Collaborating Authors

Mansour, Yishay

Optimal Rates for Random Order Online Optimization Machine Learning

We study online convex optimization in the random order model, recently proposed by \citet{garber2020online}, where the loss functions may be chosen by an adversary, but are then presented to the online algorithm in a uniformly random order. Focusing on the scenario where the cumulative loss function is (strongly) convex, yet individual loss functions are smooth but might be non-convex, we give algorithms that achieve the optimal bounds and significantly outperform the results of \citet{garber2020online}, completely removing the dimension dependence and improving their scaling with respect to the strong convexity parameter. Our analysis relies on novel connections between algorithmic stability and generalization for sampling without-replacement analogous to those studied in the with-replacement i.i.d.~setting, as well as on a refined average stability analysis of stochastic gradient descent.

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations Artificial Intelligence

Reinforcement Learning (RL) has achieved several remarkable empirical successes in the last decade, which include playing Atari 2600 video games at superhuman levels (Mnih et al., 2015), AlphaGo or AlphaGo Zero surpassing champions in Go (Silver et al., 2018), AlphaStar's victory over top-ranked professional players in StarCraft (Vinyals et al., 2019), or practical self-driving cars. These applications all correspond to the setting of rich observations, where the state space is very large and where observations may be images, text or audio data. In contrast, most provably efficient RL algorithms are still limited to the classical tabular setting where the state space is small (Kearns and Singh, 2002; Brafman and Tennenholtz, 2002; Azar et al., 2017; Dann et al., 2019) and do not scale to the rich observation setting. To derive guarantees for large state spaces, much of the existing work in RL theory relies on a realizability and a low-rank assumption (Krishnamurthy et al., 2016; Jiang et al., 2017; Dann et al., 2018; Du et al., 2019a; Misra et al., 2020; Agarwal et al., 2020b). Different notions of rank have been adopted in the literature, including that of a low-rank transition matrix (Jin et al., 2020a), a low Bellman rank (Jiang et al., 2017), Wittness rank (Sun et al., 2019), Eluder dimension (Osband and Van Roy, 2014), Bellman-Eluder dimension (Jin et al., 2021), or bilinear classes (Du et al., 2021).

Stochastic Shortest Path with Adversarially Changing Costs Machine Learning

Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In this paper we consider adversarial SSPs that also account for adversarial changes in the costs over time, while the dynamics (i.e., transition function) remains unchanged. Formally, an agent interacts with an SSP environment for $K$ episodes, the cost function changes arbitrarily between episodes, and the fixed dynamics are unknown to the agent. We give high probability regret bounds of $\widetilde O (\sqrt{K})$ assuming all costs are strictly positive, and $\widetilde O (K^{3/4})$ for the general case. To the best of our knowledge, we are the first to consider this natural setting of adversarial SSP and obtain sub-linear regret for it.

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity Machine Learning

We present a differentially private learner for halfspaces over a finite grid $G$ in $\mathbb{R}^d$ with sample complexity $\approx d^{2.5}\cdot 2^{\log^*|G|}$, which improves the state-of-the-art result of [Beimel et al., COLT 2019] by a $d^2$ factor. The building block for our learner is a new differentially private algorithm for approximately solving the linear feasibility problem: Given a feasible collection of $m$ linear constraints of the form $Ax\geq b$, the task is to privately identify a solution $x$ that satisfies most of the constraints. Our algorithm is iterative, where each iteration determines the next coordinate of the constructed solution $x$.

A Theory of Multiple-Source Adaptation with Limited Target Labeled Data Machine Learning

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains. We show that a new family of algorithms based on model selection ideas benefits from very favorable guarantees in this scenario and discuss some theoretical obstacles affecting some alternative techniques. We also report the results of several experiments with our algorithms that demonstrate their practical effectiveness.

