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arXiv research

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.

168,657 papers · 148 categories

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16314762 · Jun 202019922001200920172026
48 results for Parallel LN

This paper studies effective parallelization of MCTS for computer games.

problem Computational expense of MCTS due to many rollouts.
method Examined parallel MCTS performance, identified necessary conditions for desirable parallelization, and proposed BU-UCT algorithm.
result Theoretical foundation for effective parallel MCTS, with BU-UCT outperforming baselines.

The paper proves a regret bound for a sub-Gaussian mixture on unbounded data.

problem Tackles the challenge of achieving regret bounds for sub-Gaussian mixtures on unbounded data.
method Uses path-wise (deterministic) regret bounds and a cumulative variance process to derive the bound.
result Shows that on a specific event, the regret is eventually bounded by ln(ln V_T).

The paper optimizes distribution estimation with high probability in Kullback-Leibler divergence.

problem Estimating discrete distributions with high probability in Kullback-Leibler divergence.
method Uses online learning techniques for novel estimator construction via online-to-batch conversion.
result Optimal rate of estimation is pinned down up to a doubly logarithmic factor of K.

Looped transformers with LN converge to power method for principal component prediction.

problem Understanding how transformers learn algorithmic procedures.
method Study of principal component prediction with looped linear transformers and layer normalization.
result Gradient descent trains looped transformers with LN to implement the power method for principal component prediction.

New betting strategy reduces regret to ln(ln n) with protection against adversarial data.

problem Tackles the problem of minimizing regret in betting against adversarial and stochastic data.
method Combines insights from Robbins and Cover, using a mixture strategy.
result Exhibits a regret of O(ln(ln n)) on almost all paths, with O(log n) regret on the complement.

In this paper, we prove that there are no proper CRSCRS bi-warped product submanifolds other than contact CR-biwarped products in Sasakian manifolds. On the other hand, we prove that if MM is a CRSCRS bi-warped product of the form M=NT×f1Nn1×f2Nθn2M=N_T \times_{f_1}N^{n_{1}}_\perp\times_{f_2} N^{n_{2}}_θ in a cosymplectic manifold $\w…

2018-11-07abs ↗pdf ↗

Gradient descent, when applied to the task of logistic regression, outputs iterates which are biased to follow a unique ray defined by the data. The direction of this ray is the maximum margin predictor of a maximal linearly separable subset of the data; the gradient descent iterates converge to this ray in direction a…

2018-03-20abs ↗pdf ↗

We consider the problem of estimating from sample paths the absolute spectral gap γγ_* of a reversible, irreducible and aperiodic Markov chain (Xt)tN(X_t)_{t \in \mathbb{N}} over a finite state space ΩΩ. We propose the UCPI{\tt UCPI} (Upper Confidence Power Iteration) algorithm for this problem, a low-complexity algorithm …

2018-06-15abs ↗pdf ↗

We study metric and analytic properties of generalized lemniscates E_t(f)={z:ln|f(z)|=t}, where f is an analytic function. Our main result states that the length function |E_t(f)| is a bilateral Laplace transform of a certain positive measure. In particular, the function ln|E_t(f)| is convex on any interval free of cri…

2003-06-23abs ↗pdf ↗

We introduce post-Lie algebra structures on pairs of Lie algebras $(\Lg,\Ln)$ defined on a fixed vector space VV. Special cases are LR-structures and pre-Lie algebra structures on Lie algebras. We show that post-Lie algebra structures naturally arise in the study of NIL-affine actions on nilpotent Lie groups. We obtai…

2011-09-01abs ↗pdf ↗

Let GnG_n be the genus of a two-dimensional surface obtained by gluing, uniformly at random, the sides of an nn-gon. Recently Linial and Nowik proved, via an enumerational formula due to Harer and Zagier, that the expected value of GnG_n is asymptotic to (nlnn)/2(n - \ln n)/2 for nn\to\infty. We prove a local limit theorem…

2011-08-25abs ↗pdf ↗

This paper tackles resource allocation in the Lightning Network using DRL.

problem Complex combinatorial problem of node selection and resource allocation in the Lightning Network.
method Attention-based Deep Reinforcement Learning framework.
result Improved resource allocation leads to better performance and decentralization in the LN.

