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.
A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in …
In the present paper, we studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type p…
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…
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hiera…
New RL algorithm reduces policy switching cost to loglog(T) with similar regret.
New method tracks significant arm switches to improve bandit algorithms.
This paper tackles near-optimal adversarial RL with switching costs, providing algorithms and matching lower bounds.
Improved ASR for English-isiZulu code-switched speech with semi-supervised training.
New algorithm for continuous-time switching systems using variational inference.
Bayesian method clusters time series with varying dynamics.
Optimal liquidation of an asset with unknown constant drift and stochastic regime-switching volatility is studied. The uncertainty about the drift is represented by an arbitrary probability distribution; the stochastic volatility is modelled by -state Markov chain. Using filtering theory, an equivalent reformulation…
New algorithm minimizes cumulative loss in dynamic linear bandits without prior knowledge of comparator switches.
Researchers develop a method to control nonlinear systems with Koopman operator regression.
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…
New algorithm reduces switching costs in multinomial logit bandit problems.
New polynomial invariants derived from birack and switch structures.
Bayesian method adapts to unknown distribution shifts in online learning.
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 …
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.
Squirrel switches between optimizers for better performance.
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.
Animals (especially humans) have an amazing ability to learn new tasks quickly, and switch between them flexibly. How brains support this ability is largely unknown, both neuroscientifically and algorithmically. One reasonable supposition is that modules drawing on an underlying general-purpose sensory representation a…
New algorithms for multitask learning with long-term memory.
Paper presents an efficient algorithm for linear MDP with low switching cost.
Study minimax off-policy evaluation in multi-armed bandits with known and unknown behavior policies.
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.
As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the s…
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…
It has been widely assumed that a neural network cannot be recovered from its outputs, as the network depends on its parameters in a highly nonlinear way. Here, we prove that in fact it is often possible to identify the architecture, weights, and biases of an unknown deep ReLU network by observing only its output. Ever…
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. …
Audit fees change based on company and economic factors during auditor switching.
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…
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…