Adversarial Dueling Bandits Machine Learning

We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only a relative binary `win-loss' feedback for this pair, but here this feedback is generated from an arbitrary preference matrix, possibly chosen adversarially. Our main result is an algorithm whose $T$-round regret compared to the \emph{Borda-winner} from a set of $K$ items is $\tilde{O}(K^{1/3}T^{2/3})$, as well as a matching $\Omega(K^{1/3}T^{2/3})$ lower bound. We also prove a similar high probability regret bound. We further consider a simpler \emph{fixed-gap} adversarial setup, which bridges between two extreme preference feedback models for dueling bandits: stationary preferences and an arbitrary sequence of preferences. For the fixed-gap adversarial setup we give an $\smash{ \tilde{O}((K/\Delta^2)\log{T}) }$ regret algorithm, where $\Delta$ is the gap in Borda scores between the best item and all other items, and show a lower bound of $\Omega(K/\Delta^2)$ indicating that our dependence on the main problem parameters $K$ and $\Delta$ is tight (up to logarithmic factors).

The Sparse Vector Technique, Revisited Machine Learning

We revisit one of the most basic and widely applicable techniques in the literature of differential privacy - the sparse vector technique [Dwork et al., STOC 2009]. Loosely speaking, this technique allows us to privately test whether the value of a given query is close to what we expect it would be (w.r.t. the input database), where we are allowed to test an unbounded number of queries as long as their value is indeed close to what we expected. After the first time in which this is not the case, the process halts. We present a modification to the sparse vector technique that allows for a more fine-tuned privacy analysis. As a result, in some cases we are able to continue with the process of testing queries even after the first time in which the value of the query did not meet our expectations. We demonstrate our technique by applying it to the shifting-heavy-hitters problem: On every time step, each of n users gets a new input, and the task is to privately identify all the current heavy-hitters. That is, on time step i, the goal is to identify all data elements x such that many of the users have x as their current input. We present an algorithm for this problem with improved error guarantees over what can be obtained using existing techniques. Specifically, the error of our algorithm depends on the maximal number of times that a singe user holds a heavy-hitter as input, rather than the total number of times in which a heavy-hitter exists.

Oracle-Efficient Reinforcement Learning in Factored MDPs with Unknown Structure Machine Learning

We consider provably-efficient reinforcement learning (RL) in non-episodic factored Markov decision processes (FMDPs). All previous algorithms for regret minimization in this setting made the strong assumption that the factored structure of the FMDP is known to the learner in advance. In this paper, we provide the first provably-efficient algorithm that has to learn the structure of the FMDP while minimizing its regret. Our algorithm is based on the optimism in face of uncertainty principle, combined with a simple statistical method for structure learning, and can be implemented efficiently given oracle-access to an FMDP planner. It maintains its computational efficiency even though the number of possible structures is exponential.

Beyond Individual and Group Fairness Machine Learning

Learning algorithms trained on large amounts of data are increasingly adopted in applications with significant individual and social consequences such as selecting loan applicants, filtering resumes of job applicants, estimating the likelihood for a defendant to commit future crimes, or deciding where to deploy police officers. Analyzing the risk of bias in these systems is therefore crucial. In fact, that is also critical for seemingly less socially consequential applications such as ads placement, recommendation systems, speech recognition, and many other common applications of machine learning. Such biases can appear due to the way the training data has been collected, due to an improper choice of the loss function optimized, or as a result of some other algorithmic choices.

Reinforcement Learning with Feedback Graphs Artificial Intelligence

We study episodic reinforcement learning in Markov decision processes when the agent receives additional feedback per step in the form of several transition observations. Such additional observations are available in a range of tasks through extended sensors or prior knowledge about the environment (e.g., when certain actions yield similar outcome). We formalize this setting using a feedback graph over state-action pairs and show that model-based algorithms can leverage the additional feedback for more sample-efficient learning. We give a regret bound that, ignoring logarithmic factors and lower-order terms, depends only on the size of the maximum acyclic subgraph of the feedback graph, in contrast with a polynomial dependency on the number of states and actions in the absence of a feedback graph. Finally, we highlight challenges when leveraging a small dominating set of the feedback graph as compared to the bandit setting and propose a new algorithm that can use knowledge of such a dominating set for more sample-efficient learning of a near-optimal policy.