arXiv research

Characterizes preferences for decision-making under uncertainty using a leader-follower game model.

We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.

Investors with asymmetric information play a game to optimize their portfolios.

New RL algorithms learn QSE from strategic feedbacks with sample efficiency.

We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a regular gradient step, and a corrective direction dictated by the currently best-performing worker (leader). Our method differs from the param…

The online problem of computing the top eigenvector is fundamental to machine learning. In both adversarial and stochastic settings, previous results (such as matrix multiplicative weight update, follow the regularized leader, follow the compressed leader, block power method) either achieve optimal regret but run slow,…

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)…

Study of $2 imes 2$ zero-sum games with noisy observations and commitments.

Transformers cluster meaningless words around leaders for sentiment analysis.

Neural operators approximate Stackelberg game solutions.

Game theory approach to predicting and responding to interventions based on causal relationships.

Mutually interacting components form complex systems and the outputs of these components are usually long-range cross-correlated. Using wavelet leaders, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call joint multifractal analysis based on wavel…

SLHF uses sequential game theory to optimize preferences from human feedback.

CB-RL solves complex decision-making problems with contextual information and exogenous events.

FTPL with Fréchet perturbation achieves near optimal regret bounds for m-set semi-bandit problems.

We study the problem of online learning with non-convex losses, where the learner has access to an offline optimization oracle. We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of $O(T^{-1/2})$ in this setting. This improves upon the previous best-known regret rate of…

Adaptive learning rates improve FTPL's BOBW guarantees in bandit problems.

New mechanism designs regulate herding in financial markets.

Improved theoretical guarantees for Top Two algorithms.

Diestel-Leader graphs are neither hyperbolic nor CAT(0), so their visual boundaries may be pathological. Indeed, we show that for $d>2$, $\partial\text{DL}_d(q)$ carries the indiscrete topology. On the other hand, $\partial\text{DL}_2(q)$, while not Hausdorff, is $T_1$, totally disconnected, and compact. Since $\text{D…

Paper optimizes FTPL for adversarial and stochastic bandits with specific tail distributions.

Paper finds political networks reduce bond issuance costs in China.

We analyze linear McKean-Vlasov forward-backward SDEs arising in leader-follower games with mean-field type control and terminal state constraints on the state process. We establish an existence and uniqueness of solutions result for such systems in time-weighted spaces as well as a {convergence} result of the solution…

Advances FTPL results for bandit problems with unbounded perturbations.

FTPL policy achieves best-of-both-worlds regret in decoupled bandits with reduced computational cost.

Given a bordified space, Karlsson defines an incidence geometry of stars at infinity. These stars and their incidence are closely related to well-understood objects when the space is hyperbolic, CAT(0), or a bounded convex domain with the Hilbert metric. A question stemming from Karlsson's original paper was whether or…

Paper analyzes FTPL's effectiveness in combinatorial semi-bandit problems.

Paper studies zero-sum games with noisy observations and identifies equilibrium conditions.

New algorithm optimizes multi-armed bandits with low computational cost.

We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex systems by identifying three of them on real-life financial markets. We extract and interpret the empirical, numerical, and semi-analytical evidences for the existence of these phase transitions, by c…

Proposes a new training algorithm for zero-sum games to avoid convergence issues.

Symbolic Data Analysis is based on special descriptions of data - symbolic objects (SO). Such descriptions preserve more detailed information about units and their clusters than the usual representations with mean values. A special kind of symbolic object is a representation with frequency or probability distributions …

Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in prac…

Network analysis reveals changing cryptocurrency market leaders.

This paper improves FTPL algorithm for semi-bandit problems with best-of-both-worlds guarantees.

New aggregation strategy handles unbounded losses with regret bounds.

The paper explores how regularization can lead to convergence in imperfect information games.

New algorithms improve dueling bandit performance in multiplayer settings.

Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities and ability to scale with the size of the data. In this work, we provide three contributions. First, we show t…

AMM finds optimal contract for LPs to maximize order flow.

Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed deep learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, w…

We fully describe the horofunction boundary $\partial_h L_2$ with the word metric associated with the generating set $\{t,at\}$ (i.e the metric arising in the Diestel-Leader graph $\text{DL}(2,2)$). The visual boundary $\partial_\infty L_2$ with this metric is a subset of $\partial_h L_2$. Although $\partial_\infty L_2…

FTPL method shows near-optimal regret bounds for AMDPs with bandit feedback.

New algorithm reduces sample complexity for Top Two method.

Optimal bounds on regret and constraint violation in adversarial COCO.

Improved FTPL algorithm reduces regret in predictable minimax games.

Mutation improves FTRL convergence in zero-sum games.

Study shows GDP and CPI predict CCC funding, highlighting need for economic forecasting.