Yishay Mansour
Prof. Yishay Mansour got his PhD from MIT in 1990, following it he was
a postdoctoral fellow in Harvard and a Research Staff Member in IBM T.
J. Watson Research Center. Since 1992 he is at Tel-Aviv University,
where he is currently a Professor of Computer Science and has serves
as the first head of the Blavatnik School of Computer Science during
2000-2002. He was the founder and first director of the Israeli Center of Research
Excellence in Algorithms.
Prof. Mansour has published over 100 journal papers and over 200
proceeding papers in various areas of computer science with special
emphasis on machine learning, algorithmic game theory, communication
networks, and theoretical computer science and has supervised over a
dozen graduate students in those areas.
Prof. Mansour was named as an ACM fellow 2014, and he is currently an
associate editor in a number of distinguished journals and has been on
numerous conference program committees. He was both the program chair
of COLT (1998) and the STOC (2016) and served twice on the COLT
steering committee and is a member of the ALT steering committee.
Research Areas
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Simple, sufficient explanations
furnished by short decision lists
can be useful for guiding stakeholder actions.
Unfortunately, this transparency can come at the expense
of the higher accuracy enjoyed by black box methods,
like deep nets.
To date, practitioners typically either (i) insist on the simpler model, forsaking accuracy; or (ii) insist on maximizing accuracy, settling for post-hoc explanations of dubious faithfulness.
In this paper, we propose a hybrid \emph{partially interpretable model} that represents a compromise between the two extremes.
In our setup, each input is first processed by a decision list that can either execute a decision or abstain,
handing off authority to the opaque model.
The key to optimizing the decision list is to optimally
trade off the accuracy of the composite system
against coverage (the fraction of the population
that receives explanations).
We contribute a new principled algorithm for constructing partially interpretable decision lists,
providing theoretical guarantees
addressing both interpretability and accuracy.
As an instance of our result, we prove
that when the optimal decision list has length $k$, coverage $c$, and $b$ mistakes,
our algorithm will generate a decision list
that has length no greater than $4k$,
coverage at least $c/2$,
and makes at most $4b$ mistakes.
Finally, we empirically validate the effectiveness of the new model.
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Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems, and recent work has extended this to more complex scenarios such as Markov Decision Processes (MDPs). In this paper, we further explore this line of research by investigating how reward shaping under budget constraints can improve the principal's utility. We study a two-player Stackelberg game where the principal and the agent have different reward functions, and the agent chooses an MDP policy for both players. The principal offers an additional reward to the agent, and the agent picks their policy selfishly to maximize their reward, which is the sum of the original and the offered reward. Our results establish the NP-hardness of the problem and offer polynomial approximation algorithms for two classes of instances: Stochastic trees and deterministic decision processes with a finite horizon.
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In this work we revisit an interactive variant of joint differential privacy, recently introduced by Naor et al. [2023], and generalize it towards handling online processes in which existing privacy definitions seem too restrictive. We study basic properties of this definition and demonstrate that it satisfies (suitable variants) of group privacy, composition, and post processing.
In order to demonstrate the advantages of this privacy definition compared to traditional forms of differential privacy, we consider the basic setting of online classification. We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound. This demonstrates a stark difference with traditional forms of differential privacy, such as the one studied by Golowich and Livni [2021], where only a double exponential overhead in the mistake bound is known (via an information theoretic upper bound).
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We present the OMG-CMDP! algorithm for regret minimization in adversarial Contextual MDPs. The algorithm operates under the minimal assumptions of realizable function class and access to online least squares and log loss regression oracles. Our algorithm is efficient (assuming efficient online regression oracles), simple and robust to approximation errors. It enjoys an
$\widetilde{O}(H^{2.5} \sqrt{ T|S||A| (
} \linebreak[1]
\overline{ \mathcal{R}(\mathcal{O})
+ H \log(\delta^{-1}) )})$ regret guarantee,
with $T$ being the number of episodes, $S$ the state space, $A$ the action space, $H$ the horizon and $\mathcal{R}(\mathcal{O}) = \mathcal{R}(\mathcal{O}_{\mathrm{sq}}^\mathcal{F}) + \mathcal{R}(\mathcal{O}_{\mathrm{log}}^\mathcal{P})$
is the sum of the regression oracles' regret, used to approximate the context-dependent rewards and dynamics, respectively. To the best of our knowledge, our algorithm is the first efficient and rate optimal regret minimization algorithm for adversarial CMDPs which operates under the minimal and standard assumption of online function approximation. Our technique relies on standard convex optimization algorithms, and we show that it is robust to approximation errors.