We consider combinatorial semi-bandits over a set of arms X{0,1}d{\cal X} \subset \{0,1\}^d where rewards are uncorrelated across items. For this problem, the algorithm ESCB yields the smallest known regret bound R(T)=O(d(lnm)2(lnT)Δmin)R(T) = {\cal O}\Big( {d (\ln m)^2 (\ln T) \over Δ_{\min} }\Big), but it has computational complexity ${\cal O}…

2020-02-17abs ↗pdf ↗

We give a fast oblivious L2-embedding of ARnxdA\in \mathbb{R}^{n x d} to BRrxdB\in \mathbb{R}^{r x d} satisfying (1ε)Ax22Bx22<=(1+ε)Ax22.(1-\varepsilon)\|A x\|_2^2 \le \|B x\|_2^2 <= (1+\varepsilon) \|Ax\|_2^2. Our embedding dimension rr equals dd, a constant independent of the distortion ε\varepsilon. We use as a black-box any L2-embedding $Π…

2019-09-27abs ↗pdf ↗

Step decay schedules improve convergence in non-convex optimization.

problem Improving convergence in non-convex optimization problems.
method Analyzing convergence rates of step decay schedules in non-convex, convex, and strongly convex problems.
result Step decay schedules achieve O(lnT/T)\mathcal{O}(\ln T/\sqrt{T}) convergence rates in various optimization scenarios.

New bounds on learning from multiple distributions for VC classes.

problem Understanding the sample complexity of learning from multiple data distributions.
method Analyzing the gap between known upper and lower bounds for PAC-learnable classes.
result Recent progress on sample complexity for VC dimension d classes on k distributions.

Improved regret bounds for bandits with expert advice.

problem Optimizing decision-making in environments with expert advice.
method Proved lower and upper bounds for regret in restricted and standard feedback models.
result Proved a new upper bound of order KTln(N/K)\sqrt{K T \ln(N/K)} for the worst-case regret, matching a previously known lower bound.

Algorithm identifies best arm in bandit game with variance consideration.

problem Identifying the best arm in a stochastic multi-armed bandit game with varying variances.
method Adaptive algorithm using grouped median elimination to explore gaps and variances.
result Guarantees to output the best arm with probability (1-δ) using optimal number of samples.

We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have kk players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously. The quality of the col…

2018-05-23abs ↗pdf ↗

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyper-parameter tunings. In this paper, we fir…

2020-02-12abs ↗pdf ↗

New binary classification techniques help multiclass classification by aggregating proper learners.

problem Multiclass classification faces a properness barrier that prevents optimal learning by proper learners.
method Aggregations of proper binary learners, generalized to multiclass settings, achieve optimal sample complexity.
result Optimal binary learners can achieve sample complexity $O\left(\frac{d_G + \ln(1 / δ)}ε ight)$ for classes with finite Graph dimension dGd_G.

Layer normalization improves federated learning with skewed labels.

problem Label skewness in federated learning datasets.
method Identified feature normalization as key mechanism; applied to latent features before classifier.
result Normalization accelerates global training and improves convergence under extreme label shift.

Logarithmic regret achieved in continuous-time linear-quadratic reinforcement learning.

problem Optimizing control actions in unknown continuous-time systems over a finite time horizon.
method Least-squares algorithm based on continuous-time observations and controls, with perturbation analysis and parameter estimation error analysis.
result Logarithmic regret bound of order O((lnM)(lnlnM))O((\ln M)(\ln\ln M)).

We consider Markov Decision Processes (MDPs) where the rewards are unknown and may change in an adversarial manner. We provide an algorithm that achieves state-of-the-art regret bound of O(τ(lnS+lnA)Tln(T))O( \sqrt{τ(\ln|S|+\ln|A|)T}\ln(T)), where SS is the state space, AA is the action space, ττ is the mixing time of the MDP, and $…

2019-05-25abs ↗pdf ↗

This is the third in a series of papers attempting to describe a uniform geometric framework in which many integrable systems can be placed. A soliton hierarchy can be constructed from a splitting of an infinite dimensional group LL as positive and negative subgroups L_+, L_- and a commuting sequence in the Lie algebr…

2014-06-19abs ↗pdf ↗

Introducing a way to modify knots using nn-trivial rational tangles, we show that knots with given values of Vassiliev invariants of bounded degree can have arbitrary unknotting number (extending a recent result of Ohyama, Taniyama and Yamada). The same result is shown for 4-genera and finite reductions of the homolog…

1999-09-09abs ↗pdf ↗

Sharp gradient estimates for a weighted p-Laplacian equation on metric measure spaces.

problem Analyzing solutions to a specific weighted p-Laplacian equation.
method Applying Nash-Moser iteration to obtain sharp gradient estimates.
result Established Liouville theorems for the equation.

