variables. Interestingly, we find that the condi…

This work extends reinforcement learning to handle non-cumulative objectives.

Paper defines the payback period for nonconventional cash flows using axioms.

A new GAN loss function based on cumulant generating functions improves stability and robustness.

In this paper, we shall prove that any Heegaard splitting of a $\partial$-reducible 3-manifold $M$, say $M=W\cup V$, can be obtained by doing connected sums, boundary connected sums and self-boundary connected sums from Heegaard splittings of $n$ manifolds $M_{1},..., M_{n}$ where $M_{i}$ is either a solid torus or a $…

New methods improve estimation accuracy in noisy settings.

Study cost-driven state representation learning for control from partial observations.

Let $M$ be a surface sum of 3-manifolds $M_1$ and $M_2$ along a bounded connected surface $F$ and $\partial_i$ be the component of $\partial M_i$ containing $F$. If $M_i$ has a high distance Heegaard splitting, then any minimal Heegaard splitting of $M$ is the amalgamation of those of $M^1, M^2$ and $M^*$, where $M^i=M…

In this paper we provide a valuation formula for different classes of actuarial and financial contracts which depend on a general loss process, by using the Malliavin calculus. In analogy with the celebrated Black-Scholes formula, we aim at expressing the expected cash flow in terms of a building block. The former is r…

Paper tackles online DR-submodular maximization with stochastic constraints.

The authors prove that the logarithmic Monge-Ampère flow with uniformly bound and convex initial data satisfies uniform decay estimates away from time $t=0$. Then applying the decay estimates, we conclude that every entire classical strictly convex solution of the equation {equation*} \det D^{2}u=\exp\{n(-u+1/2\sum_{i=…

Log-concavity proven for multinomial likelihoods under specific constraints.

Paper optimizes change detection in unnormalized distributions.

The paper develops algorithms for competitive RL in partially observable MGs.

It is shown that any smooth strictly convex global solution of $\det(\frac{\partial^{2}u}{\partial ξ_{i}\partial ξ_{j}}) = \exp \left\{-\sum_{i=1}^n d_i \frac{\partial u}{\partial ξ_{i}} - d_0\right\},$ where $d_0$, $d_1$,...,$d_n$ are constants, must be a quadratic polynomial. This extends a well-known theorem of Jö…

Boost-R uses gradient boosted trees for analyzing recurrence data.

The stochastic multi-armed bandit (MAB) problem is a common model for sequential decision problems. In the standard setup, a decision maker has to choose at every instant between several competing arms, each of them provides a scalar random variable, referred to as a "reward." Nearly all research on this topic consider…

Let $u$ be a positive solution of the ultraparabolic equation \begin{equation*} \partial_t u=\sum_{i=1}^n \partial_{x_i}^2 u+\sum_{i=1}^k x_i\partial_{x_{n+i}}u \hspace{8mm} \mbox{on} \hspace{4mm} \mathbb{R}^{n+k}\times (0,T), \end{equation*} where $1\leq k\leq n$ and $0<T \leq +\infty$. Assume that $u$ and its derivat…

Homotopy theory for $(2n+1)$-dimensional manifold triads with fixed boundary.

Exponential dispersion model is a useful framework in machine learning and statistics. Primarily, thanks to the additive structure of the model, it can be achieved without difficulty to estimate parameters including mean. However, tight conditions on cumulant function, such as analyticity, strict convexity, and steepne…

The paper studies hyperkähler structures and adapted complex structures using the Monge-Ampère equation.

New algorithms achieve near-optimal cumulative loss in nonparametric online learning and games.

Study of correlated Wigner matrices with BBP transitions.

GACBO optimizes unknown causal graphs with interventions.

Paper generalizes reward distribution in multi-armed bandits with temporally-partitioned rewards.

Direct formula found for ADO invariants from homological representations.

The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bia…

New algorithm minimizes cumulative loss in dynamic linear bandits without prior knowledge of comparator switches.

In this paper, we present NESTA, a specialized Neural engine that significantly accelerates the computation of convolution layers in a deep convolutional neural network, while reducing the computational energy. NESTA reformats Convolutions into $3 \times 3$ batches and uses a hierarchy of Hamming Weight Compressors to …

Study optimal policy regret in partially observable Markov games with adaptive opponents.

Study of constant curvature hypersurfaces in hyperbolic space.

We consider the problem of online multiclass classification with partial feedback, where an algorithm predicts a class for a new instance in each round and only receives its correctness. Although several methods have been developed for this problem, recent challenging real-world applications require further performance…

The paper prices energy spread options using a complex stochastic model.

A method to monitor probability predictions for calibration loss in image classification models.

Kjolstad et. al. proposed a tensor algebra compiler. It takes expressions that define a tensor element-wise, such as $f_{ij}(a,b,c,d) = \exp\left[-\sum_{k=0}^4 \left((a_{ik}+b_{jk})^2\, c_{ii} + d_{i+k}^3 \right) \right]$, and generates the corresponding compute kernel code. For machine learning, especially deep learni…

New monitoring method detects ML risk models' performance changes in medical interventions.

Algorithm learns from changing zero-sum games with no regret.

A new trading model uses deep reinforcement learning to optimize portfolio weights.

Partial-monitoring games constitute a mathematical framework for sequential decision making problems with imperfect feedback: The learner repeatedly chooses an action, opponent responds with an outcome, and then the learner suffers a loss and receives a feedback signal, both of which are fixed functions of the action a…

Study on parity of singular set components of maps to surfaces.

The paper proves local laws for non-separable sample covariance matrices.

For a non-uniform lattice in SL(2,R), we consider excursions in cusp neighborhoods of a random geodesic on the corresponding finite area hyperbolic surface or orbifold. We prove a strong law for a certain partial sum involving these excursions. This generalizes a theorem of Diamond and Vaaler for continued fractions. I…

Boosts change-point detection power with optimal sub-sampling.