\tau-rigid modules for algebras with radical square zero

In this paper, we show that for an algebra $\Lambda$ with radical square zero and an indecomposable $\Lambda$-module $M$ such that $\Lambda$ is Gorenstein of finite type or $\tau M$ is $\tau$-rigid, $M$ is $\tau$-rigid if and only if the first two projective terms of a minimal projective resolution of $M$ have no on-zero direct summands in common. We also determined all $\tau$-tilting modules for Nakayama algebras with radical square zero. Moreover, by giving a construction theorem we show that a basic connected radical square zero algebra admitting a unique $\tau$-tilting module is local

A Note on Gorenstein Projective Conjecture II

In this paper, we prove that Gorenstein projective conjecture is left and right symmetric and the co-homology vanishing condition can not be reduced in general. Moreover, the Gorenstein projective conjecture is proved to be true for CM-finite algebras.Comment: 7pages,Section 3 is deleted and We use the notion "Auslander-Reiten Conjecture

Classifying τ\tau-tilting modules over the Auslander algebra of K[x]/(xn)K[x]/(x^n)

We build a bijection between the set \sttilt\Lambda of isomorphism classes of basic support $\tau$-tilting modules over the Auslander algebra $\Lambda$ of $K[x]/(x^n)$ and the symmetric group $\mathfrak{S}_{n+1}$, which is an anti-isomorphism of partially ordered sets with respect to the generation order on \sttilt\Lambda and the left order on $\mathfrak{S}_{n+1}$. This restricts to the bijection between the set \tilt\Lambda of isomorphism classes of basic tilting $\Lambda$-modules and the symmetric group $\mathfrak{S}_n$ due to Br\"{u}stle, Hille, Ringel and R\"{o}hrle. Regarding the preprojective algebra $\Gamma$ of Dynkin type $A_n$ as a factor algebra of $\Lambda$, we show that the tensor functor $-\otimes_{\Lambda}\Gamma$ induces a bijection between \sttilt\Lambda\to\sttilt\Gamma. This recover Mizuno's bijection \mathfrak{S}_{n+1}\to\sttilt\Gamma for type $A_n$

Higher Auslander Algebras Admitting Trivial Maximal Orthogonal Subcategories

For an Artinian $(n-1)$-Auslander algebra $\Lambda$ with global dimension $n(\geq 2)$, we show that if $\Lambda$ admits a trivial maximal $(n-1)$-orthogonal subcategory of $\mod\Lambda$, then $\Lambda$ is a Nakayama algebra and the projective or injective dimension of any indecomposable module in $\mod\Lambda$ is at most $n-1$. As a result, for an Artinian Auslander algebra with global dimension 2, if $\Lambda$ admits a trivial maximal 1-orthogonal subcategory of $\mod\Lambda$, then $\Lambda$ is a tilted algebra of finite representation type. Further, for a finite-dimensional algebra $\Lambda$ over an algebraically closed field $K$, we show that $\Lambda$ is a basic and connected $(n-1)$-Auslander algebra $\Lambda$ with global dimension $n(\geq 2)$ admitting a trivial maximal $(n-1)$-orthogonal subcategory of $\mod\Lambda$ if and only if $\Lambda$ is given by the quiver: \xymatrix{1 & \ar[l]_{\beta_{1}} 2 & \ar[l]_{\beta_{2}} 3 & \ar[l]_{\beta_{3}} ... & \ar[l]_{\beta_{n}} n+1} modulo the ideal generated by $\{\beta_{i}\beta_{i+1}| 1\leq i\leq n-1 \}$. As a consequence, we get that a finite-dimensional algebra over an algebraically closed field $K$ is an $(n-1)$-Auslander algebra with global dimension $n(\geq 2)$ admitting a trivial maximal $(n-1)$-orthogonal subcategory if and only if it is a finite direct product of $K$ and $\Lambda$ as above. Moreover, we give some necessary condition for an Artinian Auslander algebra admitting a non-trivial maximal 1-orthogonal subcategory.Comment: 25 pages. This version is a combination of the orginal version of this paper with "From Auslander Algebras to Tilted Algebras" (arXiv:0903.0760). The latter paper has been withdraw

