arXiv research

Paper proves exponential lower bounds for policy iteration in MDPs.

The paper evaluates index-based allocation policies using data from randomized control trials.

An optimal algorithm for multi-armed bandits with constraints.

A new tail-shape index based on Value at Risk and Expected Shortfall.

New algorithm reduces switching costs in multinomial logit bandit problems.

New polynomial invariants derived from birack and switch structures.

In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …

We consider $K$-armed stochastic bandits and consider cumulative regret bounds up to time $T$. We are interested in strategies achieving simultaneously a distribution-free regret bound of optimal order $\sqrt{KT}$ and a distribution-dependent regret that is asymptotically optimal, that is, matching the $κ\ln T$ lower b…

This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.

The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched …

New algorithms improve sampling from complex distributions.

We consider the restless Markov bandit problem, in which the state of each arm evolves according to a Markov process independently of the learner's actions. We suggest an algorithm that after $T$ steps achieves $\tilde{O}(\sqrt{T})$ regret with respect to the best policy that knows the distributions of all arms. No ass…

Squirrel switches between optimizers for better performance.

Paper develops a two-population model to assess longevity basis risk.

Study approximates financial market with discrete-time models.

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines …

Optimizes control of hybrid systems with multiple switching processes.

Study tackles balancing policy switching costs in offline RL.

New algorithm learns switching dynamics from multiple neural signals.

Paper tackles utility maximization with job-switching and retirement constraints.

New RL algorithm reduces policy switching cost to loglog(T) with similar regret.

The paper explores dynamic regret with switching cost in online decision making.

Indices of acceptability are well suited to frame the axiomatic features of many performance measures, associated to terminal random cash flows.We extend this notion to classes of càdlàg processes modelling cash flows over a fixed investment horizon.We provide a representation result for bounded paths. We suggest an ac…

Paper presents an efficient algorithm for linear MDP with low switching cost.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

Study strategic competition in commodity markets using impulse-switching controls.

One type of switch simplifies operations on lattice knots.

Optimal switching regret for all segmentations in online convex optimisation.

This paper tackles near-optimal adversarial RL with switching costs, providing algorithms and matching lower bounds.

New algorithm uses Whittle index to improve Q-learning for restless bandits.

This paper addresses parameter estimation for wave equations with Markovian switching.

Algorithm for bandits with switching costs achieves optimal regret bounds.

In this paper, we derive the family switching formula of -n two-sphere fiber bundle embedded in a smooth four-manifold fiber bundle. In the smooth category, it is a partial generalization of Fintushel-Stern's argument for four-manifolds. We also derive an algebraic analogue of the family switching formula, allowing the…

Audit fees change based on company and economic factors during auditor switching.

Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. …

We study the problem of switching-constrained online convex optimization (OCO), where the player has a limited number of opportunities to change her action. While the discrete analog of this online learning task has been studied extensively, previous work in the continuous setting has neither established the minimax ra…

New invariant for virtual links using multi-switches and algebraic systems.

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

New algorithm reduces switching costs in RL beyond linear MDPs.

Develops identifiability theory for multi-lag regime-switching models.

We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous works studied the expected regret of the le…

Paper derives analytical formulas for NLD-CEV moments with regime switching.

OMGD algorithm optimizes online convex optimization with switching costs and delayed gradients.

Pricing financial or real options with arbitrary payoffs in regime-switching models is an important problem in finance. Mathematically, it is to solve, under certain standard assumptions, a general form of optimal stopping problems in regime-switching models. In this article, we reduce an optimal stopping problem with …

Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…

New algorithm reduces RL complexity with low switching costs.

In this paper, we consider the problem of pricing discretely-sampled variance swaps based on a hybrid model of stochastic volatility and stochastic interest rate with regime-switching. Our modelling framework extends the Heston stochastic volatility model by including the CIR stochastic interest rate and model paramete…