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There is often a great degree of freedom in the reward design when formulating a task as a reinforcement learning (RL) problem. The choice of reward function has significant impact on the learned policy and how fast the algorithm converges to it. Typically several iterations of specifying and learning with the reward function are necessary to find one that leads to sample-efficient learning of desired behavior. In this work, we instead propose to directly pass multiple alternate reward formulations of the task to the RL agent. We
show that natural extensions of action-elimination algorithms to multiple rewards achieve more favorable instance-dependent regret bounds than their single-reward counterparts, both in multi-armed bandits and in tabular Markov decision processes. Specifically our bounds scale for each state-action pair with the inverse of the most favorable gap among all reward functions. This suggests that learning with multiple rewards can indeed be more sample-efficient, as long as the rewards agree on an optimal policy. We further prove that when rewards do not agree on the optimal policy, multi-reward action elimination in multi-armed bandits still learns a policy that is good across all reward functions.
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We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle.
We analyze three different settings: where the dynamics is known, where the dynamics is unknown but independent of the context and the most challenging setting where the dynamics is unknown and context-dependent. For the latter, our algorithm obtains
$
\tilde{O}\left( {1}/{p_{min}}H|S|^{3/2}\sqrt{|A|T\log(\max\{|\F|,|\Fp|\}/\delta)}
\right)
$
regret bound, with probability $1-\delta$, where $\Fp$ and $\F$ are finite and realizable regressors classes used to approximate the dynamics and rewards respectively, $p_{min}$ is the minimum reachability parameter, $S$ is the set of states, $A$ the set of actions, $H$ the horizon, and $T$ the number of episodes.
To our knowledge, our approach is the first optimistic approach applied to Contextual MDPs with general function approximation (i.e., without additional knowledge regarding the function class, such as it being linear and etc.).
In addition, we present a lower bound of $\Omega(\sqrt{T H |S| |A| \ln(|\F|)/\ln(|S||A|)})$, which holds even in the case of known dynamics.
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We study reinforcement learning with linear function approximation and adversarially changing cost functions, a setup that has mostly been considered under simplifying assumptions such as full information feedback or exploratory conditions.
We present a computationally efficient policy optimization algorithm for the challenging general setting of unknown dynamics and bandit feedback, featuring a combination of mirror-descent and least squares policy evaluation in an auxiliary MDP used to compute exploration bonuses.
Our algorithm obtains a $\widetilde O(K^{6/7})$ regret bound, improving significantly over previous state-of-the-art of $\widetilde O (K^{14/15})$ in this setting.
In addition, we present a version of the same algorithm under the assumption a simulator of the environment is available to the learner (but otherwise no exploratory assumptions are made), and prove it obtains state-of-the-art regret of $\widetilde O (K^{2/3})$.
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A landmark negative result of Long and Servedio established a worst-case spectacular failure of a supervised learning trio (loss, algorithm, model) otherwise praised for its high precision machinery. Hundreds of papers followed up on the two suspected culprits: the loss (for being convex) and/or the algorithm (for fitting a classical boosting blueprint). Here, we call to the half-century+ founding theory of losses for class probability estimation (properness), an extension of Long and Servedio's results and a new general boosting algorithm to demonstrate that the real culprit in their specific context was in fact the (linear) model class. We advocate for a more general stanpoint on the problem as we argue that the source of the negative result lies in the dark side of a pervasive -- and otherwise prized -- aspect of ML: \textit{parameterisation}.
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We consider a seller faced with buyers which have the ability to delay their decision, which we call patience. Each buyer's type is composed of value and patience, and it is sampled i.i.d. from a distribution. The seller, using posted prices, would like to maximize her revenue from selling to the buyer. In this paper, we formalize this setting and characterize the resulting Stackelberg equilibrium, where the seller first commits to her strategy, and then the buyers best respond. Following this, we show how to compute both the optimal pure and mixed strategies. We then consider a learning setting, where the seller does not have access to the distribution over buyer's types. Our main results are the following. We derive a sample complexity bound for the learning of an approximate optimal pure strategy, by computing the fat-shattering dimension of this setting. Moreover, we provide a general sample complexity bound for the approximate optimal mixed strategy. We also consider an online setting and derive a vanishing regret bound with respect to both the optimal pure strategy and the optimal mixed strategy.
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Given a policy, we define a {\newprob}%\emph{safe zone}
as a subset of states, such that most of the policy's trajectories are confined to this subset.
The quality of the {\newprob }%safe zone
is parameterized by the number of states and the escape probability, i.e., the probability that a random trajectory will leave the subset. Safe zones are especially interesting when they have a small number of states and low escape probability.
We study the complexity of finding optimal {\textsc{safeZones}},
and show that in general the problem is computationally hard. For this reason we concentrate on computing approximate {\textsc{safeZones}.}
Our main result is a bi-criteria approximation algorithm which gives a factor of almost $2$ approximation for both the escape probability and safe zone size, using a polynomial size sample complexity.
We conclude the paper with an empirical evaluation of our algorithm.
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