The paper tackles risk-averse multi-armed bandit with linear payoffs.

problem Risk-averse contextual multi-armed bandit problem with linear payoffs.
method Apply Thompson Sampling algorithm for disjoint model and provide comprehensive regret analysis.
result Proved an O((1+ρ+1ρ)dlnTlnKδdKT1+2εlnKδ1ε)O((1+ρ+\frac{1}ρ) d\ln T \ln \frac{K}δ\sqrt{d K T^{1+2ε} \ln \frac{K}δ \frac{1}ε}) regret bound for mean-variance criterion.

Improved sequential tests detect anomalies faster in multi-stream auditing.

problem Efficiently auditing machine learning systems across multiple data streams.
method Developed new sequential tests using merging martingales and averaging/products rules.
result Balanced tests achieve optimal stopping times in sparse and dense alternatives.

We study bi-warped product submanifolds of nearly Kaehler manifolds which are the natural extension of warped products. We prove that every bi-warped product submanifold of the form M=MT×f1 ⁣M×f2 ⁣MθM=M_T\times_{f_1}\! M_\perp\times_{f_2}\! M_θ in a nearly Kaehler manifold satisfies the following sharp inequality: $$\|h\|^2\geq 2p\|\…

2019-02-17abs ↗pdf ↗

Let f=(f1,,fm):RnRmf = (f_1,\ldots,f_m) : \R^n \longrightarrow \R^m be a polynomial map; Gf(r)={xRn:fi(x)r, i=1,,m}G^f(r) = \{x\in\R^n : |f_i(x)| \leq r,\ i =1,\ldots, m\}. We show that if ff satisfies the Mikhailov - Gindikin condition then \begin{itemize} \item[(i)] Volume Gf(r)rθ(lnr)k\text{Volume}\ G^f(r) \asymp r^θ(\ln r)^k \item[(ii)] $\text{Card}\left(G^f(r) \cap \…

2015-02-21abs ↗pdf ↗

We investigate multiarmed bandits with delayed feedback, where the delays need neither be identical nor bounded. We first prove that "delayed" Exp3 achieves the O((KT+D)lnK)O(\sqrt{(KT + D)\ln K} ) regret bound conjectured by Cesa-Bianchi et al. [2019] in the case of variable, but bounded delays. Here, KK is the number of actio…

2019-06-03abs ↗pdf ↗

The paper estimates common mean of entangled Gaussians with bounded variances.

problem Estimating common mean of entangled Gaussians with bounded variances.
method Iteratively averaging truncated samples.
result Achieves error $O \left(\frac{\sqrt{n\ln n}}{m} ight)$ with high probability when m=Ω(nlnn)m=Ω(\sqrt{n\ln n}).

For an entire mapping f:CCf:\mathbb C\mapsto\mathbb C and a triple (p,α,r)(0,)×(,)×(0,](p,α, r)\in (0,\infty)\times(-\infty,\infty)\times(0,\infty], the Gaussian integral means of ff (with respect to the area measure dAdA) is defined by $$ {\mathsf M}_{p,α}(f,r)=\Big({\int_{|z|<r}e^{-α|z|^2}dA(z)}\Big)^{-1}{\int_{|z|<r}|f(z)|^p{e^{-α|z|^…

2013-01-02abs ↗pdf ↗

Paper addresses privacy in combinatorial semi-bandits with improved bounds.

problem Privacy-preserving learning in combinatorial semi-bandits with additional dimension dependence.
method Proposes novel algorithms and proves optimal regret bounds for LDP and DP settings.
result Achieves nearly optimal regret bounds for LDP and DP settings, matching non-private rates.

New exponential decay estimate for Hermitian Yang-Mills metrics near branch points.

problem Understanding the behavior of Hermitian Yang-Mills metrics near branch points.
method Local radial solutions, global gluing construction, exponential estimate near branch points.
result Exponential decay estimate for local radial solutions near branch points.