Tilting modules over Auslander-Gorenstein Algebras

For a finite dimensional algebra $\Lambda$ and a non-negative integer $n$, we characterize when the set \tilt_n\Lambda of additive equivalence classes of tilting modules with projective dimension at most $n$ has a minimal (or equivalently, minimum) element. This generalize results of Happel-Unger. Moreover, for an $n$-Gorenstein algebra $\Lambda$ with $n\geq 1$, we construct a minimal element in \tilt_{n}\Lambda. As a result, we give equivalent conditions for a $k$-Gorenstein algebra to be Iwanaga-Gorenstein. Moreover, for an $1$-Gorenstein algebra $\Lambda$ and its factor algebra $\Gamma=\Lambda/(e)$, we show that there is a bijection between \tilt_1\Lambda and the set \sttilt\Gamma of isomorphism classes of basic support $\tau$-tilting $\Gamma$-modules, where $e$ is an idempotent such that $e\Lambda$ is the additive generator of projective-injective $\Lambda$-modules

Three results for tau-rigid modules

$\tau$-rigid modules are essential in the $\tau$-tilting theory introduced by Adachi, Iyama and Reiten. In this paper, we give equivalent conditions for Iwanaga-Gorenstein algebras with self-injective dimension at most one in terms of $\tau$-rigid modules. We show that every indecomposable module over iterated tilted algebras of Dynkin type is $\tau$-rigid. Finally, we give a $\tau$-tilting theorem on homological dimension which is an analog to that of classical tilting modules.Comment: 10 pages, to appear in Rocky Mountain Journal of Mathematic

Online Data Poisoning Attack

We study data poisoning attacks in the online setting where training items arrive sequentially, and the attacker may perturb the current item to manipulate online learning. Importantly, the attacker has no knowledge of future training items nor the data generating distribution. We formulate online data poisoning attack as a stochastic optimal control problem, and solve it with model predictive control and deep reinforcement learning. We also upper bound the suboptimality suffered by the attacker for not knowing the data generating distribution. Experiments validate our control approach in generating near-optimal attacks on both supervised and unsupervised learning tasks

Adaptive Double-Exploration Tradeoff for Outlier Detection

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. Distinct from the traditional TBP, the threshold is defined as a function of the rewards of all the arms, which is motivated by the criterion for identifying outliers. The learner needs to explore the rewards of the arms as well as the threshold. We refer to this problem as "double exploration for outlier detection". We construct an adaptively updated confidence interval for the threshold, based on the estimated value of the threshold in the previous rounds. Furthermore, by automatically trading off exploring the individual arms and exploring the outlier threshold, we provide an efficient algorithm in terms of the sample complexity. Experimental results on both synthetic datasets and real-world datasets demonstrate the efficiency of our algorithm

An Optimal Control Approach to Sequential Machine Teaching

Given a sequential learning algorithm and a target model, sequential machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate sequential machine teaching as a time-optimal control problem. This allows us to solve sequential teaching by leveraging key theoretical and computational tools developed over the past 60 years in the optimal control community. Specifically, we study the Pontryagin Maximum Principle, which yields a necessary condition for optimality of a training sequence. We present analytic, structural, and numerical implications of this approach on a case study with a least-squares loss function and gradient descent learner. We compute optimal training sequences for this problem, and although the sequences seem circuitous, we find that they can vastly outperform the best available heuristics for generating training sequences

Automatic Ensemble Learning for Online Influence Maximization

We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) problem. We assume that the topology of the social network is prescribed while the influence probabilities among edges are unknown. In order to learn the influence probabilities and simultaneously maximize the influence spread, we consider the tradeoff between exploiting the current estimation of the influence probabilities to ensure certain influence spread and exploring more nodes to learn better about the influence probabilities. The exploitation-exploration trade-off is the core issue in the multi-armed bandit (MAB) problem. If we regard the influence spread as the reward, then the IM problem could be reduced to the combinatorial multi-armed bandits. At each round, the learner selects a limited number of seed nodes in the social network, then the influence spreads over the network according to the real influence probabilities. The learner could observe the activation status of the edge if and only if its start node is influenced, which is referred to as the edge-level semi-bandit feedback. Two classical bandit algorithms including Thompson Sampling and Epsilon Greedy are used to solve this combinatorial problem. To ensure the robustness of these two algorithms, we use an automatic ensemble learning strategy, which combines the exploration strategy with exploitation strategy. The ensemble algorithm is self-adaptive regarding that the probability of each algorithm could be adjusted based on the historical performance of the algorithm. Experimental evaluation illustrates the effectiveness of the automatically adjusted hybridization of exploration algorithm with exploitation algorithm