data poisoning attack cks on stoch chastic c bandits
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Data Poisoning Attack cks on Stoch chastic c Bandits Fang Liu - PowerPoint PPT Presentation

Data Poisoning Attack cks on Stoch chastic c Bandits Fang Liu and Ness Shroff Outline Background q What are bandits? q Motivations Data poisoning attacks on stochastic bandits q Offline model q Online model q Simulation results


  1. Data Poisoning Attack cks on Stoch chastic c Bandits Fang Liu and Ness Shroff

  2. Outline • Background q What are bandits? q Motivations • Data poisoning attacks on stochastic bandits q Offline model q Online model q Simulation results • Conclusions and discussions

  3. What are bandits? • Repeated game between an agent and an environment action Reinforcement learning environment agent MAB x Stochastic x Online learning reward

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sha1_base64="fK3DLAWfZrBJ4YWuVe+8yrb6qWg=">AB73icbVBNS8NAEN34WetX1aOXxSJ4kJKIoN6qXjxWMLalDWGz3bRLdzdhdyKU0F/hxYOKV/+ON/+N2zYHbX0w8Hhvhpl5USq4Adf9dpaWV1bX1ksb5c2t7Z3dyt7+o0kyTZlPE5HoVkQME1wxHzgI1ko1IzISrBkNbyd+84lpwxP1AKOUBZL0FY85JWCldjvMr0M4hXFYqbo1dwq8SLyCVFGBRlj56vYSmkmgApiTMdzUwhyoFTwcblbmZYSuiQ9FnHUkUkM0E+PXiMj63Sw3GibSnAU/X3RE6kMSMZ2U5JYGDmvYn4n9fJIL4Mcq7SDJis0VxJjAkePI97nHNKIiRJYRqbm/FdEA0oWAzKtsQvPmXF4l/Vruqufn1fpNkUYJHaIjdI8dIHq6A41kI8okugZvaI3RzsvzrvzMWtdcoqZA/QHzucP89iQCw=</latexit> <latexit sha1_base64="fK3DLAWfZrBJ4YWuVe+8yrb6qWg=">AB73icbVBNS8NAEN34WetX1aOXxSJ4kJKIoN6qXjxWMLalDWGz3bRLdzdhdyKU0F/hxYOKV/+ON/+N2zYHbX0w8Hhvhpl5USq4Adf9dpaWV1bX1ksb5c2t7Z3dyt7+o0kyTZlPE5HoVkQME1wxHzgI1ko1IzISrBkNbyd+84lpwxP1AKOUBZL0FY85JWCldjvMr0M4hXFYqbo1dwq8SLyCVFGBRlj56vYSmkmgApiTMdzUwhyoFTwcblbmZYSuiQ9FnHUkUkM0E+PXiMj63Sw3GibSnAU/X3RE6kMSMZ2U5JYGDmvYn4n9fJIL4Mcq7SDJis0VxJjAkePI97nHNKIiRJYRqbm/FdEA0oWAzKtsQvPmXF4l/Vruqufn1fpNkUYJHaIjdI8dIHq6A41kI8okugZvaI3RzsvzrvzMWtdcoqZA/QHzucP89iQCw=</latexit> What are bandits? • Model § At each (discrete) time t, the agent plays action A t from a set of K actions § The agent receives reward , drawn from unknown distribution A t Y A t ,t • Performance measure " T T # § Regret(loss) X X R ( T ) = E max Y i,t − Y A t ,t i ∈ [ K ] t =1 t =1 § Minimize regret = maximize total reward • Regret lower bounds X ! µ ∗ − µ i § Problem-dependent: where is expected reward KL ( µ a , µ ∗ ) log T Ω µ i i § Problem-independent: ⇣ √ ⌘ Ω KT • Popular algorithms § Upper Confidence Bounds (UCB), Thompson Sampling, epsilon-greedy

  5. Motivations • Adversarial learning is well studied in deep learning • How robust are bandits? • Many applications § Clinical trials § Recommendation systems § Ad placement § A/B test § A component of game-playing algorithms (MCTS), e.g. AlphaGo § Resource allocation • If under stealthy attack, hard to detect (due to limited feedback)

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