arXiv research

Optimizes search times by resetting agents when a threshold is reached.

Optimal threshold resetting reduces search time for multiple diffusive searchers.

New method converts conventional ANNs to SNNs with minimal loss and efficiency.

Deep learning models price convertible bonds with complex reset and call features.

We study how resetting affects geometric Brownian motion, showing it becomes stationary but remains non-ergodic.

The paper solves a pricing problem for a multiple reset put option using integral equations.

Ridge regression linked to Poisson resetting in statistical physics.

This paper presents numerical algorithm and results for pricing a capital protection option offered by many asset managers for investment portfolios to take advantage of market growth and protect savings. Under optimal withdrawal policyholder behaviour the pricing of such a product is an optimal stochastic control prob…

CSER improves SGD efficiency by resetting errors and partial synchronization.

Resetting from checkpoints improves DNN training with noisy labels.

The paper analyzes XVA reduction strategies in financial crises using Mandatory Breaks, Restructuring, and Resets.

Exact results on power-law distributions in systems with resets.

This paper tackles belief-state selection in simulators with latent states.

Study of a generalized geometric Brownian motion with varying entry and exit rates.

New framework finds periodic policies in reset-free MDPs with sublinear regret.

New method extends supervised learning for non-stationary control problems.

Study shows MSE with sigmoid can match SCE in classification tasks, especially with noisy data.

Study agnostic RL in large state spaces with weak function approximation.

ET-GP-UCB optimizes time-varying functions without knowing change rates.

A variable annuity is an equity-linked financial product typically offered by insurance companies. The policyholder makes an upfront payment to the insurance company and, in return, the insurer is required to make a series of payments starting at an agreed upon date. For a higher premium, many insurance companies offer…

UDRL fails to converge in stochastic environments with episodic resets.

New RL approach uses resets to learn complex MDPs efficiently.

We provide an analytically treatable model that describes in a unified manner income distribution for all income categories. The approach is based on a master equation with growth and reset terms. The model assumptions on the growth and reset rates are tested on an exhaustive database with incomes on individual level s…

Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time. The switching mechanism between regimes is controlled by unobserved random variables that form a first-order Markov chain. Ex…

Deep RL algorithms can overfit to early experiences, leading to poor performance.

GRUwE improves irregular time series prediction with simpler, efficient RNN-based approach.

Tax dynamics affects wealth distribution in a linearly growing socio-economic model.

We consider the sequential Bayesian optimization problem with bandit feedback, adopting a formulation that allows for the reward function to vary with time. We model the reward function using a Gaussian process whose evolution obeys a simple Markov model. We introduce two natural extensions of the classical Gaussian pr…

This paper explores how environmental properties can simplify reinforcement learning in non-episodic settings.

The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while…

This is a facsimile of the circa 1990 unpublished manuscript with the same title. All the original text, figures and tables are included; although text has been reset in \TeX, the original hand-drawn figures have been redrawn digitally, and the parameter $k$ in the original table of lens spaces has been replaced with t…

New method designs fairer transport plans with uncertainty.

EXODUS improves training of SNNs by stabilizing gradients and reducing complexity.

Selective reinitialization improves adaptability of neural bandits in dynamic environments.

ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.

From the Hamilton-Jacobi-Bellman equation for the value function we derive a non-linear partial differential equation for the optimal portfolio strategy (the dynamic control). The equation is general in the sense that it does not depend on the terminal utility and provides additional analytical insight for some optimal…

In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task. We develop a simple technique using emergency stops (e-stops) to exploit this phenomenon. Using e-stops significantly improves sample complexity by reducing the amount of required explorati…

In this paper, we develop a Markovian model that deals with the volume offered at the best quote of an electronic order book. The volume of the first limit is a stochastic process whose paths are periodically interrupted and reset to a new value, either by a new limit order submitted inside the spread or by a market or…

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…

Variable annuities (VA) are popular insurance products. VAs provides the insured with a guaranteed accumulation rate on their premium at maturity. In addition, the insured may receive extra benefit if returns of underlying funds are high enough. Here we consider a special case of VA with high-water mark feature and Gua…

Recurrent neural networks (RNNs) are powerful dynamical models for data with complex temporal structure. However, training RNNs has traditionally proved challenging due to exploding or vanishing of gradients. RNN models such as LSTMs and GRUs (and their variants) significantly mitigate these issues associated with trai…

Developed a new thresholding method that connects soft and hard thresholding.

Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constraints to exploration typically requires a lot of prior knowledge and domain expertise. We present a safety measure which implicitly captures…

New trading strategy beats traditional grid in crypto markets.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

A new SSL method uses instance-dependent thresholds to improve accuracy.

Study stability thresholds of big line bundles, proving bounds and generalizing results.